diff --git a/e2e/.gitignore b/e2e/.gitignore new file mode 100644 index 0000000000..c563cae8c4 --- /dev/null +++ b/e2e/.gitignore @@ -0,0 +1,7 @@ +*.db +output_* +passages/ +data/ +run_container.sh +.claude/ +config.sh diff --git a/e2e/CLAUDE.md b/e2e/CLAUDE.md new file mode 100644 index 0000000000..e920f183ad --- /dev/null +++ b/e2e/CLAUDE.md @@ -0,0 +1,313 @@ +# CLAUDE.md + +This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. + +## Overview + +This is a RAG (Retrieval-Augmented Generation) benchmark system for evaluating multi-hop question answering using Wikipedia documents from the FRAMES dataset. The system supports multiple retrieval methods (BM25, vector search), reranking, and multi-shot iterative retrieval with query decomposition. + +## Architecture + +The codebase follows a modular pipeline architecture: + +1. **Document Ingestion** → **Passage Chunking** → **Vector/BM25 Indexing** +2. **Query** → **Retrieval** → **Optional Reranking** → **LLM Answer Generation** → **Evaluation** + +### Core Components + +- **`retrieve/` module**: Defines abstract `RagDB` base class with two implementations: + - `VectorDB`: Dense vector search using FAISS (flat, HNSW, or IVF indexing) + - `BM25DB`: Sparse lexical search using bm25s library + - Both support reranking with ColBERTv2 or similar models + +- **Retrieval Scripts**: + - `single_shot_retrieval.py`: Single-step retrieval and evaluation + - `multi_shot_retrieval.py`: Multi-hop retrieval with LLM-based query decomposition (iterative retrieval with query rewriting) + - `oracle_single_shot.py`: Upper-bound evaluation using ground truth documents + +- **Parameter Management**: `params.py` centralizes all CLI parameters with Optuna optimization metadata + +- **Utilities**: + - `download_docs.py`: Downloads Wikipedia pages from FRAMES dataset URLs + - `read_docs.py`: Extracts text and chunks documents into passages (uses `text_splitter.py`) + - `evaluate.py`: LLM judge-based evaluation of generated answers + - `utils.py`: Common helpers (device config, LLM setup, seeding) + +## Common Development Workflows + +### Initial Setup + +```bash +# Install dependencies (requires Ubuntu-based environment) +./setup.sh + +# Download Wikipedia documents from FRAMES dataset +python3 download_docs.py --output_dir doc_html --format html --processes 30 + +# Build vector database (run once) +bash scripts/run_ingestion.sh +``` + +The setup script will: +1. Extract passages from HTML documents → `passages/doc_html_len2048_ov32_word.json` +2. Build FAISS HNSW index → `vector_html_hnsw_len2048_ov32_word.db` + +### Running Experiments + +**Single-shot retrieval:** +```bash +# Run evaluation on existing database +python3 single_shot_retrieval.py \ + --db vector_html_hnsw_len2048_ov32_word.db \ + --retrieval_method vector \ + --eval 100 \ + --generate-answer + +# Compare BM25 vs Vector +python3 single_shot_retrieval.py --db bm25.db --retrieval_method bm25 --eval +``` + +**Multi-shot retrieval with query decomposition:** +```bash +# Run multi-shot experiment (requires LLM server on port 8123) +bash scripts/run_multi_shot.sh 50 # Evaluate 50 queries +bash scripts/run_multi_shot.sh all # Full evaluation +``` + +**Oracle upper bound (using ground truth docs):** +```bash +python3 oracle_single_shot.py \ + --dataset data/frames_dataset.tsv \ + --wiki-articles-dir wiki_articles \ + --batch-size 16 +``` + +### LLM Server Setup + +The system expects a vLLM-compatible OpenAI API server: + +```bash +# Start vLLM server (example from scripts/start_vllm_server.sh) +python3 -m vllm.entrypoints.openai.api_server \ + --model /model/gpt-oss-20b-mxfp4 \ + --dtype bfloat16 \ + --host 0.0.0.0 \ + --port 8123 \ + --gpu-memory-util=0.95 \ + --enable-prefix-caching \ + --max-model-len=131072 +``` + +Default service URL: `http://127.0.0.1:8123/v1/chat/completions` + +### Evaluation + +Evaluation uses an LLM judge to score answers: + +```bash +# Score results from any experiment +python3 evaluate.py result_single_shot.json +python3 evaluate.py result_multi_shot.json +python3 evaluate.py oracle_checkpoint.pkl # For oracle results +``` + +## Key Parameters (via params.py) + +All parameters are centralized in `params.py` with CLI definitions. Key categories: + +**Retrieval Method:** +- `--retrieval_method {bm25,vector}`: Choose retrieval backend +- `--vector_index_method {flat,hnsw,ivf}`: FAISS index type (default: hnsw) +- `--bm25_k1`, `--bm25_b`, `--bm25_stemmer`: BM25 tuning parameters + +**Retrieval Strategy:** +- `--retrieval_strategy {fixed_k,top_p,relative}`: How many docs to retrieve +- `--top_k_retriever N`: Number of docs to retrieve (default: 10) +- `--top_k_reranking N`: Number of docs after reranking (default: 10) + +**Device & Performance:** +- `--device {auto,xpu,cuda,hpu,cpu}`: Hardware accelerator +- `--num_embedding_devices N`: Parallel embedding generation across devices +- `--benchmark`: Enable performance monitoring + +**Multi-shot specific (multi_shot_retrieval.py):** +- `--max-iterations N`: Max retrieval rounds (default: 5) +- `--max-sub-queries N`: Sub-queries per iteration (default: 3) + +**Oracle specific (oracle_single_shot.py):** +- `--batch-size N`: Batch requests to LLM (default: 1) +- `--timeout N`: Request timeout in seconds (default: 2400) +- `--enable-thinking`: Use chain-of-thought reasoning + +## Hardware Support + +The system supports multiple accelerators via `--device`: +- **XPU** (Intel GPUs): Primary development target +- **CUDA** (NVIDIA GPUs) +- **HPU** (Habana Gaudi): Embeddings/reranking fall back to CPU due to compatibility +- **CPU**: Fallback option + +Device selection is abstracted in `utils.py:get_device_config()` and `ragdb.py:_determine_device()`. + +## Database Persistence + +- Vector databases: `.db` file (FAISS index) + `_data/` directory (docstore, metadata) +- BM25 databases: Pickled retriever object in `.db` file +- Embeddings cache: `.emb.pkl` files (use `--load-embeddings` to reuse) +- Checkpoints: `oracle_checkpoint.pkl` for oracle runs (resumable via pandas DataFrame) + +## Multi-shot Retrieval Logic + +The multi-shot system (multi_shot_retrieval.py) implements iterative retrieval: +1. LLM evaluates retrieved docs and decides if sufficient to answer +2. If insufficient, generates up to k focused sub-queries +3. Each sub-query retrieves additional docs +4. Process repeats for max N iterations +5. Final docs are reranked and passed to answer generator + +The query rewriter prompt includes failure analysis to escalate search strategies when stuck (e.g., switching from specific queries to broader entity searches). + +## Evaluation Methodology + +- **Retrieval Accuracy**: Checks if correct Wikipedia URLs are in top-K results +- **Answer Quality**: LLM judge scores generated answers against ground truth +- **Difficulty Filtering**: `--difficulty N` filters queries by number of required source documents + +Results are saved to JSON files with schema: +```json +{ + "query": "...", + "retrieved_urls": [...], + "correct_urls": [...], + "llm_answer": "...", + "ground_truth_answer": "..." +} +``` + +## Testing & Debugging + +- Use `--eval N` to test on first N queries (faster iteration) +- Use `--no-rerank` to compare retrieval methods fairly +- Use `--no-save` to skip writing database during optimization +- Use `--benchmark` to track component performance +- Check logs: Scripts redirect output to `log_*.txt` files + +## Important Notes + +- **LLM timeout**: Oracle and multi-shot runs may need `--timeout` adjustment for reasoning models +- **Checkpointing**: Oracle script saves progress after each batch and can resume from checkpoint +- **Determinism**: Use `--seed` for reproducible results (affects sampling, not LLM generation) + +--- + +## Experiments and Results + +### Accuracy Benchmarks + +| Type | Queries | Precision@N | Recall@N | F1@N | LLM Judge Accuracy | +|---|---|---|---|---|---| +| Oracle | 824 | 100% | 100% | 100% | 68% | +| Single-shot retrieval | 827 | 39% | 70% | 42% | 20% | +| Multi-shot baseline | 50 | 12% | 36% | 16% | 20% | +| Multi-shot + fixes below | 50 | 69% | 64% | 61% | 42% | +| Multi-shot + fixes below | 400 | 73% | 67% | 66% | 34% | +| **Multi-shot + fixes below** | **824** | **72%** | **67%** | **66%** | **36%** | + +The retrieval recall is the primary bottleneck: theoretical max accuracy ≈ Recall × Oracle_accuracy. + +--- + +### Fix 1: Split Monolithic Query Rewriter (HIGH IMPACT) + +**Root Cause:** The original `query_rewriter()` was a single LLM call with 3 simultaneous tasks: +1. Evaluate relevance of new documents +2. Decide if accumulated docs are sufficient to answer +3. Generate new search queries + +This cognitive overload caused the LLM to mark **all documents as irrelevant** (relevance: [0,0,0,...]), leading to 0 kept docs and 60% "Unknown" answers. + +**Fix:** Split into two focused LLM calls: +- `evaluate_document_relevance()` — binary relevance classification only (temp=0.0, short prompt) +- `generate_search_queries()` — query generation or final answer (temp=0.1, full context) + +**Additional fixes bundled with Fix 1:** +- Added best-effort final answer after max iterations (instead of always returning "Unknown") +- Added "return Unknown if insufficient" guard in prompt to reduce hallucination +- Added fallback to original query when no sub-queries are generated +- Guarded `sufficient=True` to require non-empty `kept_docs` +- Added `reasoning_content` fallback + `max_tokens=10240` for thinking-model compatibility +- Fixed `UnboundLocalError` from `import re` inside `try` blocks (4 locations) + +**Result:** Accuracy 20% → 30%, Recall 36.8% → 51.8% (n=50) + +--- + +### Fix 2: Context Length Reduction (REVERTED — made things worse) + +**Motivation:** Fix 1 still produced empty LLM responses due to long prompts when many docs accumulated (11+ docs × 1200 chars ≈ 20KB+ prompts hitting token limits). + +**Change:** Limit kept-doc context shown to LLM — query generation: 10 most recent docs; relevance check: 5 most recent docs. + +**Result:** Fewer empty responses, but accuracy dropped 30% → 28%, Recall 51.8% → 46.4%. + +**Why it failed:** Multi-hop reasoning needs to connect facts across all retrieved documents, not just the most recent ones. Truncating context broke cross-document reasoning chains. + +**Decision:** Reverted Fix 2. The right fix is instead: increase `max_tokens` for the judge/LLM calls so they don't hit length limits. + +--- + +### Chunking Strategy Experiments + +**Baseline:** 2048-char chunks, 32-char overlap (1.5% overlap) — too large, dilutes embeddings. + +**Hypothesis:** Smaller chunks produce more focused embeddings → better retrieval precision for multi-hop facts. + +#### Results Across Chunk Sizes (multi-shot, n=50) + +| Chunk Size | Overlap | Recall@N | Precision@N | LLM Accuracy | +|---|---|---|---|---| +| 2048 chars | 32 (1.5%) | 51.8% | 62.5% | 30% | +| 512 chars | 100 (20%) | — | — | Tested, no improvement | +| **768 chars** | **32 (4%)** | **67.7%** | **73.0%** | **34–37%** | + +**Winner: 768-char chunks with 32-char overlap** — significant retrieval improvement over 2048. + +**Why 768 works better than 512:** +- 512-char chunks split related facts across too many chunks; retrieval becomes noisy +- 768-char chunks fit 2–3 complete sentences; good balance of focus vs. context +- More manageable passage count than 512 + +**Overlap finding:** The 1.5% overlap (32/2048) in the baseline was too low. With 768-char chunks, even 32-char overlap (4%) provides measurably better boundary coverage than 32/2048 did. + +**Strategies considered but not implemented:** +- **Hierarchical chunking** (512 retrieval / 2048 context): Promising but complex; worth trying if further gains needed +- **Semantic sentence grouping**: More implementation complexity for marginal benefit over fixed-length with word boundary +- **Token-based chunking**: Aligns better with embedding model limits (e5-base-v2 = 512 tokens); worth trying + +**Key insight for future experiments:** The chunk size primarily affects retrieval recall. Every ~10% recall improvement translates to ~7% accuracy gain (based on Recall × Oracle_accuracy formula). + +--- + +### Iteration Distribution (full 824-query run, max_iterations=5) + +| Iterations Used | Questions | % | +|---|---|---| +| 2 | 297 | 36.0% | +| 3 | 113 | 13.7% | +| 4 | 37 | 4.5% | +| 5 | 377 | 45.8% | + +45.8% of queries hit the max-iterations limit — suggesting accuracy gains are available by increasing `--max-iterations` to 7 or 10. + +--- + +### Future Experiment Candidates + +In priority order based on findings above: + +1. **Increase `--max-iterations`** (7 or 10) — 45.8% of queries are cut off at 5 iterations +2. **Hierarchical chunking** (retrieve 512-char children, answer with 2048-char parents) +3. **Token-based chunking** at 256 tokens to align with e5-base-v2 limits +4. **Hybrid BM25 + vector retrieval** — lexical search catches exact name matches that dense search misses +5. **Larger top_k_retriever** (15 → 25) given high iteration cutoff rate +6. **Better judge model** — current judge (same gpt-oss-20b) hits token limits on complex questions; a stronger judge would give more accurate accuracy measurements diff --git a/e2e/README.md b/e2e/README.md new file mode 100644 index 0000000000..d82699a038 --- /dev/null +++ b/e2e/README.md @@ -0,0 +1,409 @@ +# E2E: RAG benchmark + +End-to-end retrieval-augmented generation benchmark for multi-hop QA on the +[FRAMES](https://huggingface.co/datasets/google/frames-benchmark) Wikipedia +dataset. Supports BM25 and dense vector retrieval, optional ColBERTv2 reranking, +and iterative multi-shot retrieval with LLM-driven query decomposition. + +This is a WIP proposal and will undergo changes. + +--- + +## Benchmark flow + +We start with a corpus of documents and a set of user queries. + +1. **Corpus preparation** (one-time): download Wikipedia pages → chunk into + passages → build a vector DB. +2. **Inference** (per query): retrieve → rerank → generate answer. +3. **Evaluation**: LLM judge scores the answer against ground truth. + +For multi-hop questions, step 2 iterates: the LLM evaluates retrieved docs, +decides if they're sufficient, and otherwise generates fresh sub-queries to +search again. See [Architecture: multi-shot retrieval](#architecture-multi-shot-retrieval). + +--- + +## Quick start + +For a system with the vector DB already built (and `data/frames_dataset.tsv` +present): + +```bash +cp config.template.sh config.sh +$EDITOR config.sh # set device, paths, NUMA, model + +export OPENROUTER_API_KEY="sk-or-v1-..." # only needed for LLM judge + +bash scripts/run_multi_shot.sh 50 10 # 50 queries, 10 parallel workers +``` + +Output lands in `output_multi_shot_n50_w10_/` with: +- `result_multi_shot_n50.json` — retrieval + answer per query +- `score_multi_shot_n50.txt` — LLM judge accuracy summary +- `run.log` — full stdout + +--- + +## Setup from scratch + +### Environment + +Recommended: a Ubuntu PyTorch sandbox via enroot or Docker. + +- [Ubuntu base image](https://hub.docker.com/_/ubuntu) (works, not optimal) +- [NVIDIA PyTorch image](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch/tags) (CUDA) +- [AMD ROCm/PyTorch image](https://hub.docker.com/r/rocm/pytorch/tags) (ROCm) +- Intel XPU: build PyTorch from Intel's wheels manually inside the sandbox. + +Enroot example: + +```bash +mkdir -p containers/ +enroot import -o containers/my_image.sqsh dockerd://pytorch/pytorch:2.8.0-cuda12.9-cudnn9-runtime +enroot create --name my_sandbox containers/my_image.sqsh +enroot start --root --rw \ + --mount /actual/path:/mounted/path \ + --mount $(pwd):/work my_sandbox + +cd /work && ./setup.sh # pip install + apt deps +``` + +### Corpus download + +Pulls all Wikipedia URLs referenced in the FRAMES dataset and saves them as +PDFs or HTML, plus a `url_mapping.json` linking the original URL to each file +(used later for retrieval grading). + +```bash +python3 download_docs.py --output_dir doc_html --format html --processes 30 +``` + +| Option | Default | Notes | +|---|---|---| +| `--tsv_path` | (downloads FRAMES) | Local TSV instead | +| `--max_urls` | all | Cap for testing | +| `--output_dir` | `doc_pdf` / `doc_html` | Where to save documents | +| `--format` | `pdf` | `pdf` or `html` | +| `--processes` | 10 | Parallelism | + +HTML is the default in our scripts since it scores ~1% better than PDF in +extraction quality. Use `download_docs.py --help` for the full list. + +### Passage chunking (handled by `run_ingestion.sh`) + +Embedding models (especially the ColBERTv2 reranker) cap input at ~512 tokens, +so we split each document into overlapping fixed-length passages. Default: +768-char passages with 32-char overlap and word-boundary splitting. + +The chunker output is a JSON list: + +```json +{ + "index": 0, + "base_filename": "name_of_file", + "original_url": "https://...", + "passage": "Long passage from (part of) document" +} +``` + +`run_ingestion.sh` invokes `read_docs.py` for chunking and then +`single_shot_retrieval.py --ingest` to build the vector DB. To change chunk +size or paths, edit `INGESTION_*` variables in `config.sh`. + +### Vector DB build + +```bash +bash scripts/run_ingestion.sh +``` + +Outputs: +- `${INGESTION_PASSAGES_JSON}` — passage JSON +- `${INGESTION_DB}.db` — FAISS index file +- `${INGESTION_DB}_data/` — docstore + metadata + +--- + +## Configuration (`config.sh`) + +System-specific knobs live in a shell-sourced `config.sh` at the repo root. +Copy `config.template.sh` and edit. The file is gitignored. + +```bash +cp config.template.sh config.sh +$EDITOR config.sh +``` + +### Variable namespacing + +| Prefix | Used by | Examples | +|---|---|---| +| `INGESTION_*` | `run_ingestion.sh` | chunk size, embedding device for build, paths | +| `INFERENCE_*` | `run_multi_shot.sh`, `run_single_shot.sh` | device, retriever, top-k, LLM endpoints, judge | +| `INFERENCE_ORACLE_*` | `run_oracle.sh` | batch size, timeout, dataset, wiki dir | +| `CPU_*` | Python (interpreted by `utils.apply_cpu_threading_env`) | NUMA node, OMP thread count | + +### Resolution order + +For each variable: + +1. Already-exported env var (one-off override). E.g. `INFERENCE_DEVICE=cpu bash scripts/run_multi_shot.sh 50`. +2. Value set in `config.sh`. +3. Built-in default in the script. + +So setting a value in `config.sh` is per-system; setting it on the command line +is per-run. + +### Pre-built reference configs + +- `config.template.sh` — annotated template, copy this. +- `config.AMD.CPU.sh` — 2×96C Turin, embedding+reranker on CPU. +- `config.AMD.GPU.sh` — 2×96C Turin + 8 AMD GPUs, embedding+reranker on GPU. + +For the full per-knob reasoning see `claude/design/config_layout.md`. + +--- + +## Pipelines + +All entry-point scripts source `config.sh` and pass appropriate flags to the +underlying Python. Direct Python invocation is still supported for ad-hoc work. + +| Script | Purpose | Underlying script | +|---|---|---| +| `scripts/run_ingestion.sh` | Build vector DB from a doc directory | `read_docs.py` + `single_shot_retrieval.py --ingest` | +| `scripts/run_single_shot.sh [N]` | Single-shot retrieval + judge | `single_shot_retrieval.py` + `evaluate.py` | +| `scripts/run_multi_shot.sh [N] [WORKERS]` | Iterative multi-shot retrieval + judge | `multi_shot_retrieval.py` + `evaluate.py` | +| `scripts/run_oracle.sh [N]` | Upper-bound: ground-truth docs → LLM | `oracle_single_shot.py` + `evaluate.py` | +| `scripts/write_db_manifest.sh` | Generate cross-system DB fingerprint | `db_manifest.py write` | +| `scripts/verify_db_manifest.sh MANIFEST` | Verify local DB matches a reference manifest | `db_manifest.py verify` | + +Positional args: +- `N` = number of queries (`all` = full 824-query dataset). +- `WORKERS` = parallel query threads (default 1). + +Both override the config defaults (`INFERENCE_N_QUERIES`, `INFERENCE_NUM_WORKERS`). + +### Direct Python invocation (ad-hoc) + +```bash +# Single query, no eval +python3 single_shot_retrieval.py \ + --db vector_html_hnsw_len768_ov32_word.db \ + --retrieval_method vector \ + --query "Who won the French Open Mens Singles tournament the year that New York City FC won their first MLS Cup title?" + +# BM25 instead of vector +python3 single_shot_retrieval.py --db bm25.db --retrieval_method bm25 --eval 50 +``` + +Useful flags (for both single-shot and multi-shot): + +| Flag | Notes | +|---|---| +| `--device {auto,cuda,rocm,xpu,hpu,cpu}` | Default device for embedding + reranker | +| `--embedding-device <...>` | Override just the embedder. Defaults to `--device`. | +| `--reranker-device <...>` | Override just the reranker. | +| `--retrieval_method {bm25,vector}` | Backend | +| `--vector_index_method {flat,hnsw,ivf}` | Vector index type (default: hnsw) | +| `--eval [N]` | Run on N queries from `--dataset` | +| `--top_k_retriever N` | Top-K from retriever | +| `--top_k_reranking N` | Top-K after reranker | +| `--no-rerank` | Skip reranker entirely | +| `--benchmark` | Performance monitoring | + +--- + +## Cross-vendor support + +This codebase originally targeted Intel CPU + Intel XPU. After the +cross-vendor refactor it also runs on: + +- AMD CPU + AMD GPU (ROCm) +- AMD CPU + NVIDIA GPU +- Intel CPU + NVIDIA GPU + +Key surfaces: + +- **Device detection**: `utils.detect_device()` returns the best available. + PyTorch ROCm exposes AMD GPUs through `torch.cuda.*`, so AMD GPU device + strings are `"cuda:N"`. `--device rocm` is a cosmetic alias for `cuda` with + ROCm-specific logging. +- **Per-model placement**: `--embedding-device` and `--reranker-device` let + embedding and reranker live on different devices. +- **GPU index allocation**: `DeviceAllocator` picks empty GPUs (≥95% free VRAM + via `mem_get_info`) and prevents within-process collisions. Override with + `INFERENCE_EMBEDDING_GPU_DEVICES` / `INFERENCE_RERANKER_GPU_DEVICES`. +- **CPU NUMA pinning** (when a model is on CPU): each worker process pins + itself to a NUMA node — `os.sched_setaffinity` for CPU, `set_mempolicy` for + memory. Configure via `INFERENCE_RERANKER_NUMA_NODE`, + `INFERENCE_EMBEDDING_NUMA_NODES`, `CPU_NUMA_NODE`. +- **Per-process reranker**: the reranker model loads in its own + `multiprocessing.Process` so it has independent `OMP_NUM_THREADS` and NUMA + placement, isolated from the main process and the embedder. + +The full reasoning is in `claude/design/cross_vendor_plan.md`. + +### Cross-system DB sanity check + +To confirm a vector DB built on system A matches one built independently on +system B (same passages, same model, same parameters): + +```bash +# System A: +bash scripts/write_db_manifest.sh manifest.json.gz +# Ship manifest.json.gz to system B. + +# System B (after building its own DB): +bash scripts/verify_db_manifest.sh manifest.json.gz +# exits 0 on match; prints summary diff on mismatch. +``` + +The manifest contains a corpus sha256, sample embeddings at deterministic +indices, and probe-query top-K results. Tolerances: +- Corpus + counts: exact match. +- Sample embeddings: cosine ≥ 0.9999 (configurable via `--cosine-threshold`). +- Probe top-K: exact rank match for top-3 (configurable via `--top-k-depth`). + +--- + +## Vector DB & indexing + +### Retrieval methods + +- **BM25**: sparse lexical retrieval (traditional keyword search). Uses [bm25s](https://github.com/xhluca/bm25s). +- **Vector**: dense semantic retrieval via [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) embeddings + FAISS. + +### Vector index types + +| Type | Time | Best for | +|---|---|---| +| `flat` (L2) | O(n) | <10K passages | +| `hnsw` | O(log n) | balanced, default | +| `ivf` | O(√n) | >1M passages, configurable `--ivf_nprobe` | + +--- + +## Architecture: multi-shot retrieval + +For multi-hop questions like: + +> Who won the French Open Mens Singles tournament the year that New York City +> FC won their first MLS Cup title? + +A single retrieval rarely surfaces both facts. Multi-shot iterates: + +1. User query → query decomposer (LLM) generates k focused sub-queries. +2. For each sub-query: embed → vector search → retrieve top docs. +3. LLM evaluates relevance of new docs. +4. LLM checks if accumulated docs are sufficient. +5. If insufficient, generate new sub-queries and repeat (up to N iterations). +6. Final docs are reranked, then passed to answer generator. + +Three LLM components, separable by URL + model: + +- **Grader** (`INFERENCE_GRADER_URL`): binary relevance per doc. Cheap; small model. +- **Sufficiency checker** (`INFERENCE_SUFFICIENCY_URL`): "is this enough to answer?" Bigger model. +- **Query generator** (`INFERENCE_QUERY_URL`): generates new sub-queries when insufficient. + +Each defaults to `INFERENCE_LLM_URL` if not set explicitly. Lets you put the +small grader on a dedicated vLLM and the larger sufficiency/query model on a +beefier endpoint. + +The judge model used by `evaluate.py` is independent +(`INFERENCE_JUDGE_URL` / `INFERENCE_JUDGE_MODEL`). + +### Query rewriter prompt (sketch) + +``` +You are an expert at generating search queries to help answer complex questions +using a collection of Wikipedia articles. + +Given: +- The user's original question. +- Relevant facts or documents already gathered so far (if any). + +Your task: +Generate [k] concise, focused search queries that could be used to find specific +information from Wikipedia to help answer the question. +- Make each query target a different aspect of the problem or missing information. +- Avoid duplicating information already in the context. +- Do not reference source filenames, document titles, or include any special characters. +- Think step by step before writing each query. +- List the missing pieces of information, then write k queries that could best retrieve them. + +[User Question:] +{user_question} + +[Known Facts / Retrieved Documents:] +{summarized_partial_context} +``` + +--- + +## Evaluation + +`evaluate.py` runs an LLM judge against the result JSON, scoring each model +answer against the gold answer. Configurable via: + +- `INFERENCE_JUDGE_URL` / `INFERENCE_JUDGE_MODEL` (config.sh) +- `--judge-url` / `--judge-model` (CLI) +- `--batch-size N` parallelism + +The default judge is `openai/gpt-oss-20b` via OpenRouter (set +`OPENROUTER_API_KEY`). Bigger judges produce more accurate scores; the 20B +judge has known length-limit issues on long multi-hop questions. + +Results from any pipeline can be re-scored later: + +```bash +python3 evaluate.py output_multi_shot_n50_w10_/result_multi_shot_n50.json +``` + +--- + +## Outputs + +Each pipeline writes to a timestamped `output____/` +directory containing: + +``` +result__.json # retrieval results + generated answers +score__.txt # LLM judge per-query scores + summary +run.log # full stdout +``` + +Result schema: + +```json +{ + "query": "...", + "retrieved_urls": [...], + "correct_urls": [...], + "llm_answer": "...", + "ground_truth_answer": "..." +} +``` + +--- + +## Reference + +- `CLAUDE.md` — architectural details, parameter reference, accuracy + experiments and chunk-size studies. +- `claude/design/config_layout.md` — `config.sh` design and resolution rules. +- `claude/design/cross_vendor_plan.md` — cross-vendor refactor reasoning + (per step, why each decision was made). +- `claude/README.md` — agent-notes for this repo (synced via git). + +--- + +## TODO + +- Token-based chunking aligned with the e5-base-v2 512-token limit (current + default is 768-char fixed-length). +- Configurable sentence-transformers `batch_size` (currently uses the SDK + default of 32). +- GPU FAISS for index sizes that outgrow CPU. +- Stronger judge model than gpt-oss-20b (current judge hits length limits on + complex multi-hop questions, capping accuracy measurements). diff --git a/e2e/config.template.sh b/e2e/config.template.sh new file mode 100644 index 0000000000..85fcb0bdd3 --- /dev/null +++ b/e2e/config.template.sh @@ -0,0 +1,83 @@ +# ============================================================================= +# config.template.sh — copy to config.sh and edit per system. config.sh is +# gitignored. +# +# Resolution order per variable: +# 1. Already-exported env var (e.g. DEVICE=cpu bash scripts/run_*.sh) +# 2. Value set in config.sh +# 3. Built-in default in the script +# +# Anything you don't set falls back to the script's built-in default. +# ============================================================================= + +# ── Ingestion pipeline (scripts/run_ingestion.sh) ───────────────────────────── +INGESTION_DEVICE="cpu" +INGESTION_EMBEDDING_DEVICE="${INGESTION_EMBEDDING_DEVICE:-${INGESTION_DEVICE}}" +INGESTION_NUM_EMBEDDING_DEVICES=4 +INGESTION_CHUNK_LEN=768 +INGESTION_CHUNK_OVERLAP=32 +INGESTION_RETRIEVER_MODEL="/data/model/e5-base-v2" +INGESTION_DOC_DIR="doc_html" +INGESTION_PASSAGES_JSON="passages/doc_html_len768_ov32_word.json" +INGESTION_DB="vector_html_hnsw_len768_ov32_word" + +# ── Inference pipeline (run_multi_shot.sh, run_single_shot.sh) ──────────────── +INFERENCE_DEVICE="cpu" +INFERENCE_EMBEDDING_DEVICE="${INFERENCE_EMBEDDING_DEVICE:-${INFERENCE_DEVICE}}" +INFERENCE_RERANKER_DEVICE="${INFERENCE_RERANKER_DEVICE:-${INFERENCE_DEVICE}}" +INFERENCE_DB="vector_html_hnsw_len768_ov32_word" +INFERENCE_RETRIEVER_MODEL="/data/model/e5-base-v2" +INFERENCE_TOP_K_RETRIEVER=15 +INFERENCE_MAX_ITERATIONS=5 +INFERENCE_MAX_SUB_QUERIES=3 +INFERENCE_TEMPERATURE=1.0 +INFERENCE_MAX_RETRIES=5 +INFERENCE_N_QUERIES=5 +INFERENCE_NUM_WORKERS=1 + +# LLM endpoints (vLLM, OpenRouter, etc.) +INFERENCE_LLM_URL="http://127.0.0.1:8123/v1/chat/completions" +INFERENCE_MODEL="/model/gpt-oss-20b-mxfp4" +INFERENCE_QUERY_MODEL="/model/gpt-oss-120b-mxfp4" + +# Per-component endpoint splits. Each defaults to INFERENCE_LLM_URL / +# INFERENCE_MODEL when empty. Set when components live on different servers +# (e.g. small grader on one vLLM, large query/sufficiency on another). +# INFERENCE_GRADER_URL="http://127.0.0.1:8124/v1/chat/completions" +# INFERENCE_GRADER_MODEL="/model/gpt-oss-20b" +# INFERENCE_QUERY_URL="http://127.0.0.1:8123/v1/chat/completions" +# INFERENCE_SUFFICIENCY_URL="http://127.0.0.1:8123/v1/chat/completions" +# INFERENCE_SUFFICIENCY_MODEL="/model/gpt-oss-120b" + +# Judge (used by evaluate.py at the end of the run scripts) +INFERENCE_JUDGE_URL="https://openrouter.ai/api/v1/chat/completions" +INFERENCE_JUDGE_MODEL="openai/gpt-oss-20b" + +# ── Oracle evaluation (run_oracle.sh) ───────────────────────────────────────── +INFERENCE_ORACLE_BATCH_SIZE=4 +INFERENCE_ORACLE_TIMEOUT=2400 +# INFERENCE_ORACLE_ENABLE_THINKING=1 # uncomment to pass --enable-thinking +INFERENCE_ORACLE_DATASET="data/frames_dataset.tsv" +INFERENCE_ORACLE_WIKI_DIR="wiki_articles" + +# Override GPU index allocator (e.g. when auto-detect picks the wrong devices). +# Comma-separated 0-based indices; subset of available CUDA/XPU devices. +# Per-component: +# INFERENCE_EMBEDDING_GPU_DEVICES="0,1" # one entry per --num_embedding_devices worker +# INFERENCE_RERANKER_GPU_DEVICES="2" + +# Per-component NUMA / OMP placement (applied inside the per-process worker). +# Pins the worker's CPU set + memory to the given node; OMP threads default +# to the node's physical core count if not set. +# INFERENCE_RERANKER_NUMA_NODE=0 +# INFERENCE_RERANKER_OMP_NUM_THREADS=21 +# INFERENCE_EMBEDDING_NUMA_NODES="0,0,1,1" # one node per embedding worker +# INFERENCE_EMBEDDING_OMP_NUM_THREADS=21 # cap per worker; default = even split + +# ── Python-side env vars (CPU_*) ────────────────────────────────────────────── +# These only fire when a model lands on CPU (gated in apply_cpu_threading_env). +# Uncomment to override Python defaults: +# CPU_DISABLE_NUMA=1 +# CPU_NUMA_NODE=0 +# CPU_NUMA_CORES="43-85" +# CPU_OMP_NUM_THREADS=43 diff --git a/e2e/db_manifest.py b/e2e/db_manifest.py new file mode 100644 index 0000000000..432456e0fd --- /dev/null +++ b/e2e/db_manifest.py @@ -0,0 +1,258 @@ +#!/usr/bin/env python3 +"""Cross-system vector DB sanity check. + +Workflow: + # System A (after building DB): + python3 db_manifest.py write \\ + --db vector_html_hnsw_len768_ov32_word.db \\ + --output manifest_intel_xpu.json + + # System B (after building DB independently): + python3 db_manifest.py verify \\ + --db vector_html_hnsw_len768_ov32_word.db \\ + --manifest manifest_intel_xpu.json + +The passage corpus is fingerprinted from the DB's docstore directly — no +external passages file needed. +""" + +import argparse +import gzip +import hashlib +import json +import random +import sys +from pathlib import Path +from typing import Dict, List + +import pandas as pd + +from retrieve import VectorDB + + +def _open_manifest(path: str, mode: str): + """Open a manifest file, transparently gzip-compressing if path ends in .gz.""" + if path.endswith(".gz"): + return gzip.open(path, mode) + return open(path, mode) + + +SAMPLE_SEED = 0xC0FFEE +NUM_SAMPLE_EMBEDDINGS = 50 +NUM_PROBE_QUERIES = 10 +PROBE_TOP_K = 5 +DEFAULT_COSINE_THRESHOLD = 0.9999 +DEFAULT_TOP_K_DEPTH = 3 + + +def _sha256_docstore(db: "VectorDB") -> str: + """SHA256 of all passages in index order; identifies the source corpus.""" + h = hashlib.sha256() + n = len(db._vector_store.index_to_docstore_id) + for i in range(n): + doc_id = db._vector_store.index_to_docstore_id[i] + doc = db._vector_store.docstore.search(doc_id) + h.update(doc.page_content.encode("utf-8", errors="replace")) + h.update(b"\x00") + return h.hexdigest() + + +def _cosine(a: List[float], b: List[float]) -> float: + import math + dot = sum(x * y for x, y in zip(a, b)) + na = math.sqrt(sum(x * x for x in a)) + nb = math.sqrt(sum(y * y for y in b)) + if na == 0 or nb == 0: + return 0.0 + return dot / (na * nb) + + +def _load_db(db_path: str, retriever_model: str) -> VectorDB: + db_path_obj = Path(db_path if db_path.endswith(".db") else f"{db_path}.db") + if not db_path_obj.exists(): + raise FileNotFoundError(f"DB file not found: {db_path_obj}") + + db = VectorDB( + retriever_model=retriever_model, + device="cpu", + embedding_device="cpu", + load_embeddings=False, + ) + db.from_serialized(db_path_obj.as_posix()) + return db + + +def _load_probe_queries(dataset_path: str, n: int) -> List[Dict]: + df = pd.read_csv(dataset_path, sep="\t") + rng = random.Random(SAMPLE_SEED) + indices = sorted(rng.sample(range(len(df)), min(n, len(df)))) + return [{"index": i, "prompt": str(df.iloc[i]["Prompt"])} for i in indices] + + +def _gather_top_k(db: VectorDB, queries: List[Dict], k: int) -> List[Dict]: + out = [] + for q in queries: + results = db.lookup(q["prompt"], k=k) + urls = [] + for doc in results: + md = getattr(doc, "metadata", None) or {} + url = md.get("original_url") or md.get("source") or md.get("base_filename") or "" + urls.append(url) + out.append({"index": q["index"], "top_k_urls": urls}) + return out + + +def _gather_sample_embeddings(db: VectorDB, total: int, n: int) -> Dict: + rng = random.Random(SAMPLE_SEED) + indices = sorted(rng.sample(range(total), min(n, total))) + + docstore = db._vector_store.docstore + embeddings = [] + for idx in indices: + # docstore is keyed by string ids; FAISS internally maps int->id->doc. + doc_id = db._vector_store.index_to_docstore_id.get(idx) + if doc_id is None: + raise RuntimeError(f"docstore has no entry for index {idx}") + doc = docstore.search(doc_id) + emb = db.embed_query(doc.page_content) + embeddings.append(list(emb)) + return {"indices": indices, "embeddings": embeddings} + + +def cmd_write(args): + db = _load_db(args.db, args.retriever_model) + total_passages = len(db._vector_store.index_to_docstore_id) + + print(f"[manifest] DB has {total_passages} passages, dim={db._embedding_dimension}") + + corpus_sha = _sha256_docstore(db) + sample_block = _gather_sample_embeddings(db, total_passages, NUM_SAMPLE_EMBEDDINGS) + probe_queries = _load_probe_queries(args.dataset, NUM_PROBE_QUERIES) + probe_block = _gather_top_k(db, probe_queries, PROBE_TOP_K) + + manifest = { + "version": 1, + "corpus_sha256": corpus_sha, + "retriever_model": args.retriever_model, + "vector_index_method": "hnsw", + "total_passages": total_passages, + "embedding_dim": db._embedding_dimension, + "sample_seed": SAMPLE_SEED, + "sample_embeddings": sample_block, + "probe_queries": probe_queries, + "probe_top_k": probe_block, + } + + with _open_manifest(args.output, "wt") as f: + json.dump(manifest, f, indent=2) + print(f"[manifest] wrote {args.output}") + + +def cmd_verify(args): + with _open_manifest(args.manifest, "rt") as f: + manifest = json.load(f) + + db = _load_db(args.db, manifest["retriever_model"]) + total_passages = len(db._vector_store.index_to_docstore_id) + + failures = [] + + # Exact-match fields. + if total_passages != manifest["total_passages"]: + failures.append( + f"total_passages mismatch: local={total_passages} manifest={manifest['total_passages']}" + ) + if db._embedding_dimension != manifest["embedding_dim"]: + failures.append( + f"embedding_dim mismatch: local={db._embedding_dimension} " + f"manifest={manifest['embedding_dim']}" + ) + + # Corpus fingerprint (sha256 of all passage texts in index order). + local_corpus_sha = _sha256_docstore(db) + if local_corpus_sha != manifest["corpus_sha256"]: + failures.append( + f"corpus sha256 mismatch:\n" + f" local = {local_corpus_sha}\n" + f" manifest = {manifest['corpus_sha256']}" + ) + + # Sample-embedding cosine similarity. + cosines = [] + for idx, ref_emb in zip(manifest["sample_embeddings"]["indices"], + manifest["sample_embeddings"]["embeddings"]): + doc_id = db._vector_store.index_to_docstore_id.get(idx) + if doc_id is None: + failures.append(f"sample idx {idx}: not present in local DB") + continue + doc = db._vector_store.docstore.search(doc_id) + local_emb = db.embed_query(doc.page_content) + cosines.append((idx, _cosine(local_emb, ref_emb))) + + if cosines: + worst_idx, worst_cos = min(cosines, key=lambda x: x[1]) + mean_cos = sum(c for _, c in cosines) / len(cosines) + print(f"[verify] sample embeddings: mean cosine={mean_cos:.6f} " + f"min={worst_cos:.6f} (idx={worst_idx}) threshold={args.cosine_threshold}") + if worst_cos < args.cosine_threshold: + failures.append( + f"sample embedding cosine below threshold: " + f"min={worst_cos:.6f} (idx={worst_idx}) < threshold={args.cosine_threshold}\n" + f" mean={mean_cos:.6f}" + ) + + # Probe-query top-K rank check. + probe_queries = manifest["probe_queries"] + local_top = _gather_top_k(db, probe_queries, PROBE_TOP_K) + ref_top = {r["index"]: r["top_k_urls"] for r in manifest["probe_top_k"]} + + rank_failures = [] + for entry in local_top: + local_urls = entry["top_k_urls"][:args.top_k_depth] + ref_urls = ref_top.get(entry["index"], [])[:args.top_k_depth] + if local_urls != ref_urls: + rank_failures.append( + f" query idx {entry['index']}: top-{args.top_k_depth} differs\n" + f" local : {local_urls}\n" + f" ref : {ref_urls}" + ) + + print(f"[verify] probe queries: {len(probe_queries)} queries, " + f"top-{args.top_k_depth} {len(probe_queries) - len(rank_failures)}/" + f"{len(probe_queries)} match") + if rank_failures: + failures.append("probe-query top-K rank mismatch:\n" + "\n".join(rank_failures)) + + if failures: + print("\n[verify] FAILED:") + for f in failures: + print(f" - {f}") + sys.exit(1) + print("\n[verify] OK") + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + sub = parser.add_subparsers(dest="cmd", required=True) + + pw = sub.add_parser("write", help="Generate a reference manifest from a DB.") + pw.add_argument("--db", required=True) + pw.add_argument("--retriever_model", default="/data/model/e5-base-v2") + pw.add_argument("--dataset", default="data/frames_dataset.tsv") + pw.add_argument("--output", required=True) + pw.set_defaults(func=cmd_write) + + pv = sub.add_parser("verify", help="Verify a DB against a reference manifest.") + pv.add_argument("--db", required=True) + pv.add_argument("--manifest", required=True) + pv.add_argument("--cosine-threshold", type=float, default=DEFAULT_COSINE_THRESHOLD) + pv.add_argument("--top-k-depth", type=int, default=DEFAULT_TOP_K_DEPTH) + pv.set_defaults(func=cmd_verify) + + args = parser.parse_args() + args.func(args) + + +if __name__ == "__main__": + main() diff --git a/e2e/download_docs.py b/e2e/download_docs.py new file mode 100644 index 0000000000..42356dde28 --- /dev/null +++ b/e2e/download_docs.py @@ -0,0 +1,796 @@ +#!/usr/bin/env python3 +""" +Download documents (PDF/HTML) from Wikipedia URLs with enhanced error handling. + +This script provides a unified interface for downloading Wikipedia pages as either PDF or HTML files, +with robust error handling, retry logic, and concurrent processing. +""" + +import asyncio +import json +import argparse +import random +import time +import concurrent.futures +from pathlib import Path +from typing import List, Set, Tuple, Dict, Optional, Any +from abc import ABC, abstractmethod +import re +import urllib.parse +import logging +from collections import Counter +from utils import save_url_mapping, get_base_filename +import subprocess +import os +import getpass +import ast +from multiprocessing import Pool +from tqdm import tqdm +import pandas as pd +from datasets import load_dataset + +try: + import requests +except ImportError: + requests = None + + +def fix_malformed_url(url: str) -> str: + # Remove text fragments (everything after #:~:text=) + if '#:~:text=' in url: + url = url.split('#:~:text=')[0] + + # Fix double URL-encoding. The FRAMES dataset occasionally contains URLs + # whose percent signs were themselves percent-encoded, e.g. "%2527" should + # be "%27" (an apostrophe). Decode one extra layer when we see "%25" sequences + # followed by two hex digits. + if re.search(r'%25[0-9A-Fa-f]{2}', url): + url = re.sub( + r'%25([0-9A-Fa-f]{2})', + lambda m: '%' + m.group(1), + url, + ) + + # Fix missing closing parentheses + open_parens = url.count('(') + close_parens = url.count(')') + + if open_parens > close_parens: + missing_closes = open_parens - close_parens + url += ')' * missing_closes + + return url + + + + + +class BaseDownloader(ABC): + """Base class for downloading web pages in different formats.""" + + def __init__(self, output_dir: str, processes: int = 10): + self.output_dir = Path(output_dir) + self.output_dir.mkdir(exist_ok=True) + self.processes = processes + self.url_mapping = {} + + @abstractmethod + def get_file_extension(self) -> str: + """Return the file extension for this downloader.""" + pass + + def create_filename(self, url: str) -> str: + """Generate a filename from URL.""" + filename = url.replace("https://", "").replace("/", "_").replace(":", "_") + + # Truncate if too long + max_length = 200 + if len(filename) > max_length: + filename = filename[:max_length] + + return filename + self.get_file_extension() + + @abstractmethod + def download_single_url(self, url: str, output_path: Path) -> Tuple[bool, str]: + """ + Download a single URL to the specified path. + + Returns: + Tuple of (success: bool, error_message: str) + """ + pass + + def process_url(self, args_tuple: Tuple[str, Path, int, int]) -> Tuple[bool, str, str, str]: + """Process a single URL - designed for multiprocessing.""" + url, output_dir, index, total = args_tuple + + # Create safe filename + filename = self.create_filename(url) + output_path = output_dir / filename + + # Skip if file already exists + if output_path.exists(): + return True, filename, "Skipping", url + + # Download the URL + try: + success, error_msg = self.download_single_url(url, output_path) + if success: + # Check if file was actually created and has reasonable size + if output_path.exists() and output_path.stat().st_size > 100: + return True, filename, "Success", url + else: + size = output_path.stat().st_size if output_path.exists() else 0 + error_msg = f"File too small or empty ({size} bytes)" + return False, filename, error_msg, url + else: + return False, filename, error_msg, url + + except Exception as e: + return False, filename, f"Exception: {str(e)[:100]}", url + + def download_urls(self, urls: List[str], retry_failures: bool = True) -> Dict[str, Any]: + """Download multiple URLs with parallel processing and progress tracking.""" + + if not urls: + print("No URLs found to process") + return {"successful": 0, "failed": 0, "failed_urls": []} + + print(f"Processing {len(urls)} URLs with {self.processes} parallel processes...") + + # Create progress bar + progress_bar = tqdm( + total=len(urls), + desc="Starting downloads...", + unit="URL" + ) + + # Process URLs in parallel with progress bar + start_time = time.time() + + # Prepare arguments for multiprocessing + process_args = [(url, self.output_dir, i + 1, len(urls)) + for i, url in enumerate(urls)] + + # Process with progress bar updates + results = [] + failed_urls = [] # Track failed URLs for detailed reporting + + with Pool(processes=self.processes) as pool: + for result in pool.imap(self.process_url, process_args): + success, filename, status, url = result + results.append((success, filename)) + + base_filename = get_base_filename(filename) + self.url_mapping[base_filename] = url + + # Update progress bar with status + if status == "Skipping": + progress_bar.set_description(f"Skipping: {filename[:30]}...") + elif status == "Success": + progress_bar.set_description(f"✓ Success: {filename[:30]}...") + else: + # This is a failure case + progress_bar.set_description(f"✗ {status}: {filename[:30]}...") + failed_urls.append((filename, status, url)) + print(f"\n❌ FAILED: {filename}") + print(f" URL: {url}") + print(f" Error: {status}") + + progress_bar.update(1) + + progress_bar.close() + + # Save URL mapping to JSON file + self.save_url_mapping() + + # Count results + successful = sum(1 for success, _ in results if success) + failed = len(results) - successful + + end_time = time.time() + duration = end_time - start_time + + print(f"\nDownload complete! Successful: {successful}, Failed: {failed}") + print(f"Total time: {duration:.2f} seconds") + print(f"Average time per URL: {duration/len(urls):.2f} seconds") + + # Print detailed failure report if there were failures + if failed_urls: + print(f"\n=== FAILED DOWNLOADS DETAILS ===") + for i, (filename, status, url) in enumerate(failed_urls, 1): + print(f"{i:2d}. {filename}") + print(f" URL: {url}") + print(f" Error: {status}") + print() + + # Retry failed URLs if requested + if retry_failures: + retry_result = self.retry_failed_urls(failed_urls) + successful += retry_result["successful"] + failed = retry_result["still_failed"] + else: + print(f"\n✅ All downloads completed successfully!") + + return { + "successful": successful, + "failed": failed, + "failed_urls": failed_urls, + "duration": duration + } + + def save_url_mapping(self): + """Save URL mapping to JSON file.""" + save_url_mapping(str(self.output_dir), self.url_mapping) + + def retry_failed_urls(self, failed_urls: List[Tuple[str, str, str]]) -> Dict[str, int]: + """Retry downloading failed URLs, but skip certain types of permanent failures.""" + if not failed_urls: + return {"successful": 0, "still_failed": 0} + + # Filter out failures that shouldn't be retried (permanent failures). + # Note: We intentionally do NOT include "HTTP error 4" here because that + # would also match retryable 429 (Too Many Requests) responses. + permanent_failure_keywords = [ + "404", "Page not found", + "HTTP error 410", "HTTP error 451", + "Invalid or empty content", + ] + + retryable_urls = [] + permanent_failures = [] + + for filename, status, url in failed_urls: + is_permanent = any(keyword in status for keyword in permanent_failure_keywords) + if is_permanent: + permanent_failures.append((filename, status, url)) + else: + retryable_urls.append((filename, status, url)) + + if permanent_failures: + print(f"\nSkipping {len(permanent_failures)} permanent failures (404s, etc.)") + + if not retryable_urls: + print("No retryable URLs found.") + return {"successful": 0, "still_failed": len(permanent_failures)} + + print(f"\n=== RETRYING FAILED DOWNLOADS ===") + print(f"Retrying {len(retryable_urls)} failed URLs (skipping {len(permanent_failures)} permanent failures)...") + + # Prepare arguments for retry + retry_args = [(url, self.output_dir, i + 1, len(retryable_urls)) + for i, (_, _, url) in enumerate(retryable_urls)] + + # Create progress bar for retry + progress_bar = tqdm(total=len(retryable_urls), desc="Retrying...", unit="URL") + + successful_retries = 0 + still_failed = [] + + with Pool(processes=self.processes) as pool: + for result in pool.imap(self.process_url, retry_args): + success, filename, status, url = result + + if success: + progress_bar.set_description(f"✓ Retry Success: {filename[:30]}...") + successful_retries += 1 + else: + progress_bar.set_description(f"✗ Retry Failed: {filename[:30]}...") + still_failed.append((filename, status, url)) + print(f"\n❌ RETRY FAILED: {filename}") + print(f" URL: {url}") + print(f" Error: {status}") + + progress_bar.update(1) + + progress_bar.close() + + # Combine still failed with permanent failures + all_failed = still_failed + permanent_failures + + print(f"\nRetry complete! Successfully retried: {successful_retries}, Still failed: {len(all_failed)}") + print(f" - Retryable failures: {len(still_failed)}") + print(f" - Permanent failures (404s, etc.): {len(permanent_failures)}") + + if all_failed: + print(f"\n=== STILL FAILED AFTER RETRY ===") + for i, (filename, status, url) in enumerate(all_failed, 1): + print(f"{i:2d}. {filename}") + print(f" URL: {url}") + print(f" Error: {status}") + print() + + return {"successful": successful_retries, "still_failed": len(all_failed)} + + +class PDFDownloader(BaseDownloader): + """Download web pages as PDFs using wkhtmltopdf.""" + + def get_file_extension(self) -> str: + return ".pdf" + + + def download_single_url(self, url: str, output_path: Path) -> Tuple[bool, str]: + """Download a single URL as PDF using wkhtmltopdf with try-fix-retry approach.""" + def attempt_pdf_download(target_url: str) -> Tuple[bool, str, bool]: + """ + Attempt to download a URL as PDF. + Returns (success, error_message, is_404_like_error) + """ + command = ( + f'wkhtmltopdf --page-size A4 --margin-top 0.75in --margin-right 0.75in ' + f'--margin-bottom 0.75in --margin-left 0.75in --encoding UTF-8 ' + f'--load-error-handling ignore --load-media-error-handling ignore ' + f'--javascript-delay 2000 "{target_url}" "{output_path}"' + ) + + try: + result = subprocess.run( + command, + shell=True, + capture_output=True, + text=True, + timeout=120 + ) + + if result.returncode == 0: + return True, "Success", False + else: + error_msg = f"wkhtmltopdf failed (return code: {result.returncode})" + if result.stderr: + stderr_text = result.stderr[:200] + error_msg += f" - {stderr_text}" + # Check for 404-like errors in stderr + is_404 = any(phrase in stderr_text.lower() for phrase in + ['404', 'not found', 'page not found', 'http error']) + return False, error_msg, is_404 + return False, error_msg, False + + except subprocess.TimeoutExpired: + return False, "Timeout (120s)", False + + # Try original URL first + success, error_msg, is_404 = attempt_pdf_download(url) + + if success: + return True, "Success" + + # If it was a 404-like error, try to fix the URL and retry + if is_404: + fixed_url = fix_malformed_url(url) + if fixed_url != url: + print(f"Retrying PDF with fixed URL: {url} -> {fixed_url}") + success, retry_error_msg, _ = attempt_pdf_download(fixed_url) + if success: + return True, f"Success (fixed URL)" + else: + return False, f"Original: {error_msg}; Fixed attempt: {retry_error_msg}" + + # Return original error if no fix was attempted or fix failed + return False, error_msg + + +class HTMLDownloader(BaseDownloader): + """Download web pages as HTML files using requests.""" + + # Per-process retry policy for transient errors (429, 5xx) + MAX_RETRIES = 5 + BASE_BACKOFF = 2.0 # seconds; exponential: BASE_BACKOFF * 2**attempt + MAX_BACKOFF = 60.0 # cap on a single sleep + + def __init__(self, output_dir: str, processes: int = 4, delay: float = 1.0, timeout: int = 30): + # HTML downloading can use parallel processes with rate limiting + super().__init__(output_dir, processes=processes) + self.delay = delay + self.timeout = timeout + + if requests is None: + raise ImportError("requests package is required for HTML downloads. Install with: pip install requests") + + self.session = requests.Session() + + # Wikipedia's User-Agent policy asks for a descriptive UA that identifies + # the tool/operator and includes a contact URL or email. Generic browser + # UAs are aggressively rate-limited. Operators may override via the + # WIKIPEDIA_DOWNLOADER_UA env var. + # See https://meta.wikimedia.org/wiki/User-Agent_policy + default_ua = ( + "mvrag-inference-e2e/1.0 " + "(https://github.com/; contact: rag-bench@local) " + "python-requests" + ) + self.session.headers.update({ + 'User-Agent': os.environ.get('WIKIPEDIA_DOWNLOADER_UA', default_ua), + 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', + 'Accept-Language': 'en-US,en;q=0.9', + }) + + def get_file_extension(self) -> str: + return ".html" + + + def _compute_backoff(self, attempt: int, retry_after_header: Optional[str]) -> float: + """Compute backoff delay honoring Retry-After if present, with jitter.""" + if retry_after_header: + try: + # Retry-After is usually an integer number of seconds + return min(float(retry_after_header), self.MAX_BACKOFF) + except (TypeError, ValueError): + pass + # Exponential backoff with full jitter + cap = min(self.BASE_BACKOFF * (2 ** attempt), self.MAX_BACKOFF) + return random.uniform(self.BASE_BACKOFF, cap) + + def download_single_url(self, url: str, output_path: Path) -> Tuple[bool, str]: + """Download a single URL as HTML using requests with try-fix-retry approach.""" + def attempt_download(target_url: str) -> Tuple[bool, str, bool]: + """ + Attempt to download a URL, retrying transient errors (429, 5xx). + Returns (success, error_message, is_404_like_error) + """ + last_error = "Unknown error" + for attempt in range(self.MAX_RETRIES): + try: + response = self.session.get(target_url, timeout=self.timeout) + + # Retry on 429 (rate limit) and 5xx (server errors) + if response.status_code == 429 or 500 <= response.status_code < 600: + last_error = ( + f"HTTP error {response.status_code}: " + f"{response.reason or 'transient error'}" + ) + if attempt < self.MAX_RETRIES - 1: + sleep_for = self._compute_backoff( + attempt, response.headers.get('Retry-After') + ) + print( + f"⏳ {response.status_code} for {target_url} " + f"(attempt {attempt + 1}/{self.MAX_RETRIES}); " + f"sleeping {sleep_for:.1f}s before retry" + ) + time.sleep(sleep_for) + continue + return False, last_error, False + + response.raise_for_status() + + # Check if we got redirected to a different article + if response.url != target_url: + print(f"Redirected from {target_url} to {response.url}") + + if 'charset' in response.headers.get('content-type', ''): + encoding = response.encoding + else: + encoding = 'utf-8' + + html_content = response.content.decode(encoding, errors='ignore') + + if 'wikipedia' not in html_content.lower() or len(html_content) < 1000: + return False, f"Invalid or empty content (length: {len(html_content)})", False + + with open(output_path, 'w', encoding='utf-8') as f: + f.write(html_content) + + # Add rate limiting after successful download + time.sleep(self.delay) + + return True, "Success", False + + except requests.HTTPError as e: + status = e.response.status_code if e.response is not None else None + if status == 404: + return False, f"Page not found (404): {target_url}", True + last_error = f"HTTP error {status}: {str(e)[:100]}" + return False, last_error, False + except requests.RequestException as e: + last_error = f"Request error: {str(e)[:100]}" + if attempt < self.MAX_RETRIES - 1: + sleep_for = self._compute_backoff(attempt, None) + print( + f"⏳ Network error for {target_url} " + f"(attempt {attempt + 1}/{self.MAX_RETRIES}); " + f"sleeping {sleep_for:.1f}s before retry: {last_error}" + ) + time.sleep(sleep_for) + continue + return False, last_error, False + except Exception as e: + return False, f"Exception: {str(e)[:100]}", False + + return False, last_error, False + + # Try original URL first + success, error_msg, is_404 = attempt_download(url) + + if success: + return True, "Success" + + # If it was a 404-like error, try to fix the URL and retry + if is_404: + fixed_url = fix_malformed_url(url) + if fixed_url != url: + print(f"Retrying with fixed URL: {url} -> {fixed_url}") + success, retry_error_msg, _ = attempt_download(fixed_url) + if success: + return True, f"Success (fixed URL)" + else: + return False, f"Original: {error_msg}; Fixed attempt: {retry_error_msg}" + + # Return original error if no fix was attempted or fix failed + return False, error_msg + + def download_urls(self, urls: List[str], retry_failures: bool = True): + """Download URLs with parallel processing and rate limiting.""" + return super().download_urls(urls, retry_failures) + + + + + + + + +def download_frames_dataset(output_dir): + """Download the FRAMES dataset from Hugging Face and save as TSV.""" + print("Downloading FRAMES dataset from Hugging Face...") + + try: + # Load the dataset + dataset = load_dataset("google/frames-benchmark", split="test") + + # Convert to pandas DataFrame + df = dataset.to_pandas() + + # Create output directory if it doesn't exist + output_dir = Path(output_dir) + output_dir.mkdir(exist_ok=True) + + # Save as TSV + tsv_path = output_dir / "frames_dataset.tsv" + df.to_csv(tsv_path, sep='\t', index=False) + + print(f"✅ FRAMES dataset downloaded and saved to: {tsv_path}") + print(f"Dataset contains {len(df)} rows") + print(f"Columns: {list(df.columns)}") + + return str(tsv_path) + + except Exception as e: + print(f"❌ Error downloading FRAMES dataset: {e}") + return None + + +_WIKI_URL_PATTERN = re.compile( + # Match Wikipedia URLs, allowing commas that are part of the URL (e.g. in + # disambiguation suffixes like "Quincy,_Massachusetts") while still treating + # ", " / ",]" as list separators in the FRAMES dataset. + r'https://en\.wikipedia\.org/wiki/(?:[^\s\],]|,(?=[^\s\],]))+' +) + + +def _strip_url_trailing_garbage(url: str) -> str: + """Strip artifacts that are clearly not part of a Wikipedia URL. + + Wikipedia article titles legitimately end with characters that look like + sentence punctuation (e.g. "_F.C.", "_Sr.", "Yahoo!", "Tick,_Tick..._Boom!", + "Who's_Afraid_of_Virginia_Woolf%3F"), so we deliberately do NOT strip + trailing ".", "!", "?", ":", ";", or quotes. + + The only artifact we strip is an unmatched trailing ")" that comes from + Markdown link syntax like "[label](https://...)". Disambiguation URLs such + as "/wiki/Quincy_(film)" keep their balanced parens intact. + """ + while url.endswith(')') and url.count('(') < url.count(')'): + url = url[:-1] + return url + + +def extract_wikipedia_links(item): + """Extract Wikipedia links from a FRAMES dataset item. + + The dataset stores lists of URLs as comma-separated strings (sometimes inside + Markdown link syntax). Wikipedia URLs themselves can legitimately contain + commas — most commonly in disambiguation suffixes like + "Quincy,_Massachusetts" — so we cannot simply split on commas. Instead we + match URLs greedily but treat a comma followed by whitespace/"]"/another + comma as a list separator. + """ + if isinstance(item, str): + item = ast.literal_eval(item) + + links = [] + for entry in item: + for match in _WIKI_URL_PATTERN.findall(entry): + cleaned = _strip_url_trailing_garbage(match) + if cleaned: + links.append(cleaned) + return links + + +def extract_urls_from_frames_dataset(tsv_path: str, max_urls: Optional[int] = None) -> List[str]: + """Extract unique Wikipedia URLs from FRAMES dataset TSV file.""" + # Load dataset + df = pd.read_csv(tsv_path, sep='\t') + + # Apply to all rows in df.wiki_links + urls = set() + for item in df.wiki_links: + for link in extract_wikipedia_links(item): + urls.add(link) + + urls = list(urls) + + # Limit number if specified + if max_urls and max_urls > 0: + urls = urls[:max_urls] + + return urls + + + + + +def main(): + parser = argparse.ArgumentParser( + description='Download Wikipedia pages as PDFs or HTML files from FRAMES dataset or other sources.\nBy default, URLs are validated before downloading to avoid 404 errors.', + formatter_class=argparse.RawTextHelpFormatter + ) + + # Format selection + parser.add_argument( + '--format', + choices=['pdf', 'html'], + default='pdf', + help='Output format: pdf or html (default: pdf)' + ) + + # URL sources (mutually exclusive) + url_group = parser.add_mutually_exclusive_group() + url_group.add_argument( + '--tsv-path', + help='Input TSV file from FRAMES dataset' + ) + + url_group.add_argument( + '--urls', + nargs='+', + help='List of URLs to download' + ) + url_group.add_argument( + '--url-file', + help='File containing URLs (one per line)' + ) + + # Output options + parser.add_argument( + '--output-dir', + help='Output directory (default: doc_pdf for PDF, doc_html for HTML)' + ) + parser.add_argument( + '--data-dir', + default='data', + help='Directory for dataset files (default: data)' + ) + + # Processing options + parser.add_argument( + '--max-files', + type=int, + help='Maximum number of URLs to process (default: all)' + ) + parser.add_argument( + '--processes', + type=int, + default=10, + help='Number of parallel processes (default: 10). ' + 'Wikipedia rate-limits aggressive HTML scrapers; ' + 'consider --processes 4 for HTML.' + ) + + # HTML-specific options + parser.add_argument( + '--delay', + type=float, + default=1.0, + help='Per-process delay between HTML downloads in seconds (default: 1.0). ' + 'Effective request rate ~= processes / delay.' + ) + parser.add_argument( + '--timeout', + type=int, + default=30, + help='Timeout for HTML requests in seconds (default: 30)' + ) + + # Dataset options + parser.add_argument( + '--download-dataset', + action='store_true', + help='Download FRAMES dataset if no TSV provided' + ) + + args = parser.parse_args() + + # Set default output directory based on format + if args.output_dir is None: + args.output_dir = f"doc_{args.format}" + + # Get URLs from various sources + urls = None + + if args.url_file: + try: + with open(args.url_file, 'r', encoding='utf-8') as f: + urls = [line.strip() for line in f if line.strip()] + print(f"Loaded {len(urls)} URLs from {args.url_file}") + except Exception as e: + print(f"Error loading URLs from file: {e}") + return + + elif args.urls: + urls = args.urls + + elif args.tsv_path or args.download_dataset: + # Handle FRAMES dataset + tsv_path = args.tsv_path + + if tsv_path is None and args.download_dataset: + print("=== DOWNLOADING FRAMES DATASET ===") + tsv_path = download_frames_dataset(args.data_dir) + if tsv_path is None: + print("❌ Failed to download FRAMES dataset. Exiting.") + return + elif tsv_path is None: + print("❌ No TSV path provided. Use --tsv-path or --download-dataset") + return + + urls = extract_urls_from_frames_dataset(tsv_path, args.max_files) + print(f"Extracted {len(urls)} URLs from FRAMES dataset: {tsv_path}") + + else: + # Default: download dataset + print("=== DOWNLOADING FRAMES DATASET ===") + tsv_path = download_frames_dataset(args.data_dir) + if tsv_path is None: + print("❌ Failed to download FRAMES dataset. Exiting.") + return + + urls = extract_urls_from_frames_dataset(tsv_path, args.max_files) + print(f"Extracted {len(urls)} URLs from FRAMES dataset: {tsv_path}") + + if not urls: + print("No URLs found to download") + return + + # Initialize appropriate downloader based on format + if args.format == 'pdf': + # Set XDG_RUNTIME_DIR for wkhtmltopdf + user = getpass.getuser() + xdg_runtime_dir = f"/tmp/runtime-{user}" + os.environ["XDG_RUNTIME_DIR"] = xdg_runtime_dir + + downloader = PDFDownloader(args.output_dir, args.processes) + + elif args.format == 'html': + if requests is None: + print("❌ HTML format requires 'requests' package. Install with: pip install requests") + return + + downloader = HTMLDownloader( + output_dir=args.output_dir, + processes=args.processes, + delay=args.delay, + timeout=args.timeout + ) + + else: + print(f"❌ Unsupported format: {args.format}") + return + + # Download URLs + print(f"\n=== DOWNLOADING {len(urls)} URLs AS {args.format.upper()} ===") + result = downloader.download_urls(urls, retry_failures=True) + + +if __name__ == "__main__": + main() diff --git a/e2e/download_models.sh b/e2e/download_models.sh new file mode 100644 index 0000000000..86f31a2bdb --- /dev/null +++ b/e2e/download_models.sh @@ -0,0 +1,78 @@ +#!/bin/bash +# Downloads models required for the multi-shot RAG pipeline to /model/. +# +# Models: +# - intfloat/e5-base-v2 → /model/e5-base-v2 (embeddings) +# - colbert-ir/colbertv2.0 → /model/colbertv2.0 (reranker) +# +# Usage (run on host, not inside container): +# bash download_models.sh # download both +# bash download_models.sh e5-base-v2 # embeddings only +# bash download_models.sh colbertv2.0 # reranker only +# +# Set DEST_DIR to override the default /model destination. + +set -e + +DEST_DIR="${DEST_DIR:-/model}" + +# Disable SSL verification to work around corporate CA cert issues +export HF_HUB_DISABLE_SSL_VERIFICATION=1 +export CURL_CA_BUNDLE="" +export REQUESTS_CA_BUNDLE="" + +download_model() { + local HF_REPO="$1" + local LOCAL_DIR="$2" + + if [[ -d "${LOCAL_DIR}" && -n "$(ls -A "${LOCAL_DIR}" 2>/dev/null)" ]]; then + echo "[skip] ${HF_REPO} already exists at ${LOCAL_DIR}" + return 0 + fi + + echo "" + echo "=== Downloading ${HF_REPO} → ${LOCAL_DIR} ===" + mkdir -p "${LOCAL_DIR}" + + python3 - < llm_answer + # Only include successfully completed queries + successful = df[df['success'] == True] + return {row['query']: row['llm_answer'] for _, row in successful.iterrows()} + else: + # Legacy JSON format + data = json.loads(path.read_text(encoding="utf-8")) + results = data.get("results", []) + return {entry.get("prompt"): entry.get("llm_answer", "") for entry in results if entry.get("prompt")} + + +def _parse_score_value(value) -> int: + """Normalize judge score values to 0 or 1.""" + if isinstance(value, bool): + return 1 if value else 0 + if isinstance(value, (int, float)): + return 1 if float(value) >= 0.5 else 0 + if isinstance(value, str): + lowered = value.strip().lower() + if lowered in {"1", "true", "correct", "yes"}: + return 1 + if lowered in {"0", "false", "incorrect", "no"}: + return 0 + try: + return 1 if float(lowered) >= 0.5 else 0 + except ValueError: + return 0 + return 0 + + +def _extract_json_dict(content: str) -> Optional[dict]: + """Attempt to recover a JSON object from the judge response.""" + if not content: + return None + + stripped = content.strip() + + candidates = [] + + # Remove optional fenced code blocks (``` or ```json) + if stripped.startswith("```"): + fence_stripped = stripped.split("```", 1)[1] + fence_stripped = fence_stripped.strip() + if fence_stripped.lower().startswith("json"): + fence_stripped = fence_stripped[4:].strip() + closing_idx = fence_stripped.find("```") + if closing_idx != -1: + fence_stripped = fence_stripped[:closing_idx] + candidates.append(fence_stripped.strip()) + + # Look for a JSON object substring. + match = re.search(r"\{.*\}", stripped, re.DOTALL) + if match: + candidates.append(match.group(0)) + + candidates.append(stripped) + + for candidate in candidates: + candidate = candidate.strip() + if not candidate: + continue + try: + parsed = json.loads(candidate) + if isinstance(parsed, dict): + return parsed + except json.JSONDecodeError: + continue + return None + + +def call_judge(session: requests.Session, service_url: str, model: str, question: str, gold: str, pred: str): + prompt = ( + "You judge whether the model answer correctly answers the question based on semantic equivalence to the gold answer.\n\n" + "GRADING RULES:\n" + "1. If model answer and gold answer are EXACTLY the same (ignoring case/punctuation), score 1\n" + "2. Focus on whether the model answer contains the KEY INFORMATION that answers the question\n" + "3. Minor differences are acceptable:\n" + " - Missing articles (a, an, the)\n" + " - Missing units when the number is correct (e.g., '50' vs '50 years')\n" + " - Additional correct details not in gold answer\n" + " - Different word order for lists (e.g., 'A and B' vs 'B, A')\n" + " - Missing location qualifiers when answer is already specific (e.g., 'Las Vegas' vs 'Las Vegas, Nevada')\n" + " - Different formatting (e.g., 'Dwight D. Eisenhower' vs 'Dwight D Eisenhower')\n" + " - Brief answers that directly answer the question vs verbose gold answers\n" + "4. Score 0 ONLY if:\n" + " - Model answer is factually wrong\n" + " - Model answer is missing ESSENTIAL information that changes the meaning\n" + " - Model answer does NOT answer what the question asked\n" + "5. Do NOT penalize for:\n" + " - Lack of context/explanation when question doesn't require it\n" + " - Different level of detail if core answer is correct\n" + " - Missing information the question didn't ask for\n\n" + f"Question: {question}\n" + f"Gold Answer: {gold}\n" + f"Model Answer: {pred}\n\n" + "Return JSON with keys score (1 or 0) and explanation." + ) + payload = { + "model": model, + "messages": [ + {"role": "system", "content": "You are a lenient grading assistant focused on semantic correctness, not exact string matching."}, + {"role": "user", "content": prompt} + ], + "temperature": 0.0, + "max_tokens": 256 + } + + # Add OpenRouter authentication headers if using OpenRouter + headers = {} + if "openrouter.ai" in service_url and OPENROUTER_API_KEY: + headers = { + "Authorization": f"Bearer {OPENROUTER_API_KEY}", + "HTTP-Referer": "https://github.com/anthropics/e2e-docgrader", + "X-Title": "E2E DocGrader Evaluation" + } + + response = session.post(service_url, json=payload, headers=headers, timeout=120) + response.raise_for_status() + data = response.json() + # Defensive: handle missing or malformed 'choices' in response + choices = data.get("choices") + if not choices or not isinstance(choices, list) or not choices[0] or "message" not in choices[0] or "content" not in choices[0]["message"]: + print("[ERROR] Judge response missing 'choices' or 'content':", data) + # Return score 0, explanation with raw response, and raw data + return 0, f"Malformed judge response: {data}", str(data) + content = choices[0]["message"]["content"] + if content is None: + print("[ERROR] Judge response 'content' is None:", data) + return 0, f"Judge response content is None: {data}", str(data) + content = content.strip() + + parsed = _extract_json_dict(content) + + if parsed is not None: + score_value = parsed.get("score") + score = _parse_score_value(score_value) + explanation_value = parsed.get("explanation") + if isinstance(explanation_value, str): + explanation = explanation_value.strip() + elif explanation_value is None: + explanation = "" + else: + # Convert non-string explanations (e.g., dict) into compact JSON + explanation = json.dumps(explanation_value, ensure_ascii=False) + else: + normalized = content.lower() + score = 1 if "correct" in normalized and "incorrect" not in normalized else 0 + explanation = content + + return score, explanation, content + + +def _judge_row(idx, prompt, gold, pred, service_url, model): + """Call judge for a single row, returning (idx, prompt, gold, pred, score, explanation, raw).""" + session = requests.Session() + score, explanation, raw = call_judge(session, service_url, model, prompt, gold, pred) + return idx, prompt, gold, pred, score, explanation, raw + + +def evaluate(results_path: Path, dataset_path: Path, service_url: str, model: str, batch_size: int = 16): + # Check for OpenRouter API key if using OpenRouter + if "openrouter.ai" in service_url and not OPENROUTER_API_KEY: + print("ERROR: OPENROUTER_API_KEY environment variable not set") + print("Usage: OPENROUTER_API_KEY=\"sk-or-v1-YOUR_KEY_HERE\" python evaluate.py ...") + exit(1) + + predictions = load_results(results_path) + + # Show checkpoint stats if loading from pickle + if results_path.suffix == '.pkl': + with open(results_path, 'rb') as f: + checkpoint_df = pickle.load(f) + print(f"CHECKPOINT STATISTICS") + print("=" * 80) + print(f"Total queries in checkpoint: {len(checkpoint_df)}") + print(f"Successful queries: {(checkpoint_df['success'] == True).sum()}") + print(f"Failed queries: {(checkpoint_df['success'] == False).sum()}") + if 'num_docs' in checkpoint_df.columns: + total_docs = checkpoint_df['num_docs'].sum() + total_missing = checkpoint_df['num_missing_docs'].sum() + print(f"Total documents referenced: {total_docs}") + print(f"Missing documents: {total_missing} ({100*total_missing/total_docs:.2f}%)") + print("=" * 80) + print() + + df = pd.read_csv(dataset_path, sep="\t") + + # Build list of items to judge + items = [] + for idx, row in df.iterrows(): + prompt = row.get("Prompt") + gold = str(row.get("Answer", "")).strip() + if prompt not in predictions: + continue + pred = str(predictions[prompt]).strip() + items.append((idx, prompt, gold, pred)) + + if not items: + print("No matching predictions found in results file.") + return + + total = len(items) + unknown = sum(1 for _, _, _, pred in items if pred.lower() == "unknown") + + # Submit all judge calls in parallel (batch_size workers), print as they complete + score_sum = 0 + with ThreadPoolExecutor(max_workers=batch_size) as executor: + futures = { + executor.submit(_judge_row, idx, prompt, gold, pred, service_url, model): idx + for idx, prompt, gold, pred in items + } + for future in as_completed(futures): + idx, prompt, gold, pred, score, explanation, raw = future.result() + score_sum += score + print("=" * 80) + print(f"Prompt {idx}: {prompt}") + print(f"Gold: {gold}") + print(f"Answer: {pred}") + print(f"Judge Score: {score}") + print(f"Judge Explanation: {explanation if explanation else raw}") + + judged = len(items) + accuracy = score_sum / judged + unknown_ratio = unknown / total + print("\nSUMMARY") + print("-" * 80) + print(f"Evaluated Samples: {judged}") + print(f"Unknown Ratio: {unknown_ratio:.3f}") + print(f"Accuracy: {accuracy:.3f}") + + +def parse_args(): + parser = argparse.ArgumentParser(description="Evaluate single-shot results using an LLM judge.") + parser.add_argument("results", type=Path, help="Path to results (result_single_shot.json or oracle_checkpoint.pkl)") + parser.add_argument("--dataset", type=Path, default=Path("data/frames_dataset.tsv"), help="Evaluation dataset TSV") + parser.add_argument("--judge-url", default=DEFAULT_JUDGE_URL, help="Judge service endpoint") + parser.add_argument("--judge-model", default=DEFAULT_JUDGE_MODEL, help="Judge model identifier") + parser.add_argument("--batch-size", type=int, default=16, help="Number of concurrent judge requests (default: 16)") + return parser.parse_args() + + +if __name__ == "__main__": + args = parse_args() + evaluate(args.results, args.dataset, args.judge_url, args.judge_model, args.batch_size) diff --git a/e2e/evaluation.py b/e2e/evaluation.py new file mode 100644 index 0000000000..758f2ffc84 --- /dev/null +++ b/e2e/evaluation.py @@ -0,0 +1,799 @@ +""" +Retrieval Evaluation Metrics Module + +This module provides comprehensive retrieval evaluation metrics including: +- Precision@k, Recall@k, F1@k +- Mean Average Precision (MAP) +- Comprehensive retrieval metrics +- Detailed dataset analysis by reasoning type and answer link count + +Designed for reuse across different retrieval systems including multi-hop QA. +""" + +import pandas as pd +from typing import List, Dict, Any, Optional, Tuple, Union, Callable +from collections import defaultdict +from utils import filter_dataset_by_difficulty + + +def calculate_retrieval_metrics(expected_urls: List[str], retrieved_urls: List[str], k_values: List[int] = [1, 3, 5, 10]) -> Dict[str, float]: + """ + Calculate comprehensive retrieval metrics. + + Args: + expected_urls: List of expected/ground truth URLs + retrieved_urls: List of retrieved URLs in ranking order + k_values: List of k values for Precision@k, Recall@k, F1@k + + Returns: + Dictionary containing all calculated metrics + """ + expected_set = set(url for url in expected_urls if url and url.strip()) + + # Handle edge cases + if not expected_set: + return {f'precision@{k}': 1.0 if len(retrieved_urls) == 0 else 0.0 for k in k_values} | \ + {f'recall@{k}': 1.0 for k in k_values} | \ + {f'f1@{k}': 1.0 if len(retrieved_urls) == 0 else 0.0 for k in k_values} | \ + {'average_precision': 1.0 if len(retrieved_urls) == 0 else 0.0} + + metrics = {} + + # Calculate metrics for different k values, including @N (actual retrieved count) + num_retrieved = len(retrieved_urls) + num_expected = len(expected_set) + k_values_with_n = k_values + [num_retrieved] # Add N to k_values + + for k in k_values_with_n: + # Determine the label (use 'N' for the actual retrieved count) + k_label = 'N' if k == num_retrieved else str(k) + + # Get top k documents + top_k = retrieved_urls[:k] + top_k_set = set(top_k) + relevant_retrieved = len(expected_set.intersection(top_k_set)) + + # Precision@k: fraction of retrieved documents that are relevant + precision_k = relevant_retrieved / k if k > 0 else 0.0 + metrics[f'precision@{k_label}'] = precision_k + + # Recall@k: fraction of relevant documents that are retrieved + recall_k = relevant_retrieved / num_expected if num_expected > 0 else 0.0 + metrics[f'recall@{k_label}'] = recall_k + + # F1@k: harmonic mean of precision and recall + if precision_k + recall_k > 0: + f1_k = 2 * (precision_k * recall_k) / (precision_k + recall_k) + else: + f1_k = 0.0 + metrics[f'f1@{k_label}'] = f1_k + + # Mean Average Precision (MAP) - considers ranking order + ap_sum = 0.0 + relevant_found = 0 + + for i, url in enumerate(retrieved_urls): + if url in expected_set: + relevant_found += 1 + precision_at_i = relevant_found / (i + 1) + ap_sum += precision_at_i + + average_precision = ap_sum / len(expected_set) if len(expected_set) > 0 else 0.0 + metrics['average_precision'] = average_precision + + return metrics + + +def evaluate_retrieval_query(rag_db, query: str, expected_urls: List[str], + top_k_retriever: int = 50, top_k_reranking: int = 10, + verbose: bool = True, no_rerank: bool = False, + retrieval_strategy: str = "fixed_k", print_results: bool = False, + return_results: bool = False, **strategy_params) -> Union[Dict[str, Any], Tuple[Dict[str, Any], List[Any]]]: + """ + Evaluate a single retrieval query and return comprehensive retrieval metrics. + + Args: + rag_db: RAG database instance + query: Query string + expected_urls: List of expected URLs + top_k_retriever: Number of documents to retrieve initially + top_k_reranking: Number of documents after reranking + verbose: Whether to print detailed results + no_rerank: Skip reranking step for fair comparison between retrieval methods + retrieval_strategy: Strategy for retrieval ("fixed_k", "top_p", "relative") + **strategy_params: Parameters for adaptive retrieval strategies + + Returns: + Dictionary containing all metrics. When return_results=True, returns a tuple of (metrics_dict, retrieved_results). + """ + import time + + # Step 1: Time the initial retrieval + retrieval_start = time.perf_counter() + if retrieval_strategy == "fixed_k": + results = rag_db.lookup(query, k=top_k_retriever) + else: + from retrieve.filter import filter + max_results = strategy_params.pop("max_results", 20) + results = filter(rag_db, query, method=retrieval_strategy, + max_results=max_results, **strategy_params) + retrieval_time = time.perf_counter() - retrieval_start + + # Step 2: Apply reranking if enabled and reranker is available + reranking_time = 0.0 + if not no_rerank and hasattr(rag_db, '_reranker_model') and rag_db._reranker_model is not None: + # Safety check: If no results retrieved, skip reranking + if not results: + if verbose: + print(f"Warning: No documents retrieved for query: {query[:50]}") + else: + reranking_start = time.perf_counter() + # Extract text content for reranking (rerank expects strings) + passages = [result.page_content for result in results] + scored_passages = rag_db.rerank(query, passages) + + # Reconstruct document objects with reranked order + # scored_passages is [(text, score), ...] ordered by score + reranked_results = [] + for text, score in scored_passages: + # Find the original document object for this text + for doc in results: + if doc.page_content == text: + reranked_results.append(doc) + break + + # Apply top_k_reranking limit AFTER reranking + # For adaptive strategies (top_p, relative, etc.), respect the number of documents + # selected by the strategy, only limit for fixed_k + if retrieval_strategy == "fixed_k": + results = reranked_results[:top_k_reranking] + else: + # For adaptive strategies, keep all documents selected by the strategy + results = reranked_results + reranking_time = time.perf_counter() - reranking_start + + # Extract URLs from results in order (maintaining ranking) + retrieved_urls = [] + for result in results: + if 'original_url' in result.metadata and result.metadata['original_url']: + retrieved_urls.append(result.metadata['original_url']) + + # Deduplicate URLs preserving first appearance order (for accurate MAP calculation) + deduplicated_urls = list(dict.fromkeys(retrieved_urls)) # Preserves order, removes duplicates + + # Calculate comprehensive metrics using deduplicated URLs (accurate MAP) + expected_set = set(url for url in expected_urls if url and url.strip()) + metrics = calculate_retrieval_metrics(list(expected_set), deduplicated_urls) + + # Track both passages and unique documents + num_passages = len(results) + num_unique_docs = len(deduplicated_urls) + + if verbose: + print(f"Query: {query:50}") + matches = len(expected_set.intersection(set(deduplicated_urls))) + print(f"Expected ({len(expected_set)}): {sorted(list(expected_set)[:3])}{'...' if len(expected_set) > 3 else ''}") + print(f"Retrieved ({num_passages} passages, {num_unique_docs} unique docs): {deduplicated_urls[:3]}{'...' if num_unique_docs > 3 else ''}") + print(f"Matches: {matches}") + + metric_categories = [ + ("P", "precision"), + ("R", "recall"), + ("F1", "f1") + ] + + for label, metric_prefix in metric_categories: + parts = [f"{label}@N: {metrics[f'{metric_prefix}@N']:.3f}"] + for k in [3, 5, 10]: + key = f"{metric_prefix}@{k}" + if key in metrics: + parts.append(f"{label}@{k}: {metrics[key]:.3f}") + print(", ".join(parts)) + + print(f"MAP: {metrics['average_precision']:.3f}") + print("-" * 80) + + # Print detailed results for single query mode + if print_results: + print(f"\n{retrieval_strategy.upper()} lookup took time. {len(results)} results found:") + + # Display which PDFs the passages are from + for i, result in enumerate(results, 1): + print(f"{i}. {result.metadata}") + print("-" * 50) + + # Show reranked results if reranker is available and reranking was used + if not no_rerank and rag_db._reranker_model is not None: + print(f"\nReranking to top-{top_k_reranking}") + print(f"Reranking results:") + + for i, result in enumerate(results, 1): + print(f"{i}. {result.metadata}") + print(f" {result.page_content[:200]}...") + print("-" * 50) + else: + if no_rerank: + print("No reranker used (--no-rerank specified)") + else: + print("No reranker available - showing retrieval results only") + + # Calculate retrieval performance metrics + total_time = retrieval_time + reranking_time + docs_per_second = len(results) / total_time if total_time > 0 else 0 + + # Add retrieval performance to metrics + retrieval_metrics = { + 'retrieval_time': retrieval_time, + 'reranking_time': reranking_time, + 'total_retrieval_time': total_time, + 'retrieved_passages_count': len(results), + 'retrieved_docs_count': num_unique_docs, + 'docs_per_second': docs_per_second + } + + # Print retrieval performance if in benchmark mode and single query mode (not evaluation) + if hasattr(rag_db, '_benchmark') and rag_db._benchmark and print_results: + print(f"\n🔍 RETRIEVAL PERFORMANCE METRICS") + print("=" * 50) + print(f"📊 Query: '{query[:50]}{'...' if len(query) > 50 else ''}'") + print(f"⏱️ Retrieval time: {retrieval_time*1000:.2f}ms") + if reranking_time > 0: + print(f"🔄 Reranking time: {reranking_time*1000:.2f}ms") + print(f"🕐 Total time: {total_time*1000:.2f}ms") + print(f"📦 Documents retrieved: {len(results)}") + print(f"🚀 Retrieval speed: {docs_per_second:.1f} docs/sec") + print(f"💾 Time per query: {total_time:.4f}s") + print() + + # Return metrics dict with retrieval performance + merged_metrics = {**metrics, **retrieval_metrics} + if return_results: + return merged_metrics, results + return merged_metrics + + +def run_evaluation(rag_db, dataset_path: str, + top_k_retriever: int = 50, top_k_reranking: int = 10, + max_queries: Optional[int] = None, no_rerank: bool = False, + retrieval_strategy: str = "fixed_k", detailed_analysis: bool = False, + difficulty: int = 0, collect_results: bool = False, + result_handler: Optional[Callable[[str, List[Any], Dict[str, Any]], Optional[Any]]] = None, + **strategy_params) -> Union[Dict[str, float], Tuple[Dict[str, float], List[Dict[str, Any]]]]: + """ + Run comprehensive evaluation on a dataset with detailed metrics reporting. + + Args: + rag_db: RAG database instance + dataset_path: Path to the dataset TSV file + top_k_retriever: Number of documents to retrieve initially + top_k_reranking: Number of documents after reranking + max_queries: Maximum number of queries to evaluate (None = all) + no_rerank: Skip reranking step for fair comparison between retrieval methods + retrieval_strategy: Strategy for retrieval ("fixed_k", "top_p", "relative") + detailed_analysis: If True, print detailed breakdown by reasoning types and link counts + difficulty: Minimum number of answer links required (0 = no filtering) + collect_results: If True, also collect retrieval outputs for each query + result_handler: Optional callback invoked per query with (prompt, retrieved_docs, metrics) + **strategy_params: Parameters for adaptive retrieval strategies + + Returns: + Dictionary of averaged metrics across all queries. When collect_results=True, returns a tuple of (metrics_dict, collected_results). + """ + df = pd.read_csv(dataset_path, sep='\t') + + # Filter by difficulty if specified + df = filter_dataset_by_difficulty(df, difficulty) + + # Limit number of queries if specified + if isinstance(max_queries, int) and max_queries > 0: + df = df.head(max_queries) + else: + max_queries = len(df) + + print(f"\nRunning evaluation on {max_queries} queries from dataset") + + # Aggregate metrics collection + total_metrics = {} + all_query_metrics = [] # Store individual query metrics for detailed analysis + retrieval_times = [] + reranking_times = [] + total_times = [] + docs_per_sec_list = [] + collected_queries = [] if collect_results else None + valid_queries = 0 + + for idx, row in df.iterrows(): + # Extract expected Wikipedia links + expected_urls = [] + for col in df.columns: + if col.startswith('wikipedia_link_') and pd.notna(row[col]): + expected_urls.append(row[col].strip()) + + if expected_urls: + # Get comprehensive metrics for this query + need_results = collect_results or (result_handler is not None) + metrics_output = evaluate_retrieval_query( + rag_db, row['Prompt'], expected_urls, + top_k_retriever, top_k_reranking, verbose=True, no_rerank=no_rerank, + retrieval_strategy=retrieval_strategy, return_results=need_results, + **strategy_params + ) + if need_results: + metrics, retrieved_docs = metrics_output + else: + metrics = metrics_output + retrieved_docs = [] + + if collect_results and retrieved_docs: + doc_entries = [] + seen_urls = set() + for doc in retrieved_docs: + url = None + if hasattr(doc, 'metadata'): + url = doc.metadata.get('original_url') or doc.metadata.get('source') + content = doc.page_content + elif isinstance(doc, dict): + url = doc.get('url') + content = doc.get('content', "") + else: + content = "" + if url and url in seen_urls: + continue + entry = { + "url": url, + "content": content[:2000] + } + doc_entries.append(entry) + if url: + seen_urls.add(url) + collected_queries.append({ + "prompt": row['Prompt'], + "docs": doc_entries + }) + + if result_handler: + result_handler(row['Prompt'], retrieved_docs, metrics) + + # Store metrics for detailed analysis if requested + if detailed_analysis: + all_query_metrics.append(metrics) + + # Collect retrieval performance metrics for statistics + if 'retrieval_time' in metrics: + retrieval_times.append(metrics['retrieval_time']) + reranking_times.append(metrics['reranking_time']) + total_times.append(metrics['total_retrieval_time']) + docs_per_sec_list.append(metrics['docs_per_second']) + + # Accumulate metrics + for metric_name, value in metrics.items(): + if metric_name not in total_metrics: + total_metrics[metric_name] = 0.0 + total_metrics[metric_name] += value + + valid_queries += 1 + + if valid_queries > 0: + # Calculate average metrics + avg_metrics = {name: total / valid_queries for name, total in total_metrics.items()} + + # Display results + results_title = "OVERALL EVALUATION RESULTS" if detailed_analysis else "EVALUATION RESULTS" + print(f"\n" + "="*60) + print(f"{results_title} ({valid_queries} queries)") + print(f"="*60) + print(f"PRECISION METRICS:") + print(f" Precision@N: {avg_metrics.get('precision@N', 0.0):.3f}") + if 'precision@1' in avg_metrics: + print(f" Precision@1: {avg_metrics['precision@1']:.3f}") + if 'precision@3' in avg_metrics: + print(f" Precision@3: {avg_metrics['precision@3']:.3f}") + if 'precision@5' in avg_metrics: + print(f" Precision@5: {avg_metrics['precision@5']:.3f}") + if 'precision@10' in avg_metrics: + print(f" Precision@10: {avg_metrics['precision@10']:.3f}") + print(f"") + print(f"RECALL METRICS:") + print(f" Recall@N: {avg_metrics.get('recall@N', 0.0):.3f}") + if 'recall@1' in avg_metrics: + print(f" Recall@1: {avg_metrics['recall@1']:.3f}") + if 'recall@3' in avg_metrics: + print(f" Recall@3: {avg_metrics['recall@3']:.3f}") + if 'recall@5' in avg_metrics: + print(f" Recall@5: {avg_metrics['recall@5']:.3f}") + if 'recall@10' in avg_metrics: + print(f" Recall@10: {avg_metrics['recall@10']:.3f}") + print(f"") + print(f"F1 METRICS:") + print(f" F1@N: {avg_metrics.get('f1@N', 0.0):.3f}") + if 'f1@1' in avg_metrics: + print(f" F1@1: {avg_metrics['f1@1']:.3f}") + if 'f1@3' in avg_metrics: + print(f" F1@3: {avg_metrics['f1@3']:.3f}") + if 'f1@5' in avg_metrics: + print(f" F1@5: {avg_metrics['f1@5']:.3f}") + if 'f1@10' in avg_metrics: + print(f" F1@10: {avg_metrics['f1@10']:.3f}") + print(f"") + print(f"RANKING METRICS:") + print(f" Mean Average Precision: {avg_metrics['average_precision']:.3f}") + print(f"") + print(f"RETRIEVAL STATISTICS:") + print(f" Avg Passages Retrieved: {avg_metrics.get('retrieved_passages_count', 0.0):.1f}") + print(f" Avg Unique Docs (N): {avg_metrics.get('retrieved_docs_count', 0.0):.1f}") + + # Add retrieval performance statistics if we have retrieval data + if retrieval_times and hasattr(rag_db, '_benchmark') and rag_db._benchmark: + import numpy as np + + print(f"") + print(f"🔍 RETRIEVAL PERFORMANCE STATISTICS:") + print(f" Retrieval Time (ms):") + print(f" Average: {np.mean(retrieval_times)*1000:.2f}ms") + print(f" P50 (Median): {np.percentile(retrieval_times, 50)*1000:.2f}ms") + print(f" P99: {np.percentile(retrieval_times, 99)*1000:.2f}ms") + + if any(t > 0 for t in reranking_times): + print(f" Reranking Time (ms):") + print(f" Average: {np.mean(reranking_times)*1000:.2f}ms") + print(f" P50 (Median): {np.percentile(reranking_times, 50)*1000:.2f}ms") + print(f" P99: {np.percentile(reranking_times, 99)*1000:.2f}ms") + + print(f" Total Query Time (ms):") + print(f" Average: {np.mean(total_times)*1000:.2f}ms") + print(f" P50 (Median): {np.percentile(total_times, 50)*1000:.2f}ms") + print(f" P99: {np.percentile(total_times, 99)*1000:.2f}ms") + + print(f" Retrieval Throughput (docs/sec):") + print(f" Average: {np.mean(docs_per_sec_list):.1f} docs/sec") + print(f" P50 (Median): {np.percentile(docs_per_sec_list, 50):.1f} docs/sec") + print(f" P99: {np.percentile(docs_per_sec_list, 99):.1f} docs/sec") + + print(f"="*60) + + # Print detailed analysis if requested + if detailed_analysis: + _print_detailed_analysis(df, all_query_metrics, valid_queries) + + if collect_results: + return avg_metrics, collected_queries or [] + return avg_metrics + else: + print("No valid queries found!") + if collect_results: + return {}, [] + return {} + + +def _print_detailed_analysis(df: pd.DataFrame, all_query_metrics: List[Dict[str, Any]], + valid_queries: int) -> None: + """ + Print detailed dataset analysis broken down by reasoning types and answer link counts. + (Internal helper function for run_evaluation) + + Args: + df: DataFrame with dataset (must have 'reasoning_types' column) + all_query_metrics: List of metrics dictionaries for each query + valid_queries: Number of valid queries processed + """ + if valid_queries == 0: + return + + print("\n" + "="*80) + print("DETAILED DATASET ANALYSIS") + print("="*80) + + # Prepare data - match metrics with reasoning types and link counts + analysis_data = [] + for idx, metrics in enumerate(all_query_metrics): + if idx < len(df): + row = df.iloc[idx] + reasoning_types = row.get('reasoning_types', 'Unknown') + + # Count Wikipedia links + num_links = sum(1 for col in df.columns + if col.startswith('wikipedia_link_') and pd.notna(row[col])) + + analysis_data.append({ + 'reasoning_types': reasoning_types, + 'num_links': num_links, + 'metrics': metrics + }) + + # === ANALYSIS 1: By Reasoning Classification === + print("\n" + "-"*80) + print("ANALYSIS BY REASONING CLASSIFICATION") + print("-"*80) + + # Group by reasoning types + reasoning_groups = defaultdict(list) + for data in analysis_data: + reasoning_groups[data['reasoning_types']].append(data['metrics']) + + # Calculate averages for each reasoning type + reasoning_results = [] + for reasoning_type, metrics_list in reasoning_groups.items(): + if not metrics_list: + continue + + avg_metrics = {} + for key in ['precision@N', 'recall@N', 'f1@N', 'average_precision']: + values = [m.get(key, 0.0) for m in metrics_list] + avg_metrics[key] = sum(values) / len(values) + + reasoning_results.append({ + 'type': reasoning_type, + 'count': len(metrics_list), + **avg_metrics + }) + + # Sort by count (most common first) + reasoning_results.sort(key=lambda x: x['count'], reverse=True) + + # Print top reasoning types + print(f"\nTop reasoning type combinations:") + print(f"{'Reasoning Type':<50} {'Count':>6} {'P@N':>6} {'R@N':>6} {'F1@N':>6} {'MAP':>6}") + print("-"*80) + + for i, result in enumerate(reasoning_results): + rt = result['type'][:48] if len(result['type']) > 48 else result['type'] + print(f"{rt:<50} {result['count']:6d} " + f"{result['precision@N']:6.3f} {result['recall@N']:6.3f} " + f"{result['f1@N']:6.3f} {result['average_precision']:6.3f}") + + # === ANALYSIS 2: By Individual Reasoning Tags === + print(f"\n" + "-"*80) + print("ANALYSIS BY INDIVIDUAL REASONING TAGS") + print("-"*80) + + # Parse reasoning tags (split by |) + tag_groups = defaultdict(list) + for data in analysis_data: + tags = [tag.strip() for tag in data['reasoning_types'].split('|')] + for tag in tags: + tag_groups[tag].append(data['metrics']) + + tag_results = [] + for tag, metrics_list in tag_groups.items(): + if not metrics_list: + continue + + avg_metrics = {} + for key in ['precision@N', 'recall@N', 'f1@N', 'average_precision']: + values = [m.get(key, 0.0) for m in metrics_list] + avg_metrics[key] = sum(values) / len(values) + + tag_results.append({ + 'tag': tag, + 'count': len(metrics_list), + 'percentage': len(metrics_list) / valid_queries * 100, + **avg_metrics + }) + + # Sort by count + tag_results.sort(key=lambda x: x['count'], reverse=True) + + print(f"\nPerformance by reasoning tag:") + print(f"{'Tag':<30} {'Count':>6} {'%':>6} {'P@N':>6} {'R@N':>6} {'F1@N':>6} {'MAP':>6}") + print("-"*80) + + for result in tag_results: + tag = result['tag'][:28] if len(result['tag']) > 28 else result['tag'] + print(f"{tag:<30} {result['count']:6d} {result['percentage']:5.1f}% " + f"{result['precision@N']:6.3f} {result['recall@N']:6.3f} " + f"{result['f1@N']:6.3f} {result['average_precision']:6.3f}") + + # === ANALYSIS 3: By Number of Answer Links === + print(f"\n" + "-"*80) + print("ANALYSIS BY NUMBER OF ANSWER LINKS (Multi-hop Analysis)") + print("-"*80) + + # Group by number of links + link_groups = defaultdict(list) + for data in analysis_data: + link_groups[data['num_links']].append(data['metrics']) + + link_results = [] + for num_links, metrics_list in link_groups.items(): + if not metrics_list: + continue + + avg_metrics = {} + for key in ['precision@N', 'recall@N', 'f1@N', 'average_precision']: + values = [m.get(key, 0.0) for m in metrics_list] + avg_metrics[key] = sum(values) / len(values) + + # Classify complexity + if num_links <= 2: + complexity = "Simple" + elif num_links <= 4: + complexity = "Multi-hop" + else: + complexity = "Complex" + + link_results.append({ + 'num_links': num_links, + 'complexity': complexity, + 'count': len(metrics_list), + 'percentage': len(metrics_list) / valid_queries * 100, + **avg_metrics + }) + + # Sort by number of links + link_results.sort(key=lambda x: x['num_links']) + + print(f"\nPerformance by number of Wikipedia links (reasoning hops):") + print(f"{'Links':>5} {'Complexity':<12} {'Count':>6} {'%':>6} {'P@N':>6} {'R@N':>6} {'F1@N':>6} {'MAP':>6}") + print("-"*80) + + for result in link_results: + print(f"{result['num_links']:5d} {result['complexity']:<12} " + f"{result['count']:6d} {result['percentage']:5.1f}% " + f"{result['precision@N']:6.3f} {result['recall@N']:6.3f} " + f"{result['f1@N']:6.3f} {result['average_precision']:6.3f}") + + # Summary by complexity category + print(f"\n" + "-"*80) + print("SUMMARY BY COMPLEXITY LEVEL") + print("-"*80) + + complexity_groups = defaultdict(list) + for result in link_results: + for _ in range(result['count']): + # Get original metrics for this group + complexity_groups[result['complexity']].append({ + 'precision@N': result['precision@N'], + 'recall@N': result['recall@N'], + 'f1@N': result['f1@N'], + 'average_precision': result['average_precision'] + }) + + # Calculate totals + complexity_summary = [] + for complexity in ["Simple", "Multi-hop", "Complex"]: + if complexity not in complexity_groups: + continue + + metrics_list = complexity_groups[complexity] + count = len(metrics_list) + + # Recalculate from link_results + matching_results = [r for r in link_results if r['complexity'] == complexity] + total_count = sum(r['count'] for r in matching_results) + + # Weighted average + weighted_metrics = {} + for key in ['precision@N', 'recall@N', 'f1@N', 'average_precision']: + weighted_sum = sum(r[key] * r['count'] for r in matching_results) + weighted_metrics[key] = weighted_sum / total_count if total_count > 0 else 0.0 + + complexity_summary.append({ + 'complexity': complexity, + 'count': total_count, + 'percentage': total_count / valid_queries * 100, + **weighted_metrics + }) + + print(f"\n{'Complexity':<12} {'Count':>6} {'%':>6} {'P@N':>6} {'R@N':>6} {'F1@N':>6} {'MAP':>6}") + print("-"*80) + + for result in complexity_summary: + print(f"{result['complexity']:<12} {result['count']:6d} {result['percentage']:5.1f}% " + f"{result['precision@N']:6.3f} {result['recall@N']:6.3f} " + f"{result['f1@N']:6.3f} {result['average_precision']:6.3f}") + + # === ANALYSIS 4: Correlation Between Complexity and Reasoning Types === + print(f"\n" + "-"*80) + print("CORRELATION: COMPLEXITY vs REASONING TYPES") + print("-"*80) + + # Build correlation matrix: complexity level x reasoning tags + complexity_reasoning_data = defaultdict(lambda: defaultdict(list)) + + for data in analysis_data: + # Determine complexity + num_links = data['num_links'] + if num_links <= 2: + complexity = "Simple" + elif num_links <= 4: + complexity = "Multi-hop" + else: + complexity = "Complex" + + # Extract individual reasoning tags + tags = [tag.strip() for tag in data['reasoning_types'].split('|')] + for tag in tags: + complexity_reasoning_data[complexity][tag].append(data['metrics']) + + # Calculate statistics for each complexity-reasoning combination + print(f"\n1. REASONING TAG DISTRIBUTION BY COMPLEXITY:") + print(f"{'Reasoning Tag':<30} {'Simple':>10} {'Multi-hop':>10} {'Complex':>10} {'Total':>10}") + print("-"*80) + + # Get all unique tags + all_tags = set() + for complexity_data in complexity_reasoning_data.values(): + all_tags.update(complexity_data.keys()) + + tag_distribution = {} + for tag in sorted(all_tags): + simple_count = len(complexity_reasoning_data.get('Simple', {}).get(tag, [])) + multihop_count = len(complexity_reasoning_data.get('Multi-hop', {}).get(tag, [])) + complex_count = len(complexity_reasoning_data.get('Complex', {}).get(tag, [])) + total = simple_count + multihop_count + complex_count + + tag_distribution[tag] = { + 'simple': simple_count, + 'multihop': multihop_count, + 'complex': complex_count, + 'total': total + } + + tag_display = tag[:28] if len(tag) > 28 else tag + print(f"{tag_display:<30} {simple_count:10d} {multihop_count:10d} {complex_count:10d} {total:10d}") + + # Calculate percentage distribution + print(f"\n2. REASONING TAG PERCENTAGE BY COMPLEXITY:") + print(f"{'Reasoning Tag':<30} {'Simple %':>10} {'Multi %':>10} {'Complex %':>10}") + print("-"*80) + + for tag in sorted(all_tags): + dist = tag_distribution[tag] + total = dist['total'] + if total > 0: + simple_pct = (dist['simple'] / total) * 100 + multihop_pct = (dist['multihop'] / total) * 100 + complex_pct = (dist['complex'] / total) * 100 + + tag_display = tag[:28] if len(tag) > 28 else tag + print(f"{tag_display:<30} {simple_pct:9.1f}% {multihop_pct:9.1f}% {complex_pct:9.1f}%") + + # Calculate average number of links per reasoning tag + print(f"\n3. AVERAGE COMPLEXITY (# LINKS) BY REASONING TAG:") + print(f"{'Reasoning Tag':<30} {'Avg Links':>10} {'Count':>10}") + print("-"*80) + + tag_link_stats = defaultdict(list) + for data in analysis_data: + tags = [tag.strip() for tag in data['reasoning_types'].split('|')] + for tag in tags: + tag_link_stats[tag].append(data['num_links']) + + tag_avg_links = [] + for tag in sorted(all_tags): + links = tag_link_stats[tag] + if links: + avg_links = sum(links) / len(links) + tag_avg_links.append((tag, avg_links, len(links))) + + # Sort by average links (descending) + tag_avg_links.sort(key=lambda x: x[1], reverse=True) + + for tag, avg_links, count in tag_avg_links: + tag_display = tag[:28] if len(tag) > 28 else tag + print(f"{tag_display:<30} {avg_links:10.2f} {count:10d}") + + # Performance by complexity x reasoning tag (for top tags only) + print(f"\n4. PERFORMANCE BY COMPLEXITY x TOP REASONING TAGS:") + print("-"*80) + + # Get top 5 most common tags + top_tags = sorted(tag_distribution.items(), key=lambda x: x[1]['total'], reverse=True)[:5] + + for tag, _ in top_tags: + print(f"\n{tag}:") + print(f"{'Complexity':<12} {'Count':>6} {'P@N':>6} {'R@N':>6} {'F1@N':>6} {'MAP':>6}") + print("-"*70) + + for complexity in ["Simple", "Multi-hop", "Complex"]: + metrics_list = complexity_reasoning_data.get(complexity, {}).get(tag, []) + if metrics_list: + count = len(metrics_list) + avg_p = sum(m.get('precision@N', 0.0) for m in metrics_list) / count + avg_r = sum(m.get('recall@N', 0.0) for m in metrics_list) / count + avg_f1 = sum(m.get('f1@N', 0.0) for m in metrics_list) / count + avg_map = sum(m.get('average_precision', 0.0) for m in metrics_list) / count + + print(f"{complexity:<12} {count:6d} {avg_p:6.3f} {avg_r:6.3f} {avg_f1:6.3f} {avg_map:6.3f}") + + print("="*80) \ No newline at end of file diff --git a/e2e/ingestion_monitor.py b/e2e/ingestion_monitor.py new file mode 100644 index 0000000000..95a3a3abf6 --- /dev/null +++ b/e2e/ingestion_monitor.py @@ -0,0 +1,372 @@ +#!/usr/bin/env python3 +""" +Ingestion Performance Monitor - Real-time performance tracking for RAG ingestion pipeline. + +Usage: + from ingestion_monitor import IngestionMonitor + + monitor = IngestionMonitor() + + # Track document processing + with monitor.track_component("html_parsing"): + process_html_files(files) + + # Track embedding generation + with monitor.track_component("embedding_generation"): + embeddings = generate_embeddings(texts) + + # Get performance report + report = monitor.get_performance_report() +""" + +import time +import json +import os +from typing import Dict, List, Optional, Any +from contextlib import contextmanager +from dataclasses import dataclass, asdict +from pathlib import Path + +@dataclass +class ComponentMetrics: + """Metrics for a single pipeline component.""" + name: str + duration: float + input_size_bytes: int + output_size_bytes: int + items_processed: int + throughput_mb_per_sec: float + throughput_items_per_sec: float + is_pipeline_input: bool = False # Mark if this is a pipeline input component + is_pipeline_output: bool = False # Mark if this is a pipeline output component + +@dataclass +class IndexingTrendPoint: + """Single data point for indexing performance trend.""" + db_size: int # Number of items in DB at this point + batch_size: int # Number of items added in this batch + indexing_time: float # Time to add this batch (seconds) + throughput_items_per_sec: float + cumulative_time: float # Total time so far + +@dataclass +class IngestionReport: + """Complete ingestion performance report.""" + total_duration: float + total_input_bytes: int + total_output_bytes: int + total_items: int + overall_throughput_mb_per_sec: float + components: List[ComponentMetrics] + bottleneck_component: str + indexing_trend: List[IndexingTrendPoint] = None # For scaling analysis = "none" + bottleneck_component: str + +class IngestionMonitor: + """Real-time ingestion performance monitoring.""" + + def __init__(self): + self.components: Dict[str, ComponentMetrics] = {} + self.start_time = None # Will be set when ingestion starts + self.current_component = None + self.component_start_time = None + self.indexing_trend: List[IndexingTrendPoint] = [] + self.cumulative_indexing_time = 0.0 + + def start_ingestion(self): + """Mark the start of ingestion. Should be called at the beginning of ingest().""" + self.start_time = time.time() + + @contextmanager + def track_component(self, component_name: str, input_size_bytes: int = 0, + items_count: int = 0, text_only: bool = False, + is_pipeline_input: bool = False, is_pipeline_output: bool = False): + """Context manager to track performance of a pipeline component. + + Args: + component_name: Name of the component being tracked + input_size_bytes: Input data size in bytes + items_count: Number of items processed + text_only: If True, only count text content bytes (exclude metadata) + is_pipeline_input: If True, mark as pipeline input component for aggregation + is_pipeline_output: If True, mark as pipeline output component for aggregation + """ + start_time = time.time() + + class ComponentContext: + def __init__(self): + self.input_size_bytes = input_size_bytes + self.items_count = items_count + self.text_only = text_only + + def set_input_size(self, size_bytes: int): + self.input_size_bytes = size_bytes + + def set_item_count(self, count: int): + self.items_count = count + + def add_text_bytes(self, text_bytes: int): + """Add text-only bytes for passage tracking.""" + self.input_size_bytes += text_bytes + + context = ComponentContext() + + try: + self.current_component = component_name + self.component_start_time = start_time + yield context + + finally: + end_time = time.time() + duration = end_time - start_time + + # Calculate throughput + total_input = context.input_size_bytes + total_output = 0 # Output will be set separately + total_items = context.items_count + total_duration = duration + + # Check if component already exists (accumulate metrics) + if component_name in self.components: + existing = self.components[component_name] + # Accumulate metrics + total_duration = existing.duration + duration + total_input = existing.input_size_bytes + context.input_size_bytes + total_output = existing.output_size_bytes + 0 + total_items = existing.items_processed + context.items_count + + is_pipeline_input = is_pipeline_input or existing.is_pipeline_input + is_pipeline_output = is_pipeline_output or existing.is_pipeline_output + + throughput_mb = (total_input / (1024 * 1024)) / total_duration if total_duration > 0 else 0 + throughput_items = total_items / total_duration if total_duration > 0 else 0 + + self.components[component_name] = ComponentMetrics( + name=component_name, + duration=total_duration, + input_size_bytes=total_input, + output_size_bytes=total_output, + items_processed=total_items, + throughput_mb_per_sec=throughput_mb, + throughput_items_per_sec=throughput_items, + is_pipeline_input=is_pipeline_input, + is_pipeline_output=is_pipeline_output + ) + + def set_output_size(self, component_name: str, output_size_bytes: int): + """Set the output size for a component after processing.""" + if component_name in self.components: + self.components[component_name].output_size_bytes = output_size_bytes + + def set_output_size_callback(self, component_name: str, callback_fn): + """Set the output size for a component using a callback function. + + This is useful when the output size calculation is complex or requires + accessing class-specific data (e.g., BM25 index files). + + Args: + component_name: Name of the component + callback_fn: Function that returns the output size in bytes + """ + if component_name in self.components: + try: + output_size = callback_fn() + self.components[component_name].output_size_bytes = output_size + except Exception as e: + print(f"Warning: Failed to calculate output size for {component_name}: {e}") + + @contextmanager + def track_ingestion(self): + """Track overall ingestion performance.""" + self.start_time = time.time() # Set start_time for get_performance_report() + + class IngestionContext: + def __init__(self): + self.item_count = 0 + + def set_item_count(self, count: int): + self.item_count = count + + context = IngestionContext() + + try: + yield context + finally: + pass # start_time is checked by get_performance_report() + + def track_incremental_indexing(self, db_size_before: int, batch_size: int, + indexing_time: float): + """Track indexing performance for incremental batches to analyze scaling trends. + + Args: + db_size_before: Number of items in DB before adding this batch + batch_size: Number of items added in this batch + indexing_time: Time taken to index this batch (seconds) + """ + db_size_after = db_size_before + batch_size + throughput = batch_size / indexing_time if indexing_time > 0 else 0 + self.cumulative_indexing_time += indexing_time + + trend_point = IndexingTrendPoint( + db_size=db_size_after, + batch_size=batch_size, + indexing_time=indexing_time, + throughput_items_per_sec=throughput, + cumulative_time=self.cumulative_indexing_time + ) + + self.indexing_trend.append(trend_point) + + def get_performance_report(self) -> IngestionReport: + """Generate comprehensive performance report.""" + # Calculate duration from when start_ingestion() was called + if self.start_time is None: + raise ValueError("start_ingestion() must be called before getting performance report") + + total_duration = time.time() - self.start_time + + # Aggregate metrics based on pipeline input/output flags + # If no flags set, fall back to first component for input + input_components = [c for c in self.components.values() if c.is_pipeline_input] + output_components = [c for c in self.components.values() if c.is_pipeline_output] + + total_input = sum(c.input_size_bytes for c in input_components) + total_items = sum(c.items_processed for c in input_components) + total_output = sum(c.output_size_bytes for c in output_components) + + overall_throughput = (total_input / (1024 * 1024)) / total_duration if total_duration > 0 else 0 + + # Find bottleneck and calculate efficiency ratio + bottleneck_name = "none" + + if self.components: + bottleneck = min(self.components.values(), key=lambda x: x.throughput_mb_per_sec) + fastest = max(self.components.values(), key=lambda x: x.throughput_mb_per_sec) + bottleneck_name = bottleneck.name + + return IngestionReport( + total_duration=total_duration, + total_input_bytes=total_input, + total_output_bytes=total_output, + total_items=total_items, + overall_throughput_mb_per_sec=overall_throughput, + components=list(self.components.values()), + bottleneck_component=bottleneck_name, + indexing_trend=self.indexing_trend if self.indexing_trend else None + ) + + def save_report(self, filename: str = "ingestion_performance.json"): + """Save performance report to JSON file.""" + report = self.get_performance_report() + + # Convert to serializable format + report_dict = asdict(report) + + with open(filename, 'w') as f: + json.dump(report_dict, f, indent=2) + + return report_dict + + def print_summary(self): + """Print detailed performance summary with individual components.""" + report = self.get_performance_report() + + print("🚀 INGESTION PERFORMANCE SUMMARY") + print("=" * 60) + print(f"📊 Overall Metrics:") + print(f" Total duration: {report.total_duration:.2f}s") + print(f" Overall throughput: {report.overall_throughput_mb_per_sec:.2f} MB/s") + print(f" Items processed: {report.total_items:,}") + + # DEBUG: Show detailed breakdown of input data aggregation + input_components = [c for c in report.components if c.is_pipeline_input] + print(f"\n🔍 DEBUG: Input Data Breakdown (is_pipeline_input=True):") + print(f" {'Component':<30} {'Input Size (MB)':<20} {'Items':<15}") + print(f" {'-'*65}") + total_input_debug = 0 + for comp in input_components: + input_mb = comp.input_size_bytes / (1024*1024) + total_input_debug += comp.input_size_bytes + print(f" {comp.name:<30} {input_mb:>18.2f} MB {comp.items_processed:>12,}") + print(f" {'-'*65}") + print(f" {'TOTAL AGGREGATED INPUT':<30} {total_input_debug/(1024*1024):>18.2f} MB") + + # Show input data size from report (aggregated from marked input components or first component) + print(f"\n Input data size (from report): {report.total_input_bytes / (1024*1024):.2f} MB") + + # Show output size and expansion ratio if output data exists + if report.total_output_bytes > 0: + output_size_mb = report.total_output_bytes / (1024*1024) + expansion_ratio = report.total_output_bytes / report.total_input_bytes if report.total_input_bytes > 0 else 0 + print(f" Output data size: {output_size_mb:.2f} MB") + print(f" Output/Input ratio: {expansion_ratio:.1f}x") + + print(f" Bottleneck component: {report.bottleneck_component}") + + print(f"\n🔧 Component Performance Details:") + for component in sorted(report.components, key=lambda x: x.duration, reverse=True): + percentage = (component.duration / report.total_duration) * 100 if report.total_duration > 0 else 0 + mb_processed = component.input_size_bytes / (1024*1024) + avg_latency_ms = (component.duration * 1000 / component.items_processed) if component.items_processed > 0 else 0 + pipeline_flags = [] + if component.is_pipeline_input: + pipeline_flags.append("INPUT") + if component.is_pipeline_output: + pipeline_flags.append("OUTPUT") + flag_str = f" [{', '.join(pipeline_flags)}]" if pipeline_flags else "" + print(f" 📈 {component.name}{flag_str}:") + print(f" ⏱️ Duration: {component.duration:.3f}s ({percentage:.1f}% of total)") + print(f" 🚀 Throughput: {component.throughput_mb_per_sec:.2f} MB/s") + print(f" 📦 Items: {component.items_processed:,}") + print(f" 💾 Data: {mb_processed:.2f} MB") + print(f" ⚡ Avg latency: {avg_latency_ms:.2f}ms per item") + print() + + # Print indexing trend analysis if available + if report.indexing_trend and len(report.indexing_trend) > 1: + print("📈 VECTOR DB INDEXING SCALING ANALYSIS") + print("=" * 60) + print("DB Size → Batch Time (Throughput)") + + for i, point in enumerate(report.indexing_trend): + db_size_k = point.db_size // 1000 if point.db_size >= 1000 else point.db_size + size_unit = "K" if point.db_size >= 1000 else "" + + print(f" {db_size_k:>4}{size_unit} docs → {point.indexing_time:>6.3f}s ({point.throughput_items_per_sec:>6.1f} docs/sec)") + + # Calculate scaling trend + if len(report.indexing_trend) >= 3: + first_point = report.indexing_trend[0] + last_point = report.indexing_trend[-1] + + size_ratio = last_point.db_size / first_point.db_size if first_point.db_size > 0 else 0 + time_ratio = last_point.indexing_time / first_point.indexing_time if first_point.indexing_time > 0 else 0 + + if size_ratio > 1: + scaling_factor = time_ratio / size_ratio + if scaling_factor > 1.5: + trend_desc = "📈 Super-linear scaling (indexing gets slower with size)" + elif scaling_factor > 0.8: + trend_desc = "📊 Linear scaling (time proportional to size)" + else: + trend_desc = "📉 Sub-linear scaling (indexing gets more efficient)" + + print(f"\n💡 Trend Analysis:") + print(f" Size increased {size_ratio:.1f}x, time increased {time_ratio:.1f}x") + print(f" {trend_desc}") + print() + + +if __name__ == "__main__": + # Example usage + monitor = IngestionMonitor() + + # Simulate components + with monitor.track_component("html_parsing", 1024*1024, 100): # 1MB, 100 files + time.sleep(0.1) + + with monitor.track_component("embedding_generation", 512*1024, 500): # 512KB, 500 chunks + time.sleep(0.5) + + monitor.print_summary() + monitor.save_report("example_performance.json") diff --git a/e2e/llm_logger.py b/e2e/llm_logger.py new file mode 100644 index 0000000000..35ad5d80f8 --- /dev/null +++ b/e2e/llm_logger.py @@ -0,0 +1,259 @@ +#!/usr/bin/env python3 +""" +LLM call logger for tracking all LLM requests, responses, and token usage. +""" + +import os +import uuid +import time +import json +import threading +from datetime import datetime +from typing import Dict, List, Any, Optional + + +class LLMLogger: + """Logger for tracking all LLM calls with full input/output and metrics.""" + + def __init__(self, output_file: str = None, experiment_metadata: Dict[str, Any] = None): + self.session_id = str(uuid.uuid4()) + self.queries = [] + self.output_file = output_file + self.experiment_metadata = experiment_metadata or {} + self._lock = threading.Lock() + self._local = threading.local() + + # Initialize file with header if output_file provided + if self.output_file: + self._initialize_file() + + @property + def current_query(self): + return getattr(self._local, 'current_query', None) + + @current_query.setter + def current_query(self, value): + self._local.current_query = value + + def start_query(self, query_id: str, original_query: str): + """Start logging a new query""" + self.current_query = { + "query_id": query_id, + "original_query": original_query, + "timestamp_start": datetime.utcnow().isoformat() + "Z", + "llm_calls": [] + } + + def log_llm_call(self, + component: str, + hop_count: Optional[int], + payload: Dict, + response: Dict, + latency_ms: float, + context: Dict[str, Any] = None): + """Log a single LLM call with full input/output""" + + usage = response.get('usage', {}) + isl = usage.get('prompt_tokens', 0) + osl = usage.get('completion_tokens', 0) + + call_record = { + "call_id": str(uuid.uuid4()), + "component": component, + "hop_count": hop_count, + "timestamp": datetime.utcnow().isoformat() + "Z", + "input": { + "messages": payload.get('messages', []), + "model": payload.get('model'), + "temperature": payload.get('temperature'), + "max_tokens": payload.get('max_tokens'), + "top_p": payload.get('top_p'), + "top_k": payload.get('top_k'), + "reasoning_effort": payload.get('reasoning_effort'), + "other_params": { + k: v for k, v in payload.items() + if k not in ['messages', 'model', 'temperature', 'max_tokens', 'top_p', 'top_k', 'reasoning_effort'] + } + }, + "output": { + "response": self._extract_response_text(response), + "finish_reason": response.get('choices', [{}])[0].get('finish_reason') if response.get('choices') else None, + }, + "metrics": { + "isl": isl, + "osl": osl, + "total_tokens": isl + osl, + "latency_ms": round(latency_ms, 2), + "tokens_per_second": round(osl / (latency_ms / 1000), 2) if latency_ms > 0 else 0 + }, + "context": context or {} + } + + if self.current_query: + self.current_query["llm_calls"].append(call_record) + + def _extract_response_text(self, response: Dict) -> str: + """Extract response text from API response""" + if not response or 'choices' not in response: + return "" + + message = response['choices'][0].get('message', {}) + content = (message.get('content') or '').strip() + + # Fallback for thinking models + if not content: + content = message.get('reasoning_content', '').strip() + + return content + + def _initialize_file(self): + """Initialize JSON file with metadata header""" + initial_data = { + "experiment_metadata": { + "session_id": self.session_id, + **self.experiment_metadata + }, + "queries": [], + "experiment_summary": {} + } + with open(self.output_file, 'w', encoding='utf-8') as f: + json.dump(initial_data, f, indent=2, ensure_ascii=False) + + def _append_query_to_file(self, query_data: Dict): + """Append a completed query to the JSON file""" + if not self.output_file: + return + + if not os.path.exists(self.output_file): + self._initialize_file() + + # Read current file + with open(self.output_file, 'r', encoding='utf-8') as f: + data = json.load(f) + + # Append query + data['queries'].append(query_data) + + # Update experiment summary + data['experiment_summary'] = self._calculate_experiment_summary(data['queries']) + + # Write back + with open(self.output_file, 'w', encoding='utf-8') as f: + json.dump(data, f, indent=2, ensure_ascii=False) + + def _calculate_experiment_summary(self, queries: List[Dict]) -> Dict: + """Calculate aggregate statistics across all queries""" + all_calls = [] + for q in queries: + all_calls.extend(q["llm_calls"]) + + if not all_calls: + return {} + + experiment_summary = { + "total_queries": len(queries), + "total_llm_calls": len(all_calls), + "total_input_tokens": sum(c["metrics"]["isl"] for c in all_calls), + "total_output_tokens": sum(c["metrics"]["osl"] for c in all_calls), + "total_tokens": sum(c["metrics"]["total_tokens"] for c in all_calls), + "total_latency_ms": round(sum(c["metrics"]["latency_ms"] for c in all_calls), 2), + "average_tokens_per_second": round(sum(c["metrics"]["tokens_per_second"] for c in all_calls) / len(all_calls), 2) if all_calls else 0, + "average_hops_per_query": round(sum(q["summary"]["total_hops"] for q in queries) / len(queries), 2) if queries else 0, + "components_used": list(set(c["component"] for c in all_calls)) + } + + # Add retrieval/answer metrics if available + queries_with_retrieval = [q for q in queries if "retrieval_results" in q] + if queries_with_retrieval: + experiment_summary["retrieval_metrics"] = { + "average_precision": round(sum(q["retrieval_results"].get("precision", 0) for q in queries_with_retrieval) / len(queries_with_retrieval), 4), + "average_recall": round(sum(q["retrieval_results"].get("recall", 0) for q in queries_with_retrieval) / len(queries_with_retrieval), 4), + "average_f1": round(sum(q["retrieval_results"].get("f1", 0) for q in queries_with_retrieval) / len(queries_with_retrieval), 4), + } + + queries_with_answers = [q for q in queries if "answer_results" in q] + if queries_with_answers: + correct_count = sum(1 for q in queries_with_answers if q["answer_results"].get("judge_score", 0) >= 4) + experiment_summary["answer_metrics"] = { + "average_judge_score": round(sum(q["answer_results"].get("judge_score", 0) for q in queries_with_answers) / len(queries_with_answers), 2), + "queries_correct": correct_count, + "queries_incorrect": len(queries_with_answers) - correct_count, + "accuracy": round(correct_count / len(queries_with_answers), 4) if queries_with_answers else 0 + } + + return experiment_summary + + def end_query(self, retrieval_results: Dict = None, answer_results: Dict = None): + """Finish logging current query, compute summary, and write to file""" + if self.current_query: + self.current_query["timestamp_end"] = datetime.utcnow().isoformat() + "Z" + + # Calculate summary + llm_calls = self.current_query["llm_calls"] + hop_counts = [c["hop_count"] for c in llm_calls if c["hop_count"] is not None] + + self.current_query["summary"] = { + "total_llm_calls": len(llm_calls), + "total_hops": max(hop_counts) if hop_counts else 0, + "total_input_tokens": sum(c["metrics"]["isl"] for c in llm_calls), + "total_output_tokens": sum(c["metrics"]["osl"] for c in llm_calls), + "total_tokens": sum(c["metrics"]["total_tokens"] for c in llm_calls), + "total_latency_ms": round(sum(c["metrics"]["latency_ms"] for c in llm_calls), 2), + "average_tokens_per_second": round(sum(c["metrics"]["tokens_per_second"] for c in llm_calls) / len(llm_calls), 2) if llm_calls else 0, + "components_used": list(set(c["component"] for c in llm_calls)) + } + + if retrieval_results: + self.current_query["retrieval_results"] = retrieval_results + if answer_results: + self.current_query["answer_results"] = answer_results + + with self._lock: + self.queries.append(self.current_query) + + # Write to file immediately after completing query + if self.output_file: + self._append_query_to_file(self.current_query) + + self.current_query = None + + def save(self, output_file: str = None, experiment_metadata: Dict[str, Any] = None): + """Save all logs to JSON file (legacy method for backward compatibility). + + Note: If logger was initialized with output_file, logs are already written + incrementally. This method can be used to write to a different file or + when using the old non-incremental mode. + """ + # Use provided file or fall back to instance file + target_file = output_file or self.output_file + + if not target_file: + print("Warning: No output file specified, logs not saved") + return + + # Calculate experiment summary + experiment_summary = self._calculate_experiment_summary(self.queries) + + # Use provided metadata or instance metadata + metadata = experiment_metadata or self.experiment_metadata + + output = { + "experiment_metadata": { + "session_id": self.session_id, + **metadata + }, + "queries": self.queries, + "experiment_summary": experiment_summary + } + + with open(target_file, 'w', encoding='utf-8') as f: + json.dump(output, f, indent=2, ensure_ascii=False) + + print(f"\n{'='*80}") + print(f"LLM logs saved to: {target_file}") + print(f"Total queries: {len(self.queries)}") + if experiment_summary: + print(f"Total LLM calls: {experiment_summary.get('total_llm_calls', 0)}") + print(f"Total tokens: {experiment_summary.get('total_tokens', 0):,} (input: {experiment_summary.get('total_input_tokens', 0):,}, output: {experiment_summary.get('total_output_tokens', 0):,})") + print(f"Total latency: {experiment_summary.get('total_latency_ms', 0)/1000:.2f}s") + print(f"{'='*80}\n") diff --git a/e2e/logs_result.json b/e2e/logs_result.json new file mode 100644 index 0000000000..1f069ce6d1 --- /dev/null +++ b/e2e/logs_result.json @@ -0,0 +1,19926 @@ +{ + "experiment_metadata": { + "session_id": "283bc5f9-8ee0-4a94-b328-abd6b7ce17a0", + "experiment_name": "multi_shot_vector_html_hnsw_len768_ov32_word_n50", + "timestamp_start": "2026-05-16T02:27:12.071352", + "timestamp_end": "2026-05-16T03:37:12.750558", + "retrieval_method": "vector", + "retrieval_mode": "multi_shot", + "max_iterations": 5, + "max_sub_queries": 3, + "top_k_retriever": 15, + "rerank_per_subquery": "top_k_retriever / num_sub_queries (= 15 / 3 = 5)", + "chunk_size": 768, + "device": "cpu", + "grader_model": "openai/gpt-oss-20b", + "sufficiency_checker_model": "openai/gpt-oss-120b", + "query_model": "openai/gpt-oss-120b", + "answer_generator_model": "openai/gpt-oss-120b", + "total_queries": 50 + }, + "queries": [ + { + "query_id": "0", + "original_query": "If my future wife has the same first name as the 15th first lady of the United States' mother and her surname is the same as the second assassinated president's mother's maiden name, what is my future wife's name? ", + "timestamp_start": "2026-05-16T02:27:12.084156Z", + "llm_calls": [ + { + "call_id": "4010490c-e7a0-4c0f-8245-9de50111177a", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:27:15.739213Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: If my future wife has the same first name as the 15th first lady of the United States' mother and her surname is the same as the second assassinated president's mother's maiden name, what is my future wife's name? \n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Harriet Lane mother\",\n \"James A. Garfield mother\",\n \"James A. Garfield mother maiden name\"\n ],\n \"feedback\": \"We need the first name of Harriet Lane's mother and the maiden surname of James A. Garfield's mother to determine the future wife's full name.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 493, + "osl": 722, + "total_tokens": 1215, + "latency_ms": 3654.86, + "tokens_per_second": 197.55 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "12fe800e-b5fa-4636-9151-5bdc397fe5ec", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:27:25.490229Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: If my future wife has the same first name as the 15th first lady of the United States' mother and her surname is the same as the second assassinated president's mother's maiden name, what is my future wife's name? \n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] f the first St. Albans School building, laid for what the invitation referred to as \"The Lane Johnston Choir School for Boys of the Washington Cathedral\". Legacy Lane left bequests in her will that established a children's hospital and a boys' school, and she donated her collection of artwork to the Smithsonian. Several Navy and Coast Guard ships have been named in her honor. Her birthplace, the Lane House , was listed on the National Register of Historic Places in 1972. Hospital and school She dedicated $400,000 (equivalent to $14,300,000 in 2025) to establish the Harriet Lane Home for Invalid Children at the Johns Hopkins Hospital in Baltimore , Maryland as a memorial to two sons who had died in childhood. In October 1912 the Harriet Lane Home officially o\n\n[NEW 2] rfield's ancestor Edward Garfield migrated from Hillmorton , Warwickshire , England, to Massachusetts around 1630. James's father Abraham was born in Worcester, New York , and came to Ohio to woo his childhood sweetheart, Mehitabel Ballou, only to find her married. He instead wed her sister Eliza, who was born in New Hampshire. James was named after an earlier son of Eliza and Abram who had died in infancy. In early 1833, Abram and Eliza Garfield joined a Stone-Campbell church, a decision that influenced their youngest son's life. Abram died later that year, and James was raised in poverty in a household led by his strong-willed mother. He was her favorite child and the two remained close for the rest of his life. Eliza remarried in 1842, but soon left her s\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 2 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 626, + "osl": 1565, + "total_tokens": 2191, + "latency_ms": 9122.91, + "tokens_per_second": 171.55 + }, + "context": { + "num_documents_evaluated": 2, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "3c75aa3b-2c5d-4908-bb42-ad7d32d99d9f", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:27:28.854476Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: If my future wife has the same first name as the 15th first lady of the United States' mother and her surname is the same as the second assassinated president's mother's maiden name, what is my future wife's name? \n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: - Query: 'Harriet Lane mother' → 1 docs\n- Query: 'James A. Garfield mother' → 1 docs\n- Query: 'James A. Garfield mother maiden name' → 0 docs\n\nFEEDBACK: We need the first name of Harriet Lane's mother and the maiden surname of James A. Garfield's mother to determine the future wife's full name.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Harriet Lane mother name\",\n \"James A. Garfield mother name\",\n \"James A. Garfield mother maiden name\"\n ],\n \"feedback\": \"Need Harriet Lane's mother's first name and James A. Garfield's mother's maiden surname to construct future wife's full name.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 492, + "osl": 71, + "total_tokens": 563, + "latency_ms": 3363.4, + "tokens_per_second": 21.11 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "4b6c96d3-8ec8-43c6-8336-c4a1eb8acb9b", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T02:27:32.768900Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: If my future wife has the same first name as the 15th first lady of the United States' mother and her surname is the same as the second assassinated president's mother's maiden name, what is my future wife's name? \n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: - Query: 'Harriet Lane mother' → 1 docs\n- Query: 'James A. Garfield mother' → 1 docs\n- Query: 'James A. Garfield mother maiden name' → 0 docs\n- Query: 'Harriet Lane mother name' → 0 docs\n- Query: 'James A. Garfield mother name' → 0 docs\n- Query: 'James A. Garfield mother maiden name' → 0 docs\n\nFEEDBACK: We need the first name of Harriet Lane's mother and the maiden surname of James A. Garfield's mother to determine the future wife's full name.\nNeed Harriet Lane's mother's first name and James A. Garfield's mother's maiden surname to construct future wife's full name.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Harriet Lane biography\",\n \"Harriet Lane mother Sally\",\n \"Lucretia Raine Garfield\"\n ],\n \"feedback\": \"Need Harriet Lane's mother's first name (likely Sally) and James A. Garfield's mother's maiden surname (Raine). The above broader queries should surface the relevant family details.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 616, + "osl": 886, + "total_tokens": 1502, + "latency_ms": 3404.3, + "tokens_per_second": 260.26 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "1be56fba-83a2-4613-9ed4-120abd5fa310", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T02:27:37.637367Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: If my future wife has the same first name as the 15th first lady of the United States' mother and her surname is the same as the second assassinated president's mother's maiden name, what is my future wife's name? \n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] ernity\". ^ Sally Heming's children recorded at Monticello included: \"Harriet (born 1795; died in infancy); Beverly (born 1798); an unnamed daughter (born 1799; died in infancy); Harriet (born 1801); Madison (born 1805); and Eston (born 1808)\". ^ Annette Gordon-Reed notes that it would have been legally challenging to free Sally Hemings, due to Virginia laws mandating the support of older slaves and requiring special permission for freed slaves to remain within the state. References ^ ^ ^ a b ^ ^ ^ ^ ^ ^ a b c ^ Tucker, 1837 , v. 1, p. 18. ^ a b Malone, 1948 , pp. 5–6. ^ Brodie, 1974 , pp. 33–34. ^ [ page needed ] ^ Tucker, 1837 , v. 1, p. 19. ^ a b Bowers, 1945 , pp. 12–13. ^ Peterson, 1970 , pp. 7–9. ^ Bowers, 1945 , p. 13 ^ Meacham, 2012 , p. 36 ^ Bowers,\n\n[NEW 2] y Florence Harding\n\n[NEW 3] igail Fillmore March 13, 1798 – March 30, 1853 (aged 55) July 9, 1850 – March 4, 1853 52 years, 118 days Millard Fillmore m. February 5, 1826 14 Jane Pierce March 12, 1806 – December 2, 1863 (aged 57) March 4, 1853 – March 4, 1857 46 years, 357 days Franklin Pierce m. November 19, 1834 15 Harriet Lane May 9, 1830 – July 3, 1903 (aged 73) March 4, 1857 – March 4, 1861 26 years, 299 days James Buchanan Uncle 16 Mary Lincoln December 13, 1818 – July 16, 1882 (aged 63) March 4, 1861 – April 15, 1865 42 years, 81 days Abraham Lincoln m. November 4, 1842 17 Eliza Johnson October 4, 1810 – January 15, 1876 (aged 65) April 15, 1865 – March 4, 1869 54 years, 193 days Andrew Johnson m. May 17, 1827 18 Julia Grant January 26, 1826 – December 14, 1902 (aged 76) March 4,\n\n[NEW 4] mate young son and raised him in their household. They had two children together. Their son, Francis Folger Franklin , was born in October 1732 and died of smallpox in 1736. Their daughter, Sarah \"Sally\" Franklin , was born in 1743 and eventually married Richard Bache . Deborah's fear of the sea meant that she never accompanied Franklin on any of his extended trips to Europe; another possible reason why they spent much time apart is that he may have blamed her for possibly preventing their son Francis from being inoculated against the disease that subsequently killed him. Deborah wrote to him in November 1769, saying she was ill due to \"dissatisfied distress\" from his prolonged absence, but he did not return until his business was done. Deborah Read Franklin\n\n[NEW 5] 362 9.650 73 76.836 55 Ildikó Balog 9.662 9.750 64 9.600 9.225 78 9.400 8.712 87 9.550 9.725 48 75.625 81 Krisztina Molnár 0.000 9.887 90 9.662 9.837 46 9.737 9.262 55 9.762 9.712 33 67.859 89 7 Australia 97.160 10 98.134 7 95.860 6 96.348 10 387.502 Lisa Read 9.812 9.437 80 9.850 9.862 21 9.787 9.675 24 9.762 9.750 31 77.935 33 Monique Allen 9.737 9.750 49 9.862 9.787 29 9.775 9.700 23 9.675 9.262 79 77.548 39 Kylie Shadbolt 9.762 9.712 51 9.737 9.812 43 9.712 9.187 63 9.712 9.562 50 77.196 46 Jane Warrilow 9.625 9.700 76 9.775 9.712 47 9.587 9.450 51 9.650 9.550 58 77.049 51 Julie-Anne Monico 9.675 9.637 77 9.812 9.862 27 9.487 9.450 58 9.725 8.712 87 76.360 68 Brooke Gysen 9.675 9.700 71 9.537 9.775 54 9.537 8.812 84 9.575 9.700 48 76.311 69 8 France 97.3\n\n[NEW 6] ey , several people Anne Stone , several people Anne Stuart , several people Anne Sutherland , several people Anne Thompson , several people Anne Turner , several people Anne Twomey , several people Anne Vaughan , several people Anne Villeneuve , several people Anne Walker , several people Anne Ward , several people Anne Warner , several people Anne Wentworth , several people Anne Wheaton , several people Anne Wilson , several people Anne Winters , several people Anne Woods , several people Anne Wright , several people Anne Young , several people Ann Ann Alexander , several people Ann Barry , several people Ann Bartholomew , several people Ann Bishop , several people Ann Bowling , several people Ann Bradshaw , several people Ann Burns , several people Ann Bu\n\n[NEW 7] nd Ray Moyer Les Girls William A. Horning and Gene Allen Edwin B. Willis and Richard Pefferle Pal Joey Walter Holscher William Kiernan and Louis Diage Raintree County William A. Horning and Urie McCleary Edwin B. Willis and Hugh Hunt 1958 (31st) Gigi William A. Horning ( posthumous award ) and E. Preston Ames Henry Grace and F. Keogh Gleason Auntie Mame Malcolm Bert George James Hopkins Bell, Book and Candle Cary Odell Louis Diage A Certain Smile Lyle R. Wheeler and John DeCuir Walter M. Scott and Paul S. Fox Vertigo Hal Pereira and Henry Bumstead Samuel M. Comer and Frank McKelvy 1959 (32nd) Black-and-White The Diary of Anne Frank Lyle R. Wheeler and George Davis Walter M. Scott and Stuart A. Reiss Career Hal Pereira and Walter H. Tyler Samuel M. Comer and\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 7 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 1, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1844, + "osl": 1249, + "total_tokens": 3093, + "latency_ms": 4275.94, + "tokens_per_second": 292.1 + }, + "context": { + "num_documents_evaluated": 7, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "0e92226d-0125-4368-a8bb-bdd0c20b1bbd", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T02:27:42.848362Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: If my future wife has the same first name as the 15th first lady of the United States' mother and her surname is the same as the second assassinated president's mother's maiden name, what is my future wife's name? \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] igail Fillmore March 13, 1798 – March 30, 1853 (aged 55) July 9, 1850 – March 4, 1853 52 years, 118 days Millard Fillmore m. February 5, 1826 14 Jane Pierce March 12, 1806 – December 2, 1863 (aged 57) March 4, 1853 – March 4, 1857 46 years, 357 days Franklin Pierce m. November 19, 1834 15 Harriet Lane May 9, 1830 – July 3, 1903 (aged 73) March 4, 1857 – March 4, 1861 26 years, 299 days James Buchanan Uncle 16 Mary Lincoln December 13, 1818 – July 16, 1882 (aged 63) March 4, 1861 – April 15, 1865 42 years, 81 days Abraham Lincoln m. November 4, 1842 17 Eliza Johnson October 4, 1810 – January 15, 1876 (aged 65) April 15, 1865 – March 4, 1869 54 years, 193 days Andrew Johnson m. May 17, 1827 18 Julia Grant January 26, 1826 – December 14, 1902 (aged 76) March 4,\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents list the 15th First Lady as Harriet Lane, giving her name but not her mother's first name, which is required for the future wife's first name. They also do not provide information about the second assassinated president (James A. Garfield) or his mother's maiden name, needed for the future wife's surname. Therefore, the necessary facts are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 575, + "osl": 94, + "total_tokens": 669, + "latency_ms": 5210.34, + "tokens_per_second": 18.04 + }, + "context": { + "kept_docs_count": 1, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "edbd4c78-a1f5-4a03-bff7-d08d366da34d", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T02:27:56.232669Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: If my future wife has the same first name as the 15th first lady of the United States' mother and her surname is the same as the second assassinated president's mother's maiden name, what is my future wife's name? \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] igail Fillmore March 13, 1798 – March 30, 1853 (aged 55) July 9, 1850 – March 4, 1853 52 years, 118 days Millard Fillmore m. February 5, 1826 14 Jane Pierce March 12, 1806 – December 2, 1863 (aged 57) March 4, 1853 – March 4, 1857 46 years, 357 days Franklin Pierce m. November 19, 1834 15 Harriet Lane May 9, 1830 – July 3, 1903 (aged 73) March 4, 1857 – March 4, 1861 26 years, 299 days James Buchanan Uncle 16 Mary Lincoln December 13, 1818 – July 16, 1882 (aged 63) March 4, 1861 – April 15, 1865 42 years, 81 days Abraham Lincoln m. November 4, 1842 17 Eliza Johnson October 4, 1810 – January 15, 1876 (aged 65) April 15, 1865 – March 4, 1869 54 years, 193 days Andrew Johnson m. May 17, 1827 18 Julia Grant January 26, 1826 – December 14, 1902 (aged 76) March 4,\n\n\nSEARCH HISTORY: - Query: 'Harriet Lane mother' → 1 docs\n- Query: 'James A. Garfield mother' → 1 docs\n- Query: 'James A. Garfield mother maiden name' → 0 docs\n- Query: 'Harriet Lane mother name' → 0 docs\n- Query: 'James A. Garfield mother name' → 0 docs\n- Query: 'James A. Garfield mother maiden name' → 0 docs\n- Query: 'Harriet Lane biography' → 0 docs\n- Query: 'Harriet Lane mother Sally' → 4 docs\n- Query: 'Lucretia Raine Garfield' → 3 docs\n\nFEEDBACK: We need the first name of Harriet Lane's mother and the maiden surname of James A. Garfield's mother to determine the future wife's full name.\nNeed Harriet Lane's mother's first name and James A. Garfield's mother's maiden surname to construct future wife's full name.\nNeed Harriet Lane's mother's first name (likely Sally) and James A. Garfield's mother's maiden surname (Raine). The above broader queries should surface the relevant family details.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Harriet Lane\",\n \"James Garfield mother\",\n \"Harriet Lane mother\"\n ],\n \"feedback\": \"We need to find the first name of Harriet Lane's mother (the mother of the 15th First Lady) and the maiden surname of James A. Garfield's mother (the second assassinated president).\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 994, + "osl": 545, + "total_tokens": 1539, + "latency_ms": 13383.68, + "tokens_per_second": 40.72 + }, + "context": { + "kept_docs_count": 1, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "854cffb3-d23d-43dc-8fc2-1d802d5af0cf", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T02:28:00.735421Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: If my future wife has the same first name as the 15th first lady of the United States' mother and her surname is the same as the second assassinated president's mother's maiden name, what is my future wife's name? \n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] tation on his way to his alma mater , Williams College , where he was scheduled to deliver a speech. Garfield was accompanied by two of his sons, James and Harry , and Secretary of State James G. Blaine . Secretary of War Robert Todd Lincoln waited at the station to see the president off. Garfield had no bodyguard or security detail; with the exception of Abraham Lincoln during the Civil War , early U. S. presidents never used any guards. As President Garfield entered the waiting room of the station, Guiteau stepped forward and pulled the trigger from behind at point-blank range. \"My God, what is that?!\" Garfield cried out, flinging up his arms. Guiteau fired again and Garfield collapsed. One bullet grazed Garfield's shoulder; the other hit him in the back,\n\n\nKEPT DOCUMENTS (context only):\n[1 documents already kept as relevant]\n[KEPT 1] igail Fillmore March 13, 1798 – March 30, 1853 (aged 55) July 9, 1850 – March 4, 1853 52 years, 118 days Millard Fillmore m. February 5, 1826 14 Jane Pierce March 12, 1806 – December 2, 1863 (aged 57) March 4, 1853 – March 4, 1857 46 years, 357 days Franklin Pierce m. November 19, 1834 15 Harriet La...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 583, + "osl": 1296, + "total_tokens": 1879, + "latency_ms": 3989.43, + "tokens_per_second": 324.86 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 1 + } + }, + { + "call_id": "8fa50979-6706-4d96-b13d-eb65dbb3fa29", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T02:28:42.509066Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: If my future wife has the same first name as the 15th first lady of the United States' mother and her surname is the same as the second assassinated president's mother's maiden name, what is my future wife's name? \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] igail Fillmore March 13, 1798 – March 30, 1853 (aged 55) July 9, 1850 – March 4, 1853 52 years, 118 days Millard Fillmore m. February 5, 1826 14 Jane Pierce March 12, 1806 – December 2, 1863 (aged 57) March 4, 1853 – March 4, 1857 46 years, 357 days Franklin Pierce m. November 19, 1834 15 Harriet Lane May 9, 1830 – July 3, 1903 (aged 73) March 4, 1857 – March 4, 1861 26 years, 299 days James Buchanan Uncle 16 Mary Lincoln December 13, 1818 – July 16, 1882 (aged 63) March 4, 1861 – April 15, 1865 42 years, 81 days Abraham Lincoln m. November 4, 1842 17 Eliza Johnson October 4, 1810 – January 15, 1876 (aged 65) April 15, 1865 – March 4, 1869 54 years, 193 days Andrew Johnson m. May 17, 1827 18 Julia Grant January 26, 1826 – December 14, 1902 (aged 76) March 4,\n\n[DOC 2] tation on his way to his alma mater , Williams College , where he was scheduled to deliver a speech. Garfield was accompanied by two of his sons, James and Harry , and Secretary of State James G. Blaine . Secretary of War Robert Todd Lincoln waited at the station to see the president off. Garfield had no bodyguard or security detail; with the exception of Abraham Lincoln during the Civil War , early U. S. presidents never used any guards. As President Garfield entered the waiting room of the station, Guiteau stepped forward and pulled the trigger from behind at point-blank range. \"My God, what is that?!\" Garfield cried out, flinging up his arms. Guiteau fired again and Garfield collapsed. One bullet grazed Garfield's shoulder; the other hit him in the back,\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide the name of the 15th First Lady (Harriet Lane) and identify James A. Garfield as the second assassinated president, but they do not contain the first name of Harriet Lane's mother nor the maiden name of James A. Garfield's mother. Without those two specific facts, the future wife's full name cannot be determined.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 788, + "osl": 1411, + "total_tokens": 2199, + "latency_ms": 41772.4, + "tokens_per_second": 33.78 + }, + "context": { + "kept_docs_count": 2, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "d5ceb2bb-0b6f-4783-ad65-814c07836dfc", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T02:28:46.082121Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: If my future wife has the same first name as the 15th first lady of the United States' mother and her surname is the same as the second assassinated president's mother's maiden name, what is my future wife's name? \n\nDOCUMENTS:\n\n[DOC 1] igail Fillmore March 13, 1798 – March 30, 1853 (aged 55) July 9, 1850 – March 4, 1853 52 years, 118 days Millard Fillmore m. February 5, 1826 14 Jane Pierce March 12, 1806 – December 2, 1863 (aged 57) March 4, 1853 – March 4, 1857 46 years, 357 days Franklin Pierce m. November 19, 1834 15 Harriet Lane May 9, 1830 – July 3, 1903 (aged 73) March 4, 1857 – March 4, 1861 26 years, 299 days James Buchanan Uncle 16 Mary Lincoln December 13, 1818 – July 16, 1882 (aged 63) March 4, 1861 – April 15, 1865 42 years, 81 days Abraham Lincoln m. November 4, 1842 17 Eliza Johnson October 4, 1810 – January 15, 1876 (aged 65) April 15, 1865 – March 4, 1869 54 years, 193 days Andrew Johnson m. May 17, 1827 18 Julia Grant January 26, 1826 – December 14, 1902 (aged 76) March 4,\n\n[DOC 2] tation on his way to his alma mater , Williams College , where he was scheduled to deliver a speech. Garfield was accompanied by two of his sons, James and Harry , and Secretary of State James G. Blaine . Secretary of War Robert Todd Lincoln waited at the station to see the president off. Garfield had no bodyguard or security detail; with the exception of Abraham Lincoln during the Civil War , early U. S. presidents never used any guards. As President Garfield entered the waiting room of the station, Guiteau stepped forward and pulled the trigger from behind at point-blank range. \"My God, what is that?!\" Garfield cried out, flinging up his arms. Guiteau fired again and Garfield collapsed. One bullet grazed Garfield's shoulder; the other hit him in the back,\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 605, + "osl": 1, + "total_tokens": 606, + "latency_ms": 3572.16, + "tokens_per_second": 0.28 + }, + "context": { + "num_documents_used": 2, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:28:46.083036Z", + "summary": { + "total_llm_calls": 10, + "total_hops": 5, + "total_input_tokens": 7616, + "total_output_tokens": 7840, + "total_tokens": 15456, + "total_latency_ms": 91749.42, + "average_tokens_per_second": 136.03, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/President_of_the_United_States", + "https://en.wikipedia.org/wiki/James_Buchanan", + "https://en.wikipedia.org/wiki/Harriet_Lane", + "https://en.wikipedia.org/wiki/List_of_presidents_of_the_United_States_who_died_in_office", + "https://en.wikipedia.org/wiki/James_A._Garfield" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "Jane Ballou" + } + }, + { + "query_id": "1", + "original_query": "Imagine there is a building called Bronte tower whose height in feet is the same number as the dewey decimal classification for the Charlotte Bronte book that was published in 1847. Where would this building rank among tallest buildings in New York City, as of August 2024?", + "timestamp_start": "2026-05-16T02:28:46.084479Z", + "llm_calls": [ + { + "call_id": "743027c8-e724-4622-a472-9bf13c423f48", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:28:50.674627Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: Imagine there is a building called Bronte tower whose height in feet is the same number as the dewey decimal classification for the Charlotte Bronte book that was published in 1847. Where would this building rank among tallest buildings in New York City, as of August 2024?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Bronte Tower height\",\n \"Jane Eyre Dewey Decimal\",\n \"tallest buildings New York 2024\"\n ],\n \"feedback\": \"We need the building's height (Bronte Tower) and the Dewey Decimal for Charlotte Bronte's 1847 book (Jane Eyre). Then we must compare that height to the rankings in a 2024 list of NYC's tallest buildings.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 432, + "osl": 92, + "total_tokens": 524, + "latency_ms": 4590.0, + "tokens_per_second": 20.04 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "975cc4ff-77b3-455f-b677-9b4af80d2338", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:29:00.797756Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: Imagine there is a building called Bronte tower whose height in feet is the same number as the dewey decimal classification for the Charlotte Bronte book that was published in 1847. Where would this building rank among tallest buildings in New York City, as of August 2024?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] and replaced by a shorter tower. The city's buildings of at least 100 m (328 ft) in height are: Rank Name Completed Image Architect Use Height to roof Floors Location metres feet 1 22 Bishopsgate 2020 PLP Architecture Office 278 912 62 22 Bishopsgate 2 Heron Tower 2010 Kohn Pedersen Fox Office 230 754 46 110 Bishopsgate 3 Leadenhall Building 2014 Rogers Stirk Harbour + Partners Office 225 737 48 122 Leadenhall Street 4 8 Bishopsgate 2022 WilkinsonEyre Office 204 669 51 8 Bishopsgate 5 The Scalpel 2018 Kohn Pedersen Fox Office 190 630 39 52 Lime Street 6 Tower 42 1980 R Siefert & Partners Office 183 600 47 25 Old Broad Street 7 30 St Mary Axe 2003 Foster and Partners Office 180 590 40 30 St Mary Axe 8 100 Bishopsgate 2019 Allies and Morrison Office 172 563 4\n\n[NEW 2] ground, the tower leans slightly to the north-west by roughly 230 mm (9.1 in) over 55 m height, giving an inclination of approximately 1 ⁄ 240 . This includes a planned maximum of 22 mm increased tilt due to tunnelling for the Jubilee Line Extension . In the 1990s thousands of tons of concrete were pumped into the ground underneath the tower to stabilise it during construction of the Westminster section of the Jubilee line of the London Underground . It leans by about 500 mm (20 in) at the finial . Experts believe the leaning will not be a problem for another 4,000 to 10,000 years. The Palace of Westminster from across the River Thames . Elizabeth Tower is on the right. Ayrton Light Ayrton Light, above the green-lit belfry A new feature was added in 1873 by\n\n[NEW 3] s, rather than on tall structures. The highest is the Tianmen Mountain Glass Skywalk , at 1,430 meters (4,690 feet). See also List of tallest buildings List of tallest structures History of the world's tallest buildings Tallest structures by category References ^ ^ ^ ^ ^ ^ Haughton, Brian (2007), Hidden History: Lost Civilizations, Secret Knowledge, and Ancient Mysteries , p. 167 ^ Michael Woods, Mary B. Woods (2009), Seven Wonders of the Ancient World , p. 41 ^ ^ Darwin Porter, Danforth Prince (2010), Frommer's England 2010 , p. 588 ^ Mary Jane Taber (1905), The cathedrals of England: an account of some of their distinguishing characteristics , p. 100 ^ ^ ^ a b ^ ^ a b c ^ a b c d ^ ^ Binders, George (August 2006). 101 of the World's Tallest Buildings. p. 1\n\n[NEW 4] houses, rowhouses , and apartment buildings . Many domestic buildings in Centretown are clad in red brick, with trim in wood, stone, or metal; variations are common, depending on the cultural heritage of the neighbourhoods and the time they were built. The skyline has been controlled by building height restrictions, originally implemented to keep Parliament Hill and the Peace Tower at 92.2 metres (302 ft) visible from most parts of the city, and prevented construction of buildings exceeding 150 feet through at least the 1950s. Today, several buildings are slightly taller than the Peace Tower, with the tallest being the Claridge Icon at 143 metres (469 ft). Many federal buildings in the National Capital Region are managed by Public Works Canada , which leads\n\n[NEW 5] 981, pp. 123–129. ^ ^ External links Jane Eyre at Wikipedia's sister projects Media from Commons Quotations from Wikiquote Texts from Wikisource Data from Wikidata Jane Eyre at Standard Ebooks Jane Eyre public domain audiobook at LibriVox Jane Eyre at the Internet Archive Jane Eyre at the British Library Archived 12 February 2015 at the Wayback Machine\n\n[NEW 6] 5-7 . Le Faye, Deirdre. Jane Austen: A Family Record . Second Edition. Cambridge: Cambridge University Press, 2004. ISBN 0-521-53417-8 . Le Faye, Deirdre. \"Letters\". Jane Austen in Context . Ed. Janet Todd. Cambridge: Cambridge University Press, 2005. ISBN 0-521-82644-6 . 33–40 Le Faye, \"Memoirs and Biographies\". Jane Austen in Context . Ed. Janet Todd. Cambridge: Cambridge University Press, 2005. ISBN 0-521-82644-6 . 51–58 Litz, A. Walton. Jane Austen: A Study of Her Development . New York: Oxford University Press, 1965. Litz, A. Walton. \"Chronology of Composition\". The Jane Austen Companion . Ed. J. David Grey. New York: Macmillan, 1986. ISBN 0-02-545540-0 . 47–62 Lodge, David. \"Jane Austen's Novels: Form and Structure\". The Jane Austen Companion . Ed. J.\n\n[NEW 7] 5-7 . Le Faye, Deirdre. Jane Austen: A Family Record . Second Edition. Cambridge: Cambridge University Press, 2004. ISBN 0-521-53417-8 . Le Faye, Deirdre. \"Letters\". Jane Austen in Context . Ed. Janet Todd. Cambridge: Cambridge University Press, 2005. ISBN 0-521-82644-6 . 33–40 Le Faye, \"Memoirs and Biographies\". Jane Austen in Context . Ed. Janet Todd. Cambridge: Cambridge University Press, 2005. ISBN 0-521-82644-6 . 51–58 Litz, A. Walton. Jane Austen: A Study of Her Development . New York: Oxford University Press, 1965. Litz, A. Walton. \"Chronology of Composition\". The Jane Austen Companion . Ed. J. David Grey. New York: Macmillan, 1986. ISBN 0-02-545540-0 . 47–62 Lodge, David. \"Jane Austen's Novels: Form and Structure\". The Jane Austen Companion . Ed. J.\n\n[NEW 8] Skyline of New York City Midtown Manhattan with the Empire State Building (center) and Lower Manhattan with One WTC (center-right) Tallest building One World Trade Center (2014) Tallest building height 1,776 ft (541 m) Major clusters Midtown Manhattan Lower Manhattan Downtown Brooklyn Long Island City First 150 m+ building Singer Building (1898) Number of tall buildings (2025) Taller than 100 m (328 ft) 884 Taller than 150 m (492 ft) 323+6 T/O Taller than 200 m (656 ft) 101+5 T/O Taller than 300 m (984 ft) 18+1 T/O Taller than 400 m (1,312 ft) 6 Number of tall buildings — feet Taller than 300 ft (91.4 m) 1,072 Midtown Manhattan in June 2024 looking north from the Empire State Building 's 102nd floor (1,224 feet or 373 meters) Lower Manhattan , viewed from Je\n\n[NEW 9] Office skyscraper in Manhattan, New York ‹ The template Infobox building is being considered for merging . › Empire State Building Empire State Building illuminated in 2021 Interactive map of the Empire State Building area Record height Tallest in the world from 1931 to 1970 Preceded by Chrysler Building Surpassed by World Trade Center General information Type Office building ; observation decks Architectural style Art Deco Location 350 Fifth Avenue Manhattan , New York, 10118 U. S. Coordinates 40°44′54′′N 73°59′08′′W  /  40.7483°N 73.9856°W  / 40.7483; -73.9856 Construction started March 17, 1930 ; 96 years ago ( 1930-03-17 ) Topped-out September 19, 1930 ; 95 years ago ( 1930-09-19 ) Completed April 11, 1931 ; 94 years ago ( 1931-04-11 ) Opened May 1, 1\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 9 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0, 0, 0, 0, 1, 1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2103, + "osl": 799, + "total_tokens": 2902, + "latency_ms": 9527.73, + "tokens_per_second": 83.86 + }, + "context": { + "num_documents_evaluated": 9, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "7c5ceef4-3563-4824-81ec-8a11b9cfdb8b", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:29:06.764578Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: Imagine there is a building called Bronte tower whose height in feet is the same number as the dewey decimal classification for the Charlotte Bronte book that was published in 1847. Where would this building rank among tallest buildings in New York City, as of August 2024?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Skyline of New York City Midtown Manhattan with the Empire State Building (center) and Lower Manhattan with One WTC (center-right) Tallest building One World Trade Center (2014) Tallest building height 1,776 ft (541 m) Major clusters Midtown Manhattan Lower Manhattan Downtown Brooklyn Long Island City First 150 m+ building Singer Building (1898) Number of tall buildings (2025) Taller than 100 m (328 ft) 884 Taller than 150 m (492 ft) 323+6 T/O Taller than 200 m (656 ft) 101+5 T/O Taller than 300 m (984 ft) 18+1 T/O Taller than 400 m (1,312 ft) 6 Number of tall buildings — feet Taller than 300 ft (91.4 m) 1,072 Midtown Manhattan in June 2024 looking north from the Empire State Building 's 102nd floor (1,224 feet or 373 meters) Lower Manhattan , viewed from Je\n\n[DOC 2] Office skyscraper in Manhattan, New York ‹ The template Infobox building is being considered for merging . › Empire State Building Empire State Building illuminated in 2021 Interactive map of the Empire State Building area Record height Tallest in the world from 1931 to 1970 Preceded by Chrysler Building Surpassed by World Trade Center General information Type Office building ; observation decks Architectural style Art Deco Location 350 Fifth Avenue Manhattan , New York, 10118 U. S. Coordinates 40°44′54′′N 73°59′08′′W  /  40.7483°N 73.9856°W  / 40.7483; -73.9856 Construction started March 17, 1930 ; 96 years ago ( 1930-03-17 ) Topped-out September 19, 1930 ; 95 years ago ( 1930-09-19 ) Completed April 11, 1931 ; 94 years ago ( 1931-04-11 ) Opened May 1, 1\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Missing: the Dewey Decimal classification for Charlotte Brontë's 1847 book (needed to determine the building's height) and a detailed ranking of New York City skyscrapers by height as of August 2024 (needed to place the building among them). The provided documents only give general tall‑building counts and some specific building heights, but do not contain the required classification or a comprehensive height‑ordered list.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 772, + "osl": 430, + "total_tokens": 1202, + "latency_ms": 5966.13, + "tokens_per_second": 72.07 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "0617aeab-b17b-497e-a11f-0c70c9eb8b96", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:29:19.169483Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: Imagine there is a building called Bronte tower whose height in feet is the same number as the dewey decimal classification for the Charlotte Bronte book that was published in 1847. Where would this building rank among tallest buildings in New York City, as of August 2024?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Skyline of New York City Midtown Manhattan with the Empire State Building (center) and Lower Manhattan with One WTC (center-right) Tallest building One World Trade Center (2014) Tallest building height 1,776 ft (541 m) Major clusters Midtown Manhattan Lower Manhattan Downtown Brooklyn Long Island City First 150 m+ building Singer Building (1898) Number of tall buildings (2025) Taller than 100 m (328 ft) 884 Taller than 150 m (492 ft) 323+6 T/O Taller than 200 m (656 ft) 101+5 T/O Taller than 300 m (984 ft) 18+1 T/O Taller than 400 m (1,312 ft) 6 Number of tall buildings — feet Taller than 300 ft (91.4 m) 1,072 Midtown Manhattan in June 2024 looking north from the Empire State Building 's 102nd floor (1,224 feet or 373 meters) Lower Manhattan , viewed from Je\n\n[DOC 2] Office skyscraper in Manhattan, New York ‹ The template Infobox building is being considered for merging . › Empire State Building Empire State Building illuminated in 2021 Interactive map of the Empire State Building area Record height Tallest in the world from 1931 to 1970 Preceded by Chrysler Building Surpassed by World Trade Center General information Type Office building ; observation decks Architectural style Art Deco Location 350 Fifth Avenue Manhattan , New York, 10118 U. S. Coordinates 40°44′54′′N 73°59′08′′W  /  40.7483°N 73.9856°W  / 40.7483; -73.9856 Construction started March 17, 1930 ; 96 years ago ( 1930-03-17 ) Topped-out September 19, 1930 ; 95 years ago ( 1930-09-19 ) Completed April 11, 1931 ; 94 years ago ( 1931-04-11 ) Opened May 1, 1\n\n\nSEARCH HISTORY: - Query: 'Bronte Tower height' → 4 docs\n- Query: 'Jane Eyre Dewey Decimal' → 3 docs\n- Query: 'tallest buildings New York 2024' → 2 docs\n\nFEEDBACK: We need the building's height (Bronte Tower) and the Dewey Decimal for Charlotte Bronte's 1847 book (Jane Eyre). Then we must compare that height to the rankings in a 2024 list of NYC's tallest buildings.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Bronte Tower building\",\n \"Jane Eyre Dewey Decimal number\",\n \"List of tallest buildings in New York City 2024\"\n ],\n \"feedback\": \"We still need the exact Dewey Decimal classification for the 1847 edition of Jane Eyre, the precise height of Bronte Tower, and a current ranked list of NYC skyscrapers to determine the building's rank.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1006, + "osl": 593, + "total_tokens": 1599, + "latency_ms": 12404.24, + "tokens_per_second": 47.81 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "a95082e2-676f-43ff-8983-bf3b9c856e56", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T02:29:23.142166Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: Imagine there is a building called Bronte tower whose height in feet is the same number as the dewey decimal classification for the Charlotte Bronte book that was published in 1847. Where would this building rank among tallest buildings in New York City, as of August 2024?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] 1. The Entrance. ^ Barker 1995 , p. 503 ^ Barker 1995 , pp. 539–542 ^ Barker 1995 , pp. 537–539 ^ Smith Kenyon 2002 , pp. 12–13 ^ Barker 1995 , pp. 90, 91, 533–534, 539–540, 653–654 ^ a b Barker 1995 , p. 654 ^ ^ ^ ^ Barker 1995 , p. 138 ^ Barker 1995 , pp. 135–136 ^ Barker 1995 , pp. 801–808 ^ Barker 1995 , pp. 119–127 ^ Barker 1995 , pp. 643–644 ^ The Brontës of Haworth , Brontë Parsonage Museum, section 3, The Dining Room , illustration 5. This pencil drawing by Samuel Lawrence, is visible on this page ^ Barker 1995 , pp. 621, 675–678 ^ Barker 1995 , p. 676 ^ Peters 1977 , p. 358 ^ Peters 1977 , pp. 358–363, 378–387, 358 ^ Barker 1995 , p. 758 ^ Peters 1977 , p. 400. ^ Barker 1995 , pp. 769–772 ^ Barker 1995 , pp. 651–653, 681, 726–728, 738–741 ^ ^ ^ ^ B\n\n[NEW 2] d gable roof Seaforth Armoury 1650 Burrard Street 1936 (completed) Classified - 1997 Register of the Government of Canada Heritage Buildings Vancouver, British Columbia designed by architects McCarter and Nairne, the massive, low-massed, asymmetrical, concrete Neo-Gothic style structure with a fortified appearance is as a Class A Heritage Building in downtown Vancouver housing The Seaforth Highlanders of Canada Confederation Building (Ottawa) , Wellington Street at Bank Street 1928-31 Ottawa, Ontario The Ore Building, Booth Street 1929 Ottawa, Ontario Custom House, Front Street West at Yonge Street 1930-31 Toronto, Ontario Department of Justice Building , Wellington Street 1935-7 Ottawa, Ontario Dominion Public Building, 457 Richmond Street 1935-1936 London,\n\n[NEW 3] ation Road and Station Rise. 18 North Street Postern Tower/Barkers Tower 19 Lendal Tower 20 The site of this tower is now the Lodge at the entrance to the Museum Gardens. 21 It no longer exists; it was demolished during the construction of St Leonards Place and is located where the De Gray Rooms are currently. 22 A demi-hexagonal interval tower overlooking gardens on Gillygate. 23 A demi-hexagonal interval tower overlooking gardens on Gillygate. 24 A demi-hexagonal interval tower overlooking gardens on Gillygate. 25 A semi-circular interval tower overlooking gardens on Gillygate. 26 A semi-circular interval tower overlooking gardens on Gillygate. A 20th-century replacement for the original 14th-century tower. 27 Robin Hood Tower Forms the northwestern corner\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] Skyline of New York City Midtown Manhattan with the Empire State Building (center) and Lower Manhattan with One WTC (center-right) Tallest building One World Trade Center (2014) Tallest building height 1,776 ft (541 m) Major clusters Midtown Manhattan Lower Manhattan Downtown Brooklyn Long Island Ci...\n[KEPT 2] Office skyscraper in Manhattan, New York ‹ The template Infobox building is being considered for merging . › Empire State Building Empire State Building illuminated in 2021 Interactive map of the Empire State Building area Record height Tallest in the world from 1931 to 1970 Preceded by Chrysler Bui...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 3 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1107, + "osl": 960, + "total_tokens": 2067, + "latency_ms": 3334.57, + "tokens_per_second": 287.89 + }, + "context": { + "num_documents_evaluated": 3, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "ad20f80e-09a6-47de-a037-dcd7564a2d19", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T02:29:24.803116Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: Imagine there is a building called Bronte tower whose height in feet is the same number as the dewey decimal classification for the Charlotte Bronte book that was published in 1847. Where would this building rank among tallest buildings in New York City, as of August 2024?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Skyline of New York City Midtown Manhattan with the Empire State Building (center) and Lower Manhattan with One WTC (center-right) Tallest building One World Trade Center (2014) Tallest building height 1,776 ft (541 m) Major clusters Midtown Manhattan Lower Manhattan Downtown Brooklyn Long Island City First 150 m+ building Singer Building (1898) Number of tall buildings (2025) Taller than 100 m (328 ft) 884 Taller than 150 m (492 ft) 323+6 T/O Taller than 200 m (656 ft) 101+5 T/O Taller than 300 m (984 ft) 18+1 T/O Taller than 400 m (1,312 ft) 6 Number of tall buildings — feet Taller than 300 ft (91.4 m) 1,072 Midtown Manhattan in June 2024 looking north from the Empire State Building 's 102nd floor (1,224 feet or 373 meters) Lower Manhattan , viewed from Je\n\n[DOC 2] Office skyscraper in Manhattan, New York ‹ The template Infobox building is being considered for merging . › Empire State Building Empire State Building illuminated in 2021 Interactive map of the Empire State Building area Record height Tallest in the world from 1931 to 1970 Preceded by Chrysler Building Surpassed by World Trade Center General information Type Office building ; observation decks Architectural style Art Deco Location 350 Fifth Avenue Manhattan , New York, 10118 U. S. Coordinates 40°44′54′′N 73°59′08′′W  /  40.7483°N 73.9856°W  / 40.7483; -73.9856 Construction started March 17, 1930 ; 96 years ago ( 1930-03-17 ) Topped-out September 19, 1930 ; 95 years ago ( 1930-09-19 ) Completed April 11, 1931 ; 94 years ago ( 1931-04-11 ) Opened May 1, 1\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Missing: the Dewey Decimal classification for the 1847 Charlotte Bronte book (needed to determine the building's height) and a comprehensive list of NYC building heights to rank the hypothetical Bronte tower among them. The provided documents only give some tall building statistics and details about the Empire State Building, but do not contain the required classification or sufficient height data for ranking.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 773, + "osl": 373, + "total_tokens": 1146, + "latency_ms": 1660.1, + "tokens_per_second": 224.68 + }, + "context": { + "kept_docs_count": 2, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "18bd624f-e0d7-414a-98d3-ee30529a64dd", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T02:29:28.158897Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: Imagine there is a building called Bronte tower whose height in feet is the same number as the dewey decimal classification for the Charlotte Bronte book that was published in 1847. Where would this building rank among tallest buildings in New York City, as of August 2024?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Skyline of New York City Midtown Manhattan with the Empire State Building (center) and Lower Manhattan with One WTC (center-right) Tallest building One World Trade Center (2014) Tallest building height 1,776 ft (541 m) Major clusters Midtown Manhattan Lower Manhattan Downtown Brooklyn Long Island City First 150 m+ building Singer Building (1898) Number of tall buildings (2025) Taller than 100 m (328 ft) 884 Taller than 150 m (492 ft) 323+6 T/O Taller than 200 m (656 ft) 101+5 T/O Taller than 300 m (984 ft) 18+1 T/O Taller than 400 m (1,312 ft) 6 Number of tall buildings — feet Taller than 300 ft (91.4 m) 1,072 Midtown Manhattan in June 2024 looking north from the Empire State Building 's 102nd floor (1,224 feet or 373 meters) Lower Manhattan , viewed from Je\n\n[DOC 2] Office skyscraper in Manhattan, New York ‹ The template Infobox building is being considered for merging . › Empire State Building Empire State Building illuminated in 2021 Interactive map of the Empire State Building area Record height Tallest in the world from 1931 to 1970 Preceded by Chrysler Building Surpassed by World Trade Center General information Type Office building ; observation decks Architectural style Art Deco Location 350 Fifth Avenue Manhattan , New York, 10118 U. S. Coordinates 40°44′54′′N 73°59′08′′W  /  40.7483°N 73.9856°W  / 40.7483; -73.9856 Construction started March 17, 1930 ; 96 years ago ( 1930-03-17 ) Topped-out September 19, 1930 ; 95 years ago ( 1930-09-19 ) Completed April 11, 1931 ; 94 years ago ( 1931-04-11 ) Opened May 1, 1\n\n\nSEARCH HISTORY: - Query: 'Bronte Tower height' → 4 docs\n- Query: 'Jane Eyre Dewey Decimal' → 3 docs\n- Query: 'tallest buildings New York 2024' → 2 docs\n- Query: 'Bronte Tower building' → 3 docs\n- Query: 'Jane Eyre Dewey Decimal number' → 0 docs\n- Query: 'List of tallest buildings in New York City 2024' → 0 docs\n\nFEEDBACK: We need the building's height (Bronte Tower) and the Dewey Decimal for Charlotte Bronte's 1847 book (Jane Eyre). Then we must compare that height to the rankings in a 2024 list of NYC's tallest buildings.\nWe still need the exact Dewey Decimal classification for the 1847 edition of Jane Eyre, the precise height of Bronte Tower, and a current ranked list of NYC skyscrapers to determine the building's rank.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Jane Eyre classification\",\n \"Bronte Tower Manhattan\",\n \"New York City skyscraper rankings 2024\"\n ],\n \"feedback\": \"Need: exact Dewey Decimal number for the 1847 edition of Jane Eyre; precise height of the building named Bronte Tower; a 2024 ranking of NYC skyscrapers to place that height within the list.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1068, + "osl": 86, + "total_tokens": 1154, + "latency_ms": 3355.19, + "tokens_per_second": 25.63 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "65d87778-cc6b-4c87-b7b6-3dd0ea66cf17", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T02:29:35.629097Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: Imagine there is a building called Bronte tower whose height in feet is the same number as the dewey decimal classification for the Charlotte Bronte book that was published in 1847. Where would this building rank among tallest buildings in New York City, as of August 2024?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Skyline of New York City Midtown Manhattan with the Empire State Building (center) and Lower Manhattan with One WTC (center-right) Tallest building One World Trade Center (2014) Tallest building height 1,776 ft (541 m) Major clusters Midtown Manhattan Lower Manhattan Downtown Brooklyn Long Island City First 150 m+ building Singer Building (1898) Number of tall buildings (2025) Taller than 100 m (328 ft) 884 Taller than 150 m (492 ft) 323+6 T/O Taller than 200 m (656 ft) 101+5 T/O Taller than 300 m (984 ft) 18+1 T/O Taller than 400 m (1,312 ft) 6 Number of tall buildings — feet Taller than 300 ft (91.4 m) 1,072 Midtown Manhattan in June 2024 looking north from the Empire State Building 's 102nd floor (1,224 feet or 373 meters) Lower Manhattan , viewed from Je\n\n[DOC 2] Office skyscraper in Manhattan, New York ‹ The template Infobox building is being considered for merging . › Empire State Building Empire State Building illuminated in 2021 Interactive map of the Empire State Building area Record height Tallest in the world from 1931 to 1970 Preceded by Chrysler Building Surpassed by World Trade Center General information Type Office building ; observation decks Architectural style Art Deco Location 350 Fifth Avenue Manhattan , New York, 10118 U. S. Coordinates 40°44′54′′N 73°59′08′′W  /  40.7483°N 73.9856°W  / 40.7483; -73.9856 Construction started March 17, 1930 ; 96 years ago ( 1930-03-17 ) Topped-out September 19, 1930 ; 95 years ago ( 1930-09-19 ) Completed April 11, 1931 ; 94 years ago ( 1931-04-11 ) Opened May 1, 1\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Missing: the Dewey Decimal classification number for the 1847 Charlotte Brontë book (e.g., Jane Eyre), the existence and height of a building named Bronte Tower, and a ranking list of NYC skyscrapers by height as of August 2024 to determine the building's rank. The kept documents only provide general information about NYC skyline and details about the Empire State Building, which do not cover these required facts.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 723, + "osl": 103, + "total_tokens": 826, + "latency_ms": 6912.15, + "tokens_per_second": 14.9 + }, + "context": { + "kept_docs_count": 2, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "30bdeed3-d32d-491b-a771-dbf5ea73f927", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T02:29:44.970793Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: Imagine there is a building called Bronte tower whose height in feet is the same number as the dewey decimal classification for the Charlotte Bronte book that was published in 1847. Where would this building rank among tallest buildings in New York City, as of August 2024?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Skyline of New York City Midtown Manhattan with the Empire State Building (center) and Lower Manhattan with One WTC (center-right) Tallest building One World Trade Center (2014) Tallest building height 1,776 ft (541 m) Major clusters Midtown Manhattan Lower Manhattan Downtown Brooklyn Long Island City First 150 m+ building Singer Building (1898) Number of tall buildings (2025) Taller than 100 m (328 ft) 884 Taller than 150 m (492 ft) 323+6 T/O Taller than 200 m (656 ft) 101+5 T/O Taller than 300 m (984 ft) 18+1 T/O Taller than 400 m (1,312 ft) 6 Number of tall buildings — feet Taller than 300 ft (91.4 m) 1,072 Midtown Manhattan in June 2024 looking north from the Empire State Building 's 102nd floor (1,224 feet or 373 meters) Lower Manhattan , viewed from Je\n\n[DOC 2] Office skyscraper in Manhattan, New York ‹ The template Infobox building is being considered for merging . › Empire State Building Empire State Building illuminated in 2021 Interactive map of the Empire State Building area Record height Tallest in the world from 1931 to 1970 Preceded by Chrysler Building Surpassed by World Trade Center General information Type Office building ; observation decks Architectural style Art Deco Location 350 Fifth Avenue Manhattan , New York, 10118 U. S. Coordinates 40°44′54′′N 73°59′08′′W  /  40.7483°N 73.9856°W  / 40.7483; -73.9856 Construction started March 17, 1930 ; 96 years ago ( 1930-03-17 ) Topped-out September 19, 1930 ; 95 years ago ( 1930-09-19 ) Completed April 11, 1931 ; 94 years ago ( 1931-04-11 ) Opened May 1, 1\n\n\nSEARCH HISTORY: - Query: 'Bronte Tower height' → 4 docs\n- Query: 'Jane Eyre Dewey Decimal' → 3 docs\n- Query: 'tallest buildings New York 2024' → 2 docs\n- Query: 'Bronte Tower building' → 3 docs\n- Query: 'Jane Eyre Dewey Decimal number' → 0 docs\n- Query: 'List of tallest buildings in New York City 2024' → 0 docs\n- Query: 'Jane Eyre classification' → 0 docs\n- Query: 'Bronte Tower Manhattan' → 0 docs\n- Query: 'New York City skyscraper rankings 2024' → 0 docs\n\nFEEDBACK: We need the building's height (Bronte Tower) and the Dewey Decimal for Charlotte Bronte's 1847 book (Jane Eyre). Then we must compare that height to the rankings in a 2024 list of NYC's tallest buildings.\nWe still need the exact Dewey Decimal classification for the 1847 edition of Jane Eyre, the precise height of Bronte Tower, and a current ranked list of NYC skyscrapers to determine the building's rank.\nNeed: exact Dewey Decimal number for the 1847 edition of Jane Eyre; precise height of the building named Bronte Tower; a 2024 ranking of NYC skyscrapers to place that height within the list.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Jane Eyre Dewey Decimal 1847\",\n \"Bronte Tower New York City height\",\n \"New York City skyscraper ranking 2024\"\n ],\n \"feedback\": \"Missing: exact Dewey Decimal number for the 1847 edition of Jane Eyre, precise height (feet) of Bronte Tower, and a 2024 list showing the rank order of NYC tall buildings to compare the height.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1161, + "osl": 96, + "total_tokens": 1257, + "latency_ms": 9340.79, + "tokens_per_second": 10.28 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "80e1872c-4090-4597-ad99-fbbe892bafd9", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T02:29:46.876073Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: Imagine there is a building called Bronte tower whose height in feet is the same number as the dewey decimal classification for the Charlotte Bronte book that was published in 1847. Where would this building rank among tallest buildings in New York City, as of August 2024?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] , as thought most advisable,\" and draws Colburn's attention to the fact that the authors have already been published elsewhere. The Professor was not published, though the other two novels were accepted for publication on somewhat unfavourable terms. Charlotte submitted a second manuscript, Jane Eyre , to a different publisher, Smith, Elder & Co. in August 1847, and it was published soon afterwards. The novel follows the life of a plain young woman, Jane, depicting her troubled childhood, her unhappy schooldays and her arrival in a new post as a governess to a young girl in a secluded mansion in Yorkshire. Jane falls in love with her employer, Mr Rochester , who is secretly hiding the fact that his first wife, a dangerous madwoman, is being kept in the attic\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] Skyline of New York City Midtown Manhattan with the Empire State Building (center) and Lower Manhattan with One WTC (center-right) Tallest building One World Trade Center (2014) Tallest building height 1,776 ft (541 m) Major clusters Midtown Manhattan Lower Manhattan Downtown Brooklyn Long Island Ci...\n[KEPT 2] Office skyscraper in Manhattan, New York ‹ The template Infobox building is being considered for merging . › Empire State Building Empire State Building illuminated in 2021 Interactive map of the Empire State Building area Record height Tallest in the world from 1931 to 1970 Preceded by Chrysler Bui...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 597, + "osl": 433, + "total_tokens": 1030, + "latency_ms": 1328.55, + "tokens_per_second": 325.92 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "df9e78fb-eeeb-4734-9b0b-b5086f73db13", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T02:29:51.523779Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: Imagine there is a building called Bronte tower whose height in feet is the same number as the dewey decimal classification for the Charlotte Bronte book that was published in 1847. Where would this building rank among tallest buildings in New York City, as of August 2024?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Skyline of New York City Midtown Manhattan with the Empire State Building (center) and Lower Manhattan with One WTC (center-right) Tallest building One World Trade Center (2014) Tallest building height 1,776 ft (541 m) Major clusters Midtown Manhattan Lower Manhattan Downtown Brooklyn Long Island City First 150 m+ building Singer Building (1898) Number of tall buildings (2025) Taller than 100 m (328 ft) 884 Taller than 150 m (492 ft) 323+6 T/O Taller than 200 m (656 ft) 101+5 T/O Taller than 300 m (984 ft) 18+1 T/O Taller than 400 m (1,312 ft) 6 Number of tall buildings — feet Taller than 300 ft (91.4 m) 1,072 Midtown Manhattan in June 2024 looking north from the Empire State Building 's 102nd floor (1,224 feet or 373 meters) Lower Manhattan , viewed from Je\n\n[DOC 2] Office skyscraper in Manhattan, New York ‹ The template Infobox building is being considered for merging . › Empire State Building Empire State Building illuminated in 2021 Interactive map of the Empire State Building area Record height Tallest in the world from 1931 to 1970 Preceded by Chrysler Building Surpassed by World Trade Center General information Type Office building ; observation decks Architectural style Art Deco Location 350 Fifth Avenue Manhattan , New York, 10118 U. S. Coordinates 40°44′54′′N 73°59′08′′W  /  40.7483°N 73.9856°W  / 40.7483; -73.9856 Construction started March 17, 1930 ; 96 years ago ( 1930-03-17 ) Topped-out September 19, 1930 ; 95 years ago ( 1930-09-19 ) Completed April 11, 1931 ; 94 years ago ( 1931-04-11 ) Opened May 1, 1\n\n[DOC 3] , as thought most advisable,\" and draws Colburn's attention to the fact that the authors have already been published elsewhere. The Professor was not published, though the other two novels were accepted for publication on somewhat unfavourable terms. Charlotte submitted a second manuscript, Jane Eyre , to a different publisher, Smith, Elder & Co. in August 1847, and it was published soon afterwards. The novel follows the life of a plain young woman, Jane, depicting her troubled childhood, her unhappy schooldays and her arrival in a new post as a governess to a young girl in a secluded mansion in Yorkshire. Jane falls in love with her employer, Mr Rochester , who is secretly hiding the fact that his first wife, a dangerous madwoman, is being kept in the attic\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Missing the Dewey Decimal classification number for Charlotte Brontë's 1847 book (Jane Eyre), which is needed to determine the building's height. Additionally, the documents do not provide a complete ranking list of NYC building heights to place a building of that specific height among the tallest as of August 2024.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 932, + "osl": 633, + "total_tokens": 1565, + "latency_ms": 4647.16, + "tokens_per_second": 136.21 + }, + "context": { + "kept_docs_count": 3, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "bb92dc96-d305-4886-84ea-3a6d07ba7145", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T02:29:52.231769Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: Imagine there is a building called Bronte tower whose height in feet is the same number as the dewey decimal classification for the Charlotte Bronte book that was published in 1847. Where would this building rank among tallest buildings in New York City, as of August 2024?\n\nDOCUMENTS:\n\n[DOC 1] Skyline of New York City Midtown Manhattan with the Empire State Building (center) and Lower Manhattan with One WTC (center-right) Tallest building One World Trade Center (2014) Tallest building height 1,776 ft (541 m) Major clusters Midtown Manhattan Lower Manhattan Downtown Brooklyn Long Island City First 150 m+ building Singer Building (1898) Number of tall buildings (2025) Taller than 100 m (328 ft) 884 Taller than 150 m (492 ft) 323+6 T/O Taller than 200 m (656 ft) 101+5 T/O Taller than 300 m (984 ft) 18+1 T/O Taller than 400 m (1,312 ft) 6 Number of tall buildings — feet Taller than 300 ft (91.4 m) 1,072 Midtown Manhattan in June 2024 looking north from the Empire State Building 's 102nd floor (1,224 feet or 373 meters) Lower Manhattan , viewed from Je\n\n[DOC 2] Office skyscraper in Manhattan, New York ‹ The template Infobox building is being considered for merging . › Empire State Building Empire State Building illuminated in 2021 Interactive map of the Empire State Building area Record height Tallest in the world from 1931 to 1970 Preceded by Chrysler Building Surpassed by World Trade Center General information Type Office building ; observation decks Architectural style Art Deco Location 350 Fifth Avenue Manhattan , New York, 10118 U. S. Coordinates 40°44′54′′N 73°59′08′′W  /  40.7483°N 73.9856°W  / 40.7483; -73.9856 Construction started March 17, 1930 ; 96 years ago ( 1930-03-17 ) Topped-out September 19, 1930 ; 95 years ago ( 1930-09-19 ) Completed April 11, 1931 ; 94 years ago ( 1931-04-11 ) Opened May 1, 1\n\n[DOC 3] , as thought most advisable,\" and draws Colburn's attention to the fact that the authors have already been published elsewhere. The Professor was not published, though the other two novels were accepted for publication on somewhat unfavourable terms. Charlotte submitted a second manuscript, Jane Eyre , to a different publisher, Smith, Elder & Co. in August 1847, and it was published soon afterwards. The novel follows the life of a plain young woman, Jane, depicting her troubled childhood, her unhappy schooldays and her arrival in a new post as a governess to a young girl in a secluded mansion in Yorkshire. Jane falls in love with her employer, Mr Rochester , who is secretly hiding the fact that his first wife, a dangerous madwoman, is being kept in the attic\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 791, + "osl": 170, + "total_tokens": 961, + "latency_ms": 707.38, + "tokens_per_second": 240.32 + }, + "context": { + "num_documents_used": 3, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:29:52.232531Z", + "summary": { + "total_llm_calls": 12, + "total_hops": 5, + "total_input_tokens": 11465, + "total_output_tokens": 4768, + "total_tokens": 16233, + "total_latency_ms": 63773.99, + "average_tokens_per_second": 124.13, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Charlotte_Bront%C3%AB", + "https://en.wikipedia.org/wiki/Jane_Eyre", + "https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_New_York_City" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "37th" + } + }, + { + "query_id": "2", + "original_query": "How many years earlier would Punxsutawney Phil have to be canonically alive to have made a Groundhog Day prediction in the same state as the US capitol?", + "timestamp_start": "2026-05-16T02:29:52.234795Z", + "llm_calls": [ + { + "call_id": "aeb2679c-f4b7-4888-a38d-9a720700ac83", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:30:06.516428Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: How many years earlier would Punxsutawney Phil have to be canonically alive to have made a Groundhog Day prediction in the same state as the US capitol?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Punxsutawney Phil first prediction\",\n \"United States Capitol first session year\",\n \"US Capitol building completed year\"\n ],\n \"feedback\": \"Need the year Punxsutawney Phil first made a Groundhog Day prediction and the year the U.S. Capitol was first in use/completed. The difference will give the number of years earlier Phil would need to be alive.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 410, + "osl": 90, + "total_tokens": 500, + "latency_ms": 14281.5, + "tokens_per_second": 6.3 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "ba0165fb-f0c2-43df-8dd3-f4d9571a69c8", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:30:09.712431Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: How many years earlier would Punxsutawney Phil have to be canonically alive to have made a Groundhog Day prediction in the same state as the US capitol?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] , Pennsylvania . The Pennsylvania Lottery 's mascot is a groundhog named Gus , referred to in commercials as \"the second most famous groundhog in Pennsylvania\", in deference to Phil. Since the Groundhog Club Inner Circle has trademarked the use of the name \"Punxsutawney Phil,\" no commercial entity may use the name without permission. Phil is the protagonist of the 2024 play How to Kill a Rodent . In the play, Canadians Guy and Carrie go to Punxsutawney to commit an act of terrorism against the United States by assassinating Phil on live television. Historical predictions Punxsutawney Phil's predictions 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 191\n\n[NEW 2] the north wing until a temporary wooden pavilion was erected on the future site of the House wing which served for a few years for the Representatives to meet in, until the House of Representatives (south) wing was finally completed in 1811, with a covered wooden temporary walkway connecting the two wings with the Congressional chambers where the future center section with rotunda and dome would eventually be. However, the House of Representatives moved early into their House wing in 1807. Though the Senate wing building was incomplete, the Capitol held its first session of the U. S. Congress with both chambers in session on November 17, 1800. The National Legislature was moved to Washington prematurely, at the urging of President John Adams , in hopes of s\n\n[NEW 3] from Philadelphia due to a riot of angry soldiers. See: Pennsylvania Mutiny of 1783 ^ Government offices were evacuated to Trenton, New Jersey , from August to November 1799 following an outbreak of yellow fever in Philadelphia. ^ The District of Columbia was formed February 27, 1801, with the District of Columbia Organic Act of 1801 . The city of Washington was founded in 1791 and construction of the new capital began while it was still part of Maryland. President John Adams moved to the White House on November 1, 1800 and the 6th United States Congress held its first session in Washington on November 17, 1800. ^ President James Madison fled to the home of Caleb Bentley in Brookeville, Maryland following the burning of Washington on August 24–25, 1814. As\n\n[NEW 4] ( 1789-03-04 ) First holder George Washington Succession Line of succession Salary $400,000 per year Website whitehouse. gov This article is part of a series on the Politics of the United States Federal government Constitution of the United States Laws Treaties Legislature United States Congress House of Representatives Speaker Mike Johnson (R) Majority Leader Steve Scalise (R) Minority Leader Hakeem Jeffries (D) Congressional districts ( list ) Non-voting members Senate President JD Vance (R) President Pro Tempore Chuck Grassley (R) Majority Leader John Thune (R) Minority Leader Chuck Schumer (D) Executive President of the United States Donald Trump (R) Vice President of the United States JD Vance (R) Cabinet Federal agencies Executive Office Judiciary Supr\n\n[NEW 5] ( 1789-03-04 ) First holder George Washington Succession Line of succession Salary $400,000 per year Website whitehouse. gov This article is part of a series on the Politics of the United States Federal government Constitution of the United States Laws Treaties Legislature United States Congress House of Representatives Speaker Mike Johnson (R) Majority Leader Steve Scalise (R) Minority Leader Hakeem Jeffries (D) Congressional districts ( list ) Non-voting members Senate President JD Vance (R) President Pro Tempore Chuck Grassley (R) Majority Leader John Thune (R) Minority Leader Chuck Schumer (D) Executive President of the United States Donald Trump (R) Vice President of the United States JD Vance (R) Cabinet Federal agencies Executive Office Judiciary Supr\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 5 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[1,1,1,0,0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1160, + "osl": 751, + "total_tokens": 1911, + "latency_ms": 2636.3, + "tokens_per_second": 284.87 + }, + "context": { + "num_documents_evaluated": 5, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "cfab4c64-4969-4710-9901-c4a15986f8b4", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:30:25.481404Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: How many years earlier would Punxsutawney Phil have to be canonically alive to have made a Groundhog Day prediction in the same state as the US capitol?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] , Pennsylvania . The Pennsylvania Lottery 's mascot is a groundhog named Gus , referred to in commercials as \"the second most famous groundhog in Pennsylvania\", in deference to Phil. Since the Groundhog Club Inner Circle has trademarked the use of the name \"Punxsutawney Phil,\" no commercial entity may use the name without permission. Phil is the protagonist of the 2024 play How to Kill a Rodent . In the play, Canadians Guy and Carrie go to Punxsutawney to commit an act of terrorism against the United States by assassinating Phil on live television. Historical predictions Punxsutawney Phil's predictions 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 191\n\n[DOC 2] the north wing until a temporary wooden pavilion was erected on the future site of the House wing which served for a few years for the Representatives to meet in, until the House of Representatives (south) wing was finally completed in 1811, with a covered wooden temporary walkway connecting the two wings with the Congressional chambers where the future center section with rotunda and dome would eventually be. However, the House of Representatives moved early into their House wing in 1807. Though the Senate wing building was incomplete, the Capitol held its first session of the U. S. Congress with both chambers in session on November 17, 1800. The National Legislature was moved to Washington prematurely, at the urging of President John Adams , in hopes of s\n\n[DOC 3] from Philadelphia due to a riot of angry soldiers. See: Pennsylvania Mutiny of 1783 ^ Government offices were evacuated to Trenton, New Jersey , from August to November 1799 following an outbreak of yellow fever in Philadelphia. ^ The District of Columbia was formed February 27, 1801, with the District of Columbia Organic Act of 1801 . The city of Washington was founded in 1791 and construction of the new capital began while it was still part of Maryland. President John Adams moved to the White House on November 1, 1800 and the 6th United States Congress held its first session in Washington on November 17, 1800. ^ President James Madison fled to the home of Caleb Bentley in Brookeville, Maryland following the burning of Washington on August 24–25, 1814. As\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": true,\n \"reasoning\": \"DOC 1 lists the earliest canonical Punxsutawney Phil prediction year as 1886. DOC 2 states that the United States Capitol held its first session of Congress on November 17, 1800. The difference between these two dates (1886 − 1800) is 86 years, providing the required information to answer how many years earlier Phil would need to be alive to have made a prediction in the same state as the US Capitol.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 872, + "osl": 1188, + "total_tokens": 2060, + "latency_ms": 15768.09, + "tokens_per_second": 75.34 + }, + "context": { + "kept_docs_count": 3, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "d9ad2074-ab2f-4314-a1bb-d1b1dd98c6da", + "component": "answer_generator", + "hop_count": 2, + "timestamp": "2026-05-16T02:30:32.471283Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: How many years earlier would Punxsutawney Phil have to be canonically alive to have made a Groundhog Day prediction in the same state as the US capitol?\n\nDOCUMENTS:\n\n[DOC 1] , Pennsylvania . The Pennsylvania Lottery 's mascot is a groundhog named Gus , referred to in commercials as \"the second most famous groundhog in Pennsylvania\", in deference to Phil. Since the Groundhog Club Inner Circle has trademarked the use of the name \"Punxsutawney Phil,\" no commercial entity may use the name without permission. Phil is the protagonist of the 2024 play How to Kill a Rodent . In the play, Canadians Guy and Carrie go to Punxsutawney to commit an act of terrorism against the United States by assassinating Phil on live television. Historical predictions Punxsutawney Phil's predictions 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 191\n\n[DOC 2] the north wing until a temporary wooden pavilion was erected on the future site of the House wing which served for a few years for the Representatives to meet in, until the House of Representatives (south) wing was finally completed in 1811, with a covered wooden temporary walkway connecting the two wings with the Congressional chambers where the future center section with rotunda and dome would eventually be. However, the House of Representatives moved early into their House wing in 1807. Though the Senate wing building was incomplete, the Capitol held its first session of the U. S. Congress with both chambers in session on November 17, 1800. The National Legislature was moved to Washington prematurely, at the urging of President John Adams , in hopes of s\n\n[DOC 3] from Philadelphia due to a riot of angry soldiers. See: Pennsylvania Mutiny of 1783 ^ Government offices were evacuated to Trenton, New Jersey , from August to November 1799 following an outbreak of yellow fever in Philadelphia. ^ The District of Columbia was formed February 27, 1801, with the District of Columbia Organic Act of 1801 . The city of Washington was founded in 1791 and construction of the new capital began while it was still part of Maryland. President John Adams moved to the White House on November 1, 1800 and the 6th United States Congress held its first session in Washington on November 17, 1800. ^ President James Madison fled to the home of Caleb Bentley in Brookeville, Maryland following the burning of Washington on August 24–25, 1814. As\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 668, + "osl": 1, + "total_tokens": 669, + "latency_ms": 6989.21, + "tokens_per_second": 0.14 + }, + "context": { + "num_documents_used": 3, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:30:32.471990Z", + "summary": { + "total_llm_calls": 4, + "total_hops": 2, + "total_input_tokens": 3110, + "total_output_tokens": 2030, + "total_tokens": 5140, + "total_latency_ms": 39675.1, + "average_tokens_per_second": 91.66, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Punxsutawney_Phil", + "https://en.wikipedia.org/wiki/United_States_Capitol" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "87" + } + }, + { + "query_id": "3", + "original_query": "As of August 1, 2024, which country were holders of the FIFA World Cup the last time the UEFA Champions League was won by a club from London?", + "timestamp_start": "2026-05-16T02:30:32.474786Z", + "llm_calls": [ + { + "call_id": "3465ee37-c36d-42a7-b570-1a2726550a80", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:30:38.137303Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 1, 2024, which country were holders of the FIFA World Cup the last time the UEFA Champions League was won by a club from London?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"2021 UEFA Champions League winner\",\n \"France 2018 FIFA World Cup champion\",\n \"World Cup holders 2021\"\n ],\n \"feedback\": \"Need to confirm the most recent London club to win the Champions League (Chelsea 2021) and identify the nation holding the FIFA World Cup title at that time (France, champions of 2018).\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 409, + "osl": 85, + "total_tokens": 494, + "latency_ms": 5662.35, + "tokens_per_second": 15.01 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "db913241-80f6-46cd-bc5b-7238becb56c4", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:30:52.780674Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of August 1, 2024, which country were holders of the FIFA World Cup the last time the UEFA Champions League was won by a club from London?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] all of their matches in a single tournament en route to their tournament victory: Bayern Munich in the 2019–20 season. Paris Saint-Germain are the current European champions, having beaten Inter Milan 5–0 in the 2025 final for their first ever title. History Winners European Cup / UEFA Champions League Season Winners European Cup 1955–56 Real Madrid 1956–57 Real Madrid (2) 1957–58 Real Madrid (3) 1958–59 Real Madrid (4) 1959–60 Real Madrid (5) 1960–61 Benfica 1961–62 Benfica (2) 1962–63 AC Milan 1963–64 Inter Milan 1964–65 Inter Milan (2) 1965–66 Real Madrid (6) 1966–67 Celtic 1967–68 Manchester United 1968–69 AC Milan (2) 1969–70 Feyenoord 1970–71 Ajax 1971–72 Ajax (2) 1972–73 Ajax (3) 1973–74 Bayern Munich 1974–75 Bayern Munich (2) 1975–76 Bayern Munich (\n\n[NEW 2] s 2017 UEFA Champions League Final No support act Black Eyed Peas performed the opening ceremony of the 2017 UEFA Champions League Final. 21 June Robbie Williams The Heavy Entertainment Show Tour Erasure 30 June Justin Bieber Purpose World Tour Halsey 11 July Coldplay A Head Full of Dreams Tour AlunaGeorge and Embrace 12 July 2018 6 June Beyoncé Jay-Z On the Run II Tour DJ Tom Clugston 39,731 people attended the sold out concert. 15 June The Rolling Stones No Filter Tour Elbow 21 June Ed Sheeran ÷ Tour (Divide Tour) Anne-Marie No other act has played at the Stadium on four consecutive nights 22 June 23 June 24 June 2019 27 May Spice Girls Spice World - 2019 UK Tour Jess Glynne Spice Girls first tour since 2007. 8 June Take That Greatest Hits Live Rick Astley\n\n[NEW 3] 20) Team : 2020 Golden Boy : 2020 Golden Player Man Award : 2023 Gullballen : 2020, 2021, 2022, 2023 Kniksen's honour award : 2020 Norwegian Sportsperson of the Year : 2020, 2025 UEFA Champions League Squad/Team of the Season: 2020–21 , 2022–23 UEFA Champions League Forward of the Season : 2020–21 UEFA Champions League top scorer : 2020–21 , 2022–23 UEFA Men's Player of the Year : 2022–23 UEFA Nations League top scorer: 2020–21 , 2022–23 FIFA FIFPRO Men's World 11 : 2021 , 2022 , 2023 , 2024 IFFHS Men's World Team : 2022, 2023 IFFHS Men's UEFA Team: 2024 Manchester City Player of the Season : 2022–23 PFA Team of the Year : 2022–23 Premier League , 2023–24 Premier League PFA Players' Player of the Year : 2022–23 Gerd Müller Trophy : 2023 Globe Soccer Best Pla\n\n[NEW 4] Association football tournament in Russia 2018 FIFA World Cup Чемпионат мира по футболу FIFA 2018 ( Russian ) Chempionat mira po futbolu FIFA 2018 Играй с открытым сердцем Igray s otkrytym serdtsem \"Play with an open heart\" Tournament details Host country Russia Dates 14 June – 15 July Teams 32 (from 5 confederations) Venue 12 (in 11 host cities) Final positions Champions France (2nd title) Runners-up Croatia Third place Belgium Fourth place England Tournament statistics Matches played 64 Goals scored 169 (2.64 per match) Attendance 3,031,768 (47,371 per match) Top scorer Harry Kane (6 goals) Best player Luka Modrić Best young player Kylian Mbappé Best goalkeeper Thibaut Courtois Fair play award Spain ← 2014 2022 → International football competition The 2018\n\n[NEW 5] pted Nemo , a black Labrador Retriever-Griffon dog who lives with them in the Élysée Palace. When he was a schoolboy, Macron decided to be baptised as a Catholic. In June 2018, prior to meeting Pope Francis , he identified himself as an agnostic Catholic . In the same year he agreed to become an honorary canon of St John Lateran , the cathedral of Rome. Macron celebrating France's victory over Croatia in the 2018 World Cup final in Moscow, Russia A fan of football , Macron is a supporter of French club Olympique de Marseille . During the 2018 World Cup , he attended the semi-final between France and Belgium with the Belgian King Philippe and Queen Mathilde ; and at the World Cup final against Croatia , he sat and celebrated alongside Croatian president Kolin\n\n[NEW 6] France 2002 South Korea/Japan Oliver Kahn Ronaldo 8 Oliver Kahn 5 Landon Donovan Belgium 2006 Germany Zinedine Zidane Miroslav Klose 5 Gianluigi Buffon 5 Lukas Podolski Brazil Spain 2010 South Africa Diego Forlán Thomas Müller 5 Iker Casillas 5 Thomas Müller Spain 2014 Brazil Lionel Messi James Rodríguez 6 Manuel Neuer 4 Paul Pogba Colombia 2018 Russia Luka Modrić Harry Kane 6 Thibaut Courtois 3 Kylian Mbappé Spain 2022 Qatar Lionel Messi Kylian Mbappé 8 Emiliano Martínez 3 Enzo Fernández England See also List of FIFA World Cup finals FIFA World Cup records and statistics FIFA World Cup awards FIFA U-20 World Cup FIFA U-17 World Cup FIFA Club World Cup FIFA Beach Soccer World Cup FIFA Futsal World Cup FIFA Confederations Cup List of association football comp\n\n[NEW 7] , 1993 , 2021 , 2024 ) 14 ( 1916 * , 1917 , 1920 , 1923 , 1924 , 1926 , 1935 , 1942 , 1959 , 1967 , 2004 , 2007 , 2015 , 2016 ) 30 Uruguay 15 ( 1916 , 1917 * , 1920 , 1923 * , 1924 * , 1926 , 1935 , 1942 * , 1956 * , 1959 , 1967 * , 1983 , 1987 , 1995 * , 2011 ) 6 ( 1919 , 1927 , 1939 , 1941 , 1989 , 1999 ) 21 Brazil 9 ( 1919 * , 1922 * , 1949 * , 1989 * , 1997 , 1999 , 2004 , 2007 , 2019 * ) 11 ( 1921 , 1925 , 1937 , 1945 , 1946 , 1953 , 1957 , 1959 , 1983 , 1991 , 1995 , 2021 * ) 20 Paraguay 2 ( 1953 , 1979 ) 6 ( 1922 , 1929 , 1947 , 1949 , 1963 , 2011 ) 8 Chile 2 ( 2015 * , 2016 ) 4 ( 1955 * , 1956 , 1979 , 1987 ) 6 Peru 2 ( 1939 * , 1975 ) 1 ( 2019 ) 3 Colombia 1 ( 2001 * ) 2 ( 1975 , 2024 ) 3 Bolivia 1 ( 1963 * ) 1 ( 1997 * ) 2 Mexico — 2 ( 1993 , 2001\n\n[NEW 8] FW FRA Mathys Tel 13 DF ITA Destiny Udogie 14 MF ENG Archie Gray 15 MF SWE Lucas Bergvall 17 DF ARG Cristian Romero ( captain ) 19 FW ENG Dominic Solanke No. Pos. Nation Player 20 FW GHA Mohammed Kudus 21 FW SWE Dejan Kulusevski 22 MF ENG Conor Gallagher 23 DF ESP Pedro Porro 24 DF ENG Djed Spence 28 FW FRA Wilson Odobert 29 MF SEN Pape Matar Sarr 30 MF URU Rodrigo Bentancur 31 GK CZE Antonín Kinský 33 DF WAL Ben Davies 37 DF NED Micky van de Ven 38 DF BRA Souza 39 FW FRA Randal Kolo Muani (on loan from Paris Saint-Germain ) 40 GK USA Brandon Austin Out on loan As of 30 January 2026 Note: Flags indicate national team as defined under FIFA eligibility rules ; some limited exceptions apply. Players may hold more than one non-FIFA nationality. No. Pos. Nation\n\n[NEW 9] UEFA Super Cup winning managers References ^ ^ ^ ^ ^ ^ ^ ^ a b ^ a b ^ a b ^ a b ^ ^ ^ ^ ^ ^ ^ ^ {{ cite news }} : CS1 maint: deprecated archival service ( link ) ^ ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ a b c d e f g h ^ ^ ^ ^ ^ ^ ^ ^ a b c ^ ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ ^ ^ ^ ^ ^ ^ ^ ^ {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ a b ^ a b ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c d ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ a b\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 9 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 0, 0, 1, 1, 1, 1, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2501, + "osl": 1191, + "total_tokens": 3692, + "latency_ms": 14071.24, + "tokens_per_second": 84.64 + }, + "context": { + "num_documents_evaluated": 9, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "cce2d776-8c22-43b4-9cd9-d76ee75213d4", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:31:05.012804Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 1, 2024, which country were holders of the FIFA World Cup the last time the UEFA Champions League was won by a club from London?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] all of their matches in a single tournament en route to their tournament victory: Bayern Munich in the 2019–20 season. Paris Saint-Germain are the current European champions, having beaten Inter Milan 5–0 in the 2025 final for their first ever title. History Winners European Cup / UEFA Champions League Season Winners European Cup 1955–56 Real Madrid 1956–57 Real Madrid (2) 1957–58 Real Madrid (3) 1958–59 Real Madrid (4) 1959–60 Real Madrid (5) 1960–61 Benfica 1961–62 Benfica (2) 1962–63 AC Milan 1963–64 Inter Milan 1964–65 Inter Milan (2) 1965–66 Real Madrid (6) 1966–67 Celtic 1967–68 Manchester United 1968–69 AC Milan (2) 1969–70 Feyenoord 1970–71 Ajax 1971–72 Ajax (2) 1972–73 Ajax (3) 1973–74 Bayern Munich 1974–75 Bayern Munich (2) 1975–76 Bayern Munich (\n\n[DOC 2] Association football tournament in Russia 2018 FIFA World Cup Чемпионат мира по футболу FIFA 2018 ( Russian ) Chempionat mira po futbolu FIFA 2018 Играй с открытым сердцем Igray s otkrytym serdtsem \"Play with an open heart\" Tournament details Host country Russia Dates 14 June – 15 July Teams 32 (from 5 confederations) Venue 12 (in 11 host cities) Final positions Champions France (2nd title) Runners-up Croatia Third place Belgium Fourth place England Tournament statistics Matches played 64 Goals scored 169 (2.64 per match) Attendance 3,031,768 (47,371 per match) Top scorer Harry Kane (6 goals) Best player Luka Modrić Best young player Kylian Mbappé Best goalkeeper Thibaut Courtois Fair play award Spain ← 2014 2022 → International football competition The 2018\n\n[DOC 3] pted Nemo , a black Labrador Retriever-Griffon dog who lives with them in the Élysée Palace. When he was a schoolboy, Macron decided to be baptised as a Catholic. In June 2018, prior to meeting Pope Francis , he identified himself as an agnostic Catholic . In the same year he agreed to become an honorary canon of St John Lateran , the cathedral of Rome. Macron celebrating France's victory over Croatia in the 2018 World Cup final in Moscow, Russia A fan of football , Macron is a supporter of French club Olympique de Marseille . During the 2018 World Cup , he attended the semi-final between France and Belgium with the Belgian King Philippe and Queen Mathilde ; and at the World Cup final against Croatia , he sat and celebrated alongside Croatian president Kolin\n\n[DOC 4] France 2002 South Korea/Japan Oliver Kahn Ronaldo 8 Oliver Kahn 5 Landon Donovan Belgium 2006 Germany Zinedine Zidane Miroslav Klose 5 Gianluigi Buffon 5 Lukas Podolski Brazil Spain 2010 South Africa Diego Forlán Thomas Müller 5 Iker Casillas 5 Thomas Müller Spain 2014 Brazil Lionel Messi James Rodríguez 6 Manuel Neuer 4 Paul Pogba Colombia 2018 Russia Luka Modrić Harry Kane 6 Thibaut Courtois 3 Kylian Mbappé Spain 2022 Qatar Lionel Messi Kylian Mbappé 8 Emiliano Martínez 3 Enzo Fernández England See also List of FIFA World Cup finals FIFA World Cup records and statistics FIFA World Cup awards FIFA U-20 World Cup FIFA U-17 World Cup FIFA Club World Cup FIFA Beach Soccer World Cup FIFA Futsal World Cup FIFA Confederations Cup List of association football comp\n\n[DOC 5] , 1993 , 2021 , 2024 ) 14 ( 1916 * , 1917 , 1920 , 1923 , 1924 , 1926 , 1935 , 1942 , 1959 , 1967 , 2004 , 2007 , 2015 , 2016 ) 30 Uruguay 15 ( 1916 , 1917 * , 1920 , 1923 * , 1924 * , 1926 , 1935 , 1942 * , 1956 * , 1959 , 1967 * , 1983 , 1987 , 1995 * , 2011 ) 6 ( 1919 , 1927 , 1939 , 1941 , 1989 , 1999 ) 21 Brazil 9 ( 1919 * , 1922 * , 1949 * , 1989 * , 1997 , 1999 , 2004 , 2007 , 2019 * ) 11 ( 1921 , 1925 , 1937 , 1945 , 1946 , 1953 , 1957 , 1959 , 1983 , 1991 , 1995 , 2021 * ) 20 Paraguay 2 ( 1953 , 1979 ) 6 ( 1922 , 1929 , 1947 , 1949 , 1963 , 2011 ) 8 Chile 2 ( 2015 * , 2016 ) 4 ( 1955 * , 1956 , 1979 , 1987 ) 6 Peru 2 ( 1939 * , 1975 ) 1 ( 2019 ) 3 Colombia 1 ( 2001 * ) 2 ( 1975 , 2024 ) 3 Bolivia 1 ( 1963 * ) 1 ( 1997 * ) 2 Mexico — 2 ( 1993 , 2001\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide details about FIFA World Cup winners (e.g., France winning the 2018 World Cup in DOC2) but lack any information about UEFA Champions League victories by London clubs, such as Chelsea's wins in 2012 and 2021. Without a record of the most recent Champions League win by a London club, we cannot determine which country held the World Cup title at that time.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1554, + "osl": 787, + "total_tokens": 2341, + "latency_ms": 12231.13, + "tokens_per_second": 64.34 + }, + "context": { + "kept_docs_count": 5, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "0d8ef738-b88f-49e9-880b-15bbc34765a1", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:31:08.722948Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 1, 2024, which country were holders of the FIFA World Cup the last time the UEFA Champions League was won by a club from London?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] all of their matches in a single tournament en route to their tournament victory: Bayern Munich in the 2019–20 season. Paris Saint-Germain are the current European champions, having beaten Inter Milan 5–0 in the 2025 final for their first ever title. History Winners European Cup / UEFA Champions League Season Winners European Cup 1955–56 Real Madrid 1956–57 Real Madrid (2) 1957–58 Real Madrid (3) 1958–59 Real Madrid (4) 1959–60 Real Madrid (5) 1960–61 Benfica 1961–62 Benfica (2) 1962–63 AC Milan 1963–64 Inter Milan 1964–65 Inter Milan (2) 1965–66 Real Madrid (6) 1966–67 Celtic 1967–68 Manchester United 1968–69 AC Milan (2) 1969–70 Feyenoord 1970–71 Ajax 1971–72 Ajax (2) 1972–73 Ajax (3) 1973–74 Bayern Munich 1974–75 Bayern Munich (2) 1975–76 Bayern Munich (\n\n[DOC 2] Association football tournament in Russia 2018 FIFA World Cup Чемпионат мира по футболу FIFA 2018 ( Russian ) Chempionat mira po futbolu FIFA 2018 Играй с открытым сердцем Igray s otkrytym serdtsem \"Play with an open heart\" Tournament details Host country Russia Dates 14 June – 15 July Teams 32 (from 5 confederations) Venue 12 (in 11 host cities) Final positions Champions France (2nd title) Runners-up Croatia Third place Belgium Fourth place England Tournament statistics Matches played 64 Goals scored 169 (2.64 per match) Attendance 3,031,768 (47,371 per match) Top scorer Harry Kane (6 goals) Best player Luka Modrić Best young player Kylian Mbappé Best goalkeeper Thibaut Courtois Fair play award Spain ← 2014 2022 → International football competition The 2018\n\n[DOC 3] pted Nemo , a black Labrador Retriever-Griffon dog who lives with them in the Élysée Palace. When he was a schoolboy, Macron decided to be baptised as a Catholic. In June 2018, prior to meeting Pope Francis , he identified himself as an agnostic Catholic . In the same year he agreed to become an honorary canon of St John Lateran , the cathedral of Rome. Macron celebrating France's victory over Croatia in the 2018 World Cup final in Moscow, Russia A fan of football , Macron is a supporter of French club Olympique de Marseille . During the 2018 World Cup , he attended the semi-final between France and Belgium with the Belgian King Philippe and Queen Mathilde ; and at the World Cup final against Croatia , he sat and celebrated alongside Croatian president Kolin\n\n[DOC 4] France 2002 South Korea/Japan Oliver Kahn Ronaldo 8 Oliver Kahn 5 Landon Donovan Belgium 2006 Germany Zinedine Zidane Miroslav Klose 5 Gianluigi Buffon 5 Lukas Podolski Brazil Spain 2010 South Africa Diego Forlán Thomas Müller 5 Iker Casillas 5 Thomas Müller Spain 2014 Brazil Lionel Messi James Rodríguez 6 Manuel Neuer 4 Paul Pogba Colombia 2018 Russia Luka Modrić Harry Kane 6 Thibaut Courtois 3 Kylian Mbappé Spain 2022 Qatar Lionel Messi Kylian Mbappé 8 Emiliano Martínez 3 Enzo Fernández England See also List of FIFA World Cup finals FIFA World Cup records and statistics FIFA World Cup awards FIFA U-20 World Cup FIFA U-17 World Cup FIFA Club World Cup FIFA Beach Soccer World Cup FIFA Futsal World Cup FIFA Confederations Cup List of association football comp\n\n[DOC 5] , 1993 , 2021 , 2024 ) 14 ( 1916 * , 1917 , 1920 , 1923 , 1924 , 1926 , 1935 , 1942 , 1959 , 1967 , 2004 , 2007 , 2015 , 2016 ) 30 Uruguay 15 ( 1916 , 1917 * , 1920 , 1923 * , 1924 * , 1926 , 1935 , 1942 * , 1956 * , 1959 , 1967 * , 1983 , 1987 , 1995 * , 2011 ) 6 ( 1919 , 1927 , 1939 , 1941 , 1989 , 1999 ) 21 Brazil 9 ( 1919 * , 1922 * , 1949 * , 1989 * , 1997 , 1999 , 2004 , 2007 , 2019 * ) 11 ( 1921 , 1925 , 1937 , 1945 , 1946 , 1953 , 1957 , 1959 , 1983 , 1991 , 1995 , 2021 * ) 20 Paraguay 2 ( 1953 , 1979 ) 6 ( 1922 , 1929 , 1947 , 1949 , 1963 , 2011 ) 8 Chile 2 ( 2015 * , 2016 ) 4 ( 1955 * , 1956 , 1979 , 1987 ) 6 Peru 2 ( 1939 * , 1975 ) 1 ( 2019 ) 3 Colombia 1 ( 2001 * ) 2 ( 1975 , 2024 ) 3 Bolivia 1 ( 1963 * ) 1 ( 1997 * ) 2 Mexico — 2 ( 1993 , 2001\n\n\nSEARCH HISTORY: - Query: '2021 UEFA Champions League winner' → 3 docs\n- Query: 'France 2018 FIFA World Cup champion' → 3 docs\n- Query: 'World Cup holders 2021' → 3 docs\n\nFEEDBACK: Need to confirm the most recent London club to win the Champions League (Chelsea 2021) and identify the nation holding the FIFA World Cup title at that time (France, champions of 2018).\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Chelsea 2021 UEFA Champions League winner\",\n \"List of UEFA Champions League winners London club\",\n \"FIFA World Cup holder 2021\"\n ],\n \"feedback\": \"These queries will confirm that the last London club to win the Champions League was Chelsea in 2021 and identify that France were the reigning World Cup champions at that time.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1783, + "osl": 505, + "total_tokens": 2288, + "latency_ms": 3709.46, + "tokens_per_second": 136.14 + }, + "context": { + "kept_docs_count": 5, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "690ffd1d-be11-4d6e-a786-3121df23d471", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T02:31:22.762589Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of August 1, 2024, which country were holders of the FIFA World Cup the last time the UEFA Champions League was won by a club from London?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] er to win Premier League Player of the Month and Manager of the Month, after Gareth Southgate and Stuart Pearce . Lampard eventually guided Chelsea to fourth in the Premier League and the FA Cup Final , where they lost to Arsenal . In the following season , Chelsea made five major acquisitions in the summer transfer window in Hakim Ziyech , Timo Werner , Ben Chilwell , Kai Havertz and Édouard Mendy . Chelsea initially started strongly, topping their Champions League group and the Premier League in early December. However, after a run of two wins in eight Premier League matches, Chelsea dropped to ninth and Lampard was dismissed as manager on 25 January 2021. He remained on Chelsea's payroll to see out his contract, \"pocketing £75,000\" per week in compensatio\n\n[NEW 2] 65 Honours Drogba holding the European Cup following Chelsea's penalty shootout victory over Bayern Munich Drogba banner made by Chelsea's fans Marseille UEFA Cup runner-up: 2003–04 Chelsea Premier League : 2004–05 , 2005–06 , 2009–10 , 2014–15 FA Cup : 2006–07 , 2008–09 , 2009–10 , 2011–12 Football League Cup : 2004–05 , 2006–07 , 2014–15 ; runner-up: 2007–08 FA Community Shield : 2005 , 2009 UEFA Champions League : 2011–12 Galatasaray Süper Lig : 2012–13 Turkish Cup : 2013–14 Turkish Super Cup : 2013 Phoenix Rising Western Conference (USL) : 2018 Ivory Coast Africa Cup of Nations runner-up: 2006 , 2012 Individual Africa Cup of Nations Team of the Tournament: 2006 , 2012 Africa Cup of Nations Top Scorer: 2012 African Footballer of the Year : 2006, 2009 Alan\n\n[NEW 3] 65 Honours Drogba holding the European Cup following Chelsea's penalty shootout victory over Bayern Munich Drogba banner made by Chelsea's fans Marseille UEFA Cup runner-up: 2003–04 Chelsea Premier League : 2004–05 , 2005–06 , 2009–10 , 2014–15 FA Cup : 2006–07 , 2008–09 , 2009–10 , 2011–12 Football League Cup : 2004–05 , 2006–07 , 2014–15 ; runner-up: 2007–08 FA Community Shield : 2005 , 2009 UEFA Champions League : 2011–12 Galatasaray Süper Lig : 2012–13 Turkish Cup : 2013–14 Turkish Super Cup : 2013 Phoenix Rising Western Conference (USL) : 2018 Ivory Coast Africa Cup of Nations runner-up: 2006 , 2012 Individual Africa Cup of Nations Team of the Tournament: 2006 , 2012 Africa Cup of Nations Top Scorer: 2012 African Footballer of the Year : 2006, 2009 Alan\n\n[NEW 4] 20 June 2018 Kazan Arena , Kazan , Russia 22 Iran 1–0 1–0 2018 FIFA World Cup Honours Chelsea supporters' banner in honour of Costa, November 2014 Atlético Madrid La Liga : 2013–14 , 2020–21 Copa del Rey : 2012–13 UEFA Europa League : 2017–18 UEFA Super Cup : 2010 , 2012 , 2018 UEFA Champions League runner-up: 2013–14 Chelsea Premier League : 2014–15 , 2016–17 Football League Cup : 2014–15 FA Cup runner-up: 2016–17 Atlético Mineiro Campeonato Brasileiro Série A : 2021 Copa do Brasil : 2021 Grêmio Campeonato Gaúcho : 2024 Individual La Liga Player of the Month : September 2013 La Liga Team of the Season : 2013–14 Trofeo EFE : 2013–14 UEFA Champions League Team of the Season: 2013–14 Zarra Trophy : 2013–14 Premier League Player of the Month : August 2014 , Nov\n\n[NEW 5] 26 FIFA World Cup GS Brazil v Haiti Philadelphia , United States 21:00 UTC−4 Report Stadium: Lincoln Financial Field Scotland v Brazil 24 June 2026 2026 FIFA World Cup GS Scotland v Brazil Miami Gardens , United States 18:00 UTC−4 Report Stadium: Hard Rock Stadium Coaching staff Carlo Ancelotti, the head coach of the Brazil national football team since 26 May 2025, after leaving his former club Real Madrid. Position Name Ref Head coach Carlo Ancelotti Assistant coach Paul Clement Goalkeeping coaches Cláudio Taffarel Marco Antônio Trocourt Physical coach Francesco Mauri Match analysts Simone Montanaro Guilherme Lyra João Marcos Soares Thomaz Koerich Performance analyst Mino Fulco Physiologist Guilherme Passos Doctor Rodrigo Lasmar Team coordinator Juan Sporti\n\n[NEW 6] elgrafico. com. ar, 8 February 2022 ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ https://www. beinsports. com/en-nz/football/uefa-world-cup-qualifiers/articles-video/retegui-brace-keeps-italy-s-world-cup-hopes-alive-2025-10-14 ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ External links Mateo Retegui at Soccerway\n\n\nKEPT DOCUMENTS (context only):\n[5 documents already kept as relevant]\n[KEPT 1] all of their matches in a single tournament en route to their tournament victory: Bayern Munich in the 2019–20 season. Paris Saint-Germain are the current European champions, having beaten Inter Milan 5–0 in the 2025 final for their first ever title. History Winners European Cup / UEFA Champions Lea...\n[KEPT 2] Association football tournament in Russia 2018 FIFA World Cup Чемпионат мира по футболу FIFA 2018 ( Russian ) Chempionat mira po futbolu FIFA 2018 Играй с открытым сердцем Igray s otkrytym serdtsem \"Play with an open heart\" Tournament details Host country Russia Dates 14 June – 15 July Teams 32 (fro...\n[KEPT 3] pted Nemo , a black Labrador Retriever-Griffon dog who lives with them in the Élysée Palace. When he was a schoolboy, Macron decided to be baptised as a Catholic. In June 2018, prior to meeting Pope Francis , he identified himself as an agnostic Catholic . In the same year he agreed to become an hon...\n[KEPT 4] France 2002 South Korea/Japan Oliver Kahn Ronaldo 8 Oliver Kahn 5 Landon Donovan Belgium 2006 Germany Zinedine Zidane Miroslav Klose 5 Gianluigi Buffon 5 Lukas Podolski Brazil Spain 2010 South Africa Diego Forlán Thomas Müller 5 Iker Casillas 5 Thomas Müller Spain 2014 Brazil Lionel Messi James Rodr...\n[KEPT 5] , 1993 , 2021 , 2024 ) 14 ( 1916 * , 1917 , 1920 , 1923 , 1924 , 1926 , 1935 , 1942 , 1959 , 1967 , 2004 , 2007 , 2015 , 2016 ) 30 Uruguay 15 ( 1916 , 1917 * , 1920 , 1923 * , 1924 * , 1926 , 1935 , 1942 * , 1956 * , 1959 , 1967 * , 1983 , 1987 , 1995 * , 2011 ) 6 ( 1919 , 1927 , 1939 , 1941 , 1989 ...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 6 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 1, 1, 1, 1, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1967, + "osl": 1278, + "total_tokens": 3245, + "latency_ms": 13454.43, + "tokens_per_second": 94.99 + }, + "context": { + "num_documents_evaluated": 6, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 5 + } + }, + { + "call_id": "dcd9824d-8e71-46a7-99b3-b4b188239bd1", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T02:31:32.437065Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 1, 2024, which country were holders of the FIFA World Cup the last time the UEFA Champions League was won by a club from London?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] all of their matches in a single tournament en route to their tournament victory: Bayern Munich in the 2019–20 season. Paris Saint-Germain are the current European champions, having beaten Inter Milan 5–0 in the 2025 final for their first ever title. History Winners European Cup / UEFA Champions League Season Winners European Cup 1955–56 Real Madrid 1956–57 Real Madrid (2) 1957–58 Real Madrid (3) 1958–59 Real Madrid (4) 1959–60 Real Madrid (5) 1960–61 Benfica 1961–62 Benfica (2) 1962–63 AC Milan 1963–64 Inter Milan 1964–65 Inter Milan (2) 1965–66 Real Madrid (6) 1966–67 Celtic 1967–68 Manchester United 1968–69 AC Milan (2) 1969–70 Feyenoord 1970–71 Ajax 1971–72 Ajax (2) 1972–73 Ajax (3) 1973–74 Bayern Munich 1974–75 Bayern Munich (2) 1975–76 Bayern Munich (\n\n[DOC 2] Association football tournament in Russia 2018 FIFA World Cup Чемпионат мира по футболу FIFA 2018 ( Russian ) Chempionat mira po futbolu FIFA 2018 Играй с открытым сердцем Igray s otkrytym serdtsem \"Play with an open heart\" Tournament details Host country Russia Dates 14 June – 15 July Teams 32 (from 5 confederations) Venue 12 (in 11 host cities) Final positions Champions France (2nd title) Runners-up Croatia Third place Belgium Fourth place England Tournament statistics Matches played 64 Goals scored 169 (2.64 per match) Attendance 3,031,768 (47,371 per match) Top scorer Harry Kane (6 goals) Best player Luka Modrić Best young player Kylian Mbappé Best goalkeeper Thibaut Courtois Fair play award Spain ← 2014 2022 → International football competition The 2018\n\n[DOC 3] pted Nemo , a black Labrador Retriever-Griffon dog who lives with them in the Élysée Palace. When he was a schoolboy, Macron decided to be baptised as a Catholic. In June 2018, prior to meeting Pope Francis , he identified himself as an agnostic Catholic . In the same year he agreed to become an honorary canon of St John Lateran , the cathedral of Rome. Macron celebrating France's victory over Croatia in the 2018 World Cup final in Moscow, Russia A fan of football , Macron is a supporter of French club Olympique de Marseille . During the 2018 World Cup , he attended the semi-final between France and Belgium with the Belgian King Philippe and Queen Mathilde ; and at the World Cup final against Croatia , he sat and celebrated alongside Croatian president Kolin\n\n[DOC 4] France 2002 South Korea/Japan Oliver Kahn Ronaldo 8 Oliver Kahn 5 Landon Donovan Belgium 2006 Germany Zinedine Zidane Miroslav Klose 5 Gianluigi Buffon 5 Lukas Podolski Brazil Spain 2010 South Africa Diego Forlán Thomas Müller 5 Iker Casillas 5 Thomas Müller Spain 2014 Brazil Lionel Messi James Rodríguez 6 Manuel Neuer 4 Paul Pogba Colombia 2018 Russia Luka Modrić Harry Kane 6 Thibaut Courtois 3 Kylian Mbappé Spain 2022 Qatar Lionel Messi Kylian Mbappé 8 Emiliano Martínez 3 Enzo Fernández England See also List of FIFA World Cup finals FIFA World Cup records and statistics FIFA World Cup awards FIFA U-20 World Cup FIFA U-17 World Cup FIFA Club World Cup FIFA Beach Soccer World Cup FIFA Futsal World Cup FIFA Confederations Cup List of association football comp\n\n[DOC 5] , 1993 , 2021 , 2024 ) 14 ( 1916 * , 1917 , 1920 , 1923 , 1924 , 1926 , 1935 , 1942 , 1959 , 1967 , 2004 , 2007 , 2015 , 2016 ) 30 Uruguay 15 ( 1916 , 1917 * , 1920 , 1923 * , 1924 * , 1926 , 1935 , 1942 * , 1956 * , 1959 , 1967 * , 1983 , 1987 , 1995 * , 2011 ) 6 ( 1919 , 1927 , 1939 , 1941 , 1989 , 1999 ) 21 Brazil 9 ( 1919 * , 1922 * , 1949 * , 1989 * , 1997 , 1999 , 2004 , 2007 , 2019 * ) 11 ( 1921 , 1925 , 1937 , 1945 , 1946 , 1953 , 1957 , 1959 , 1983 , 1991 , 1995 , 2021 * ) 20 Paraguay 2 ( 1953 , 1979 ) 6 ( 1922 , 1929 , 1947 , 1949 , 1963 , 2011 ) 8 Chile 2 ( 2015 * , 2016 ) 4 ( 1955 * , 1956 , 1979 , 1987 ) 6 Peru 2 ( 1939 * , 1975 ) 1 ( 2019 ) 3 Colombia 1 ( 2001 * ) 2 ( 1975 , 2024 ) 3 Bolivia 1 ( 1963 * ) 1 ( 1997 * ) 2 Mexico — 2 ( 1993 , 2001\n\n[DOC 6] er to win Premier League Player of the Month and Manager of the Month, after Gareth Southgate and Stuart Pearce . Lampard eventually guided Chelsea to fourth in the Premier League and the FA Cup Final , where they lost to Arsenal . In the following season , Chelsea made five major acquisitions in the summer transfer window in Hakim Ziyech , Timo Werner , Ben Chilwell , Kai Havertz and Édouard Mendy . Chelsea initially started strongly, topping their Champions League group and the Premier League in early December. However, after a run of two wins in eight Premier League matches, Chelsea dropped to ninth and Lampard was dismissed as manager on 25 January 2021. He remained on Chelsea's payroll to see out his contract, \"pocketing £75,000\" per week in compensatio\n\n[DOC 7] 65 Honours Drogba holding the European Cup following Chelsea's penalty shootout victory over Bayern Munich Drogba banner made by Chelsea's fans Marseille UEFA Cup runner-up: 2003–04 Chelsea Premier League : 2004–05 , 2005–06 , 2009–10 , 2014–15 FA Cup : 2006–07 , 2008–09 , 2009–10 , 2011–12 Football League Cup : 2004–05 , 2006–07 , 2014–15 ; runner-up: 2007–08 FA Community Shield : 2005 , 2009 UEFA Champions League : 2011–12 Galatasaray Süper Lig : 2012–13 Turkish Cup : 2013–14 Turkish Super Cup : 2013 Phoenix Rising Western Conference (USL) : 2018 Ivory Coast Africa Cup of Nations runner-up: 2006 , 2012 Individual Africa Cup of Nations Team of the Tournament: 2006 , 2012 Africa Cup of Nations Top Scorer: 2012 African Footballer of the Year : 2006, 2009 Alan\n\n[DOC 8] 65 Honours Drogba holding the European Cup following Chelsea's penalty shootout victory over Bayern Munich Drogba banner made by Chelsea's fans Marseille UEFA Cup runner-up: 2003–04 Chelsea Premier League : 2004–05 , 2005–06 , 2009–10 , 2014–15 FA Cup : 2006–07 , 2008–09 , 2009–10 , 2011–12 Football League Cup : 2004–05 , 2006–07 , 2014–15 ; runner-up: 2007–08 FA Community Shield : 2005 , 2009 UEFA Champions League : 2011–12 Galatasaray Süper Lig : 2012–13 Turkish Cup : 2013–14 Turkish Super Cup : 2013 Phoenix Rising Western Conference (USL) : 2018 Ivory Coast Africa Cup of Nations runner-up: 2006 , 2012 Individual Africa Cup of Nations Team of the Tournament: 2006 , 2012 Africa Cup of Nations Top Scorer: 2012 African Footballer of the Year : 2006, 2009 Alan\n\n[DOC 9] 20 June 2018 Kazan Arena , Kazan , Russia 22 Iran 1–0 1–0 2018 FIFA World Cup Honours Chelsea supporters' banner in honour of Costa, November 2014 Atlético Madrid La Liga : 2013–14 , 2020–21 Copa del Rey : 2012–13 UEFA Europa League : 2017–18 UEFA Super Cup : 2010 , 2012 , 2018 UEFA Champions League runner-up: 2013–14 Chelsea Premier League : 2014–15 , 2016–17 Football League Cup : 2014–15 FA Cup runner-up: 2016–17 Atlético Mineiro Campeonato Brasileiro Série A : 2021 Copa do Brasil : 2021 Grêmio Campeonato Gaúcho : 2024 Individual La Liga Player of the Month : September 2013 La Liga Team of the Season : 2013–14 Trofeo EFE : 2013–14 UEFA Champions League Team of the Season: 2013–14 Zarra Trophy : 2013–14 Premier League Player of the Month : August 2014 , Nov\n\n[DOC 10] 26 FIFA World Cup GS Brazil v Haiti Philadelphia , United States 21:00 UTC−4 Report Stadium: Lincoln Financial Field Scotland v Brazil 24 June 2026 2026 FIFA World Cup GS Scotland v Brazil Miami Gardens , United States 18:00 UTC−4 Report Stadium: Hard Rock Stadium Coaching staff Carlo Ancelotti, the head coach of the Brazil national football team since 26 May 2025, after leaving his former club Real Madrid. Position Name Ref Head coach Carlo Ancelotti Assistant coach Paul Clement Goalkeeping coaches Cláudio Taffarel Marco Antônio Trocourt Physical coach Francesco Mauri Match analysts Simone Montanaro Guilherme Lyra João Marcos Soares Thomaz Koerich Performance analyst Mino Fulco Physiologist Guilherme Passos Doctor Rodrigo Lasmar Team coordinator Juan Sporti\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"sufficient\": true, \"reasoning\": \"Document 7 (and 8) confirms Chelsea, a London club, won the UEFA Champions League in the 2011–12 season. Document 4 lists the 2010 FIFA World Cup winner as Spain, making Spain the World Cup holders at the time of the 2012 Champions League victory. These facts together answer the question: the country holding the FIFA World Cup when a London club last won the Champions League was Spain.\"}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2633, + "osl": 100, + "total_tokens": 2733, + "latency_ms": 9673.69, + "tokens_per_second": 10.34 + }, + "context": { + "kept_docs_count": 10, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "e1ea6ec8-7b1a-43a6-a4a7-e74b34f86519", + "component": "answer_generator", + "hop_count": 3, + "timestamp": "2026-05-16T02:31:40.376246Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: As of August 1, 2024, which country were holders of the FIFA World Cup the last time the UEFA Champions League was won by a club from London?\n\nDOCUMENTS:\n\n[DOC 1] all of their matches in a single tournament en route to their tournament victory: Bayern Munich in the 2019–20 season. Paris Saint-Germain are the current European champions, having beaten Inter Milan 5–0 in the 2025 final for their first ever title. History Winners European Cup / UEFA Champions League Season Winners European Cup 1955–56 Real Madrid 1956–57 Real Madrid (2) 1957–58 Real Madrid (3) 1958–59 Real Madrid (4) 1959–60 Real Madrid (5) 1960–61 Benfica 1961–62 Benfica (2) 1962–63 AC Milan 1963–64 Inter Milan 1964–65 Inter Milan (2) 1965–66 Real Madrid (6) 1966–67 Celtic 1967–68 Manchester United 1968–69 AC Milan (2) 1969–70 Feyenoord 1970–71 Ajax 1971–72 Ajax (2) 1972–73 Ajax (3) 1973–74 Bayern Munich 1974–75 Bayern Munich (2) 1975–76 Bayern Munich (\n\n[DOC 2] Association football tournament in Russia 2018 FIFA World Cup Чемпионат мира по футболу FIFA 2018 ( Russian ) Chempionat mira po futbolu FIFA 2018 Играй с открытым сердцем Igray s otkrytym serdtsem \"Play with an open heart\" Tournament details Host country Russia Dates 14 June – 15 July Teams 32 (from 5 confederations) Venue 12 (in 11 host cities) Final positions Champions France (2nd title) Runners-up Croatia Third place Belgium Fourth place England Tournament statistics Matches played 64 Goals scored 169 (2.64 per match) Attendance 3,031,768 (47,371 per match) Top scorer Harry Kane (6 goals) Best player Luka Modrić Best young player Kylian Mbappé Best goalkeeper Thibaut Courtois Fair play award Spain ← 2014 2022 → International football competition The 2018\n\n[DOC 3] pted Nemo , a black Labrador Retriever-Griffon dog who lives with them in the Élysée Palace. When he was a schoolboy, Macron decided to be baptised as a Catholic. In June 2018, prior to meeting Pope Francis , he identified himself as an agnostic Catholic . In the same year he agreed to become an honorary canon of St John Lateran , the cathedral of Rome. Macron celebrating France's victory over Croatia in the 2018 World Cup final in Moscow, Russia A fan of football , Macron is a supporter of French club Olympique de Marseille . During the 2018 World Cup , he attended the semi-final between France and Belgium with the Belgian King Philippe and Queen Mathilde ; and at the World Cup final against Croatia , he sat and celebrated alongside Croatian president Kolin\n\n[DOC 4] France 2002 South Korea/Japan Oliver Kahn Ronaldo 8 Oliver Kahn 5 Landon Donovan Belgium 2006 Germany Zinedine Zidane Miroslav Klose 5 Gianluigi Buffon 5 Lukas Podolski Brazil Spain 2010 South Africa Diego Forlán Thomas Müller 5 Iker Casillas 5 Thomas Müller Spain 2014 Brazil Lionel Messi James Rodríguez 6 Manuel Neuer 4 Paul Pogba Colombia 2018 Russia Luka Modrić Harry Kane 6 Thibaut Courtois 3 Kylian Mbappé Spain 2022 Qatar Lionel Messi Kylian Mbappé 8 Emiliano Martínez 3 Enzo Fernández England See also List of FIFA World Cup finals FIFA World Cup records and statistics FIFA World Cup awards FIFA U-20 World Cup FIFA U-17 World Cup FIFA Club World Cup FIFA Beach Soccer World Cup FIFA Futsal World Cup FIFA Confederations Cup List of association football comp\n\n[DOC 5] , 1993 , 2021 , 2024 ) 14 ( 1916 * , 1917 , 1920 , 1923 , 1924 , 1926 , 1935 , 1942 , 1959 , 1967 , 2004 , 2007 , 2015 , 2016 ) 30 Uruguay 15 ( 1916 , 1917 * , 1920 , 1923 * , 1924 * , 1926 , 1935 , 1942 * , 1956 * , 1959 , 1967 * , 1983 , 1987 , 1995 * , 2011 ) 6 ( 1919 , 1927 , 1939 , 1941 , 1989 , 1999 ) 21 Brazil 9 ( 1919 * , 1922 * , 1949 * , 1989 * , 1997 , 1999 , 2004 , 2007 , 2019 * ) 11 ( 1921 , 1925 , 1937 , 1945 , 1946 , 1953 , 1957 , 1959 , 1983 , 1991 , 1995 , 2021 * ) 20 Paraguay 2 ( 1953 , 1979 ) 6 ( 1922 , 1929 , 1947 , 1949 , 1963 , 2011 ) 8 Chile 2 ( 2015 * , 2016 ) 4 ( 1955 * , 1956 , 1979 , 1987 ) 6 Peru 2 ( 1939 * , 1975 ) 1 ( 2019 ) 3 Colombia 1 ( 2001 * ) 2 ( 1975 , 2024 ) 3 Bolivia 1 ( 1963 * ) 1 ( 1997 * ) 2 Mexico — 2 ( 1993 , 2001\n\n[DOC 6] er to win Premier League Player of the Month and Manager of the Month, after Gareth Southgate and Stuart Pearce . Lampard eventually guided Chelsea to fourth in the Premier League and the FA Cup Final , where they lost to Arsenal . In the following season , Chelsea made five major acquisitions in the summer transfer window in Hakim Ziyech , Timo Werner , Ben Chilwell , Kai Havertz and Édouard Mendy . Chelsea initially started strongly, topping their Champions League group and the Premier League in early December. However, after a run of two wins in eight Premier League matches, Chelsea dropped to ninth and Lampard was dismissed as manager on 25 January 2021. He remained on Chelsea's payroll to see out his contract, \"pocketing £75,000\" per week in compensatio\n\n[DOC 7] 65 Honours Drogba holding the European Cup following Chelsea's penalty shootout victory over Bayern Munich Drogba banner made by Chelsea's fans Marseille UEFA Cup runner-up: 2003–04 Chelsea Premier League : 2004–05 , 2005–06 , 2009–10 , 2014–15 FA Cup : 2006–07 , 2008–09 , 2009–10 , 2011–12 Football League Cup : 2004–05 , 2006–07 , 2014–15 ; runner-up: 2007–08 FA Community Shield : 2005 , 2009 UEFA Champions League : 2011–12 Galatasaray Süper Lig : 2012–13 Turkish Cup : 2013–14 Turkish Super Cup : 2013 Phoenix Rising Western Conference (USL) : 2018 Ivory Coast Africa Cup of Nations runner-up: 2006 , 2012 Individual Africa Cup of Nations Team of the Tournament: 2006 , 2012 Africa Cup of Nations Top Scorer: 2012 African Footballer of the Year : 2006, 2009 Alan\n\n[DOC 8] 65 Honours Drogba holding the European Cup following Chelsea's penalty shootout victory over Bayern Munich Drogba banner made by Chelsea's fans Marseille UEFA Cup runner-up: 2003–04 Chelsea Premier League : 2004–05 , 2005–06 , 2009–10 , 2014–15 FA Cup : 2006–07 , 2008–09 , 2009–10 , 2011–12 Football League Cup : 2004–05 , 2006–07 , 2014–15 ; runner-up: 2007–08 FA Community Shield : 2005 , 2009 UEFA Champions League : 2011–12 Galatasaray Süper Lig : 2012–13 Turkish Cup : 2013–14 Turkish Super Cup : 2013 Phoenix Rising Western Conference (USL) : 2018 Ivory Coast Africa Cup of Nations runner-up: 2006 , 2012 Individual Africa Cup of Nations Team of the Tournament: 2006 , 2012 Africa Cup of Nations Top Scorer: 2012 African Footballer of the Year : 2006, 2009 Alan\n\n[DOC 9] 20 June 2018 Kazan Arena , Kazan , Russia 22 Iran 1–0 1–0 2018 FIFA World Cup Honours Chelsea supporters' banner in honour of Costa, November 2014 Atlético Madrid La Liga : 2013–14 , 2020–21 Copa del Rey : 2012–13 UEFA Europa League : 2017–18 UEFA Super Cup : 2010 , 2012 , 2018 UEFA Champions League runner-up: 2013–14 Chelsea Premier League : 2014–15 , 2016–17 Football League Cup : 2014–15 FA Cup runner-up: 2016–17 Atlético Mineiro Campeonato Brasileiro Série A : 2021 Copa do Brasil : 2021 Grêmio Campeonato Gaúcho : 2024 Individual La Liga Player of the Month : September 2013 La Liga Team of the Season : 2013–14 Trofeo EFE : 2013–14 UEFA Champions League Team of the Season: 2013–14 Zarra Trophy : 2013–14 Premier League Player of the Month : August 2014 , Nov\n\n[DOC 10] 26 FIFA World Cup GS Brazil v Haiti Philadelphia , United States 21:00 UTC−4 Report Stadium: Lincoln Financial Field Scotland v Brazil 24 June 2026 2026 FIFA World Cup GS Scotland v Brazil Miami Gardens , United States 18:00 UTC−4 Report Stadium: Hard Rock Stadium Coaching staff Carlo Ancelotti, the head coach of the Brazil national football team since 26 May 2025, after leaving his former club Real Madrid. Position Name Ref Head coach Carlo Ancelotti Assistant coach Paul Clement Goalkeeping coaches Cláudio Taffarel Marco Antônio Trocourt Physical coach Francesco Mauri Match analysts Simone Montanaro Guilherme Lyra João Marcos Soares Thomaz Koerich Performance analyst Mino Fulco Physiologist Guilherme Passos Doctor Rodrigo Lasmar Team coordinator Juan Sporti\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Spain.", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2490, + "osl": 590, + "total_tokens": 3080, + "latency_ms": 7938.59, + "tokens_per_second": 74.32 + }, + "context": { + "num_documents_used": 10, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:31:40.377073Z", + "summary": { + "total_llm_calls": 7, + "total_hops": 3, + "total_input_tokens": 13337, + "total_output_tokens": 4536, + "total_tokens": 17873, + "total_latency_ms": 66740.89, + "average_tokens_per_second": 68.54, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/FIFA_World_Cup", + "https://en.wikipedia.org/wiki/London", + "https://en.wikipedia.org/wiki/UEFA_Champions_League" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Spain.", + "ground_truth_answer": "France" + } + }, + { + "query_id": "4", + "original_query": "What is the name of the vocalist from the first band to make it in the top 200 under the record label that produced the third studio album for Dismal Euphony?", + "timestamp_start": "2026-05-16T02:31:40.380625Z", + "llm_calls": [ + { + "call_id": "a0d30b54-ad1c-4aaa-b728-b43349db4b41", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:32:09.391057Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What is the name of the vocalist from the first band to make it in the top 200 under the record label that produced the third studio album for Dismal Euphony?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Dismal Euphony third album\",\n \"Dismal Euphony record label\"\n ],\n \"feedback\": \"First query aims to identify the third studio album of Dismal Euphony, which will reveal the record label that produced it. The second query directly seeks the record label associated with Dismal Euphony. Once the label is known, a subsequent query can target the first band that entered the Billboard Top 200 under that label to retrieve the vocalist's name.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 459, + "osl": 1422, + "total_tokens": 1881, + "latency_ms": 29010.26, + "tokens_per_second": 49.02 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "354c03f6-04ef-4f19-8dfe-ef763ff8fc0f", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:32:17.007719Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: What is the name of the vocalist from the first band to make it in the top 200 under the record label that produced the third studio album for Dismal Euphony?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] 1999 studio album by Dismal Euphony All Little Devils The cover was designed by Thomas Ewerhard and its text was designed by Vibeke Tveiten. Studio album by Dismal Euphony Released 22 March 1999 Recorded The Woodhouse Studios in September 1998 Genre Symphonic black metal , heavy metal Length 39 : 00 Language English Label Nuclear Blast Records Producer Waldemar Sorychta Professional ratings Review scores Source Rating AllMusic All Little Devils is the third studio album by the Norwegian gothic metal band Dismal Euphony . It was released in 1999, and was the band's first album with Nuclear Blast . Track listing \"Days of Sodom\" (5:22) \"Rage of Fire\" (3:44) \"Victory\" (4:31) \"All Little Devils\" (4:12) \"Lunatic\" (4:19) \"Psycho Path\" (4:03) \"Shine for Me, Misery\"\n\n[NEW 2] Norwegian metal band Dismal Euphony Background information Origin Stavanger , Norway Genres Symphonic black metal , melodic black metal , gothic metal Years active 1994–2001 Labels Nuclear Blast , Napalm Past members See below Dismal Euphony was a Norwegian dark metal band that mixed styles including gothic metal , black metal , death metal , melodic metal, and classical music . History The history of Dismal Euphony began in 1992 in Stavanger , Norway by bassist Ole K. Helgesen and drummer Kristoffer Vold Austrheim under the name The Headless Children, a cover band of Slayer and Kreator . A year later, singer Erik Borgen and guitarist Kenneth Bergsagel joined the group. They changed the name to Carnal Tomb, then Borgen left the band and Helgesen became the s\n\n[NEW 3] he precision-timing drones in German rock of the same period.\" The band's next album, Rated R , has a wider variety of instruments, several recording guests , and lead vocals shared by Homme, Oliveri and Lanegan. Homme has said, \"Our first record announced our sound. This one added that we're different and weird.\" The band continued to experiment on its third album, Songs for the Deaf , which also featured a lineup of three lead vocalists, many guest appearances, and wide range of instrumentation, including horn and string sections. Homme has called Lullabies to Paralyze a \"dark\" album, which includes imagery inspired by The Brothers Grimm folk and fairy tales . In 2005, Homme said, \"Where the poetry seems to be is when you start in the dark and reach for th\n\n[NEW 4] s Digital Garbage . In 2021, Mudhoney and Sub Pop celebrated the 30-year anniversary of Every Good Boy Deserves Fudge with a remastered deluxe version. This included remastered and re-released music videos and songs, as well as previously unreleased songs and demos. Plastic Eternity was released by Sub Pop on April 6, 2023. Musical style and influences Mark Deming of AllMusic called Mudhoney \"a group with a penchant for metal muscle, punk attitude, and garage rock primitivism.\" He further described the band's discography as \"big, loud, purposefully sloppy, a little bit menacing, and even more funny.\" Mudhoney have cited numerous bands and artists as influences, including the Stooges , Spacemen 3 , Wipers , Dinosaur Jr. , Neil Young , Black Flag , Feedtime ,\n\n[NEW 5] eet Oblivion (1992) Dust (1996) Last Words: The Final Recordings (2011) References ^ ^ ^ ^ ^ ^ a b c d e f g h i j k l ^ ^ a b c ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ External links The official Screaming Trees page Official Facebook page Screaming Trees discography at Discogs Screaming Trees at AllMusic Screaming Trees at IMDb\n\n[NEW 6] 09–present) Machine Shop Records (2001–present) Masked Records (2018–present) Mind of a Genius (2016–present) Nonesuch Records (2004–present) Parlophone (2014–present) REMember Music (2014–present) Reprise Records (1963–present) Sire Records (1978–1995, 2003–present) Tha Lights Global The Benton Music Records (2018–present) Word Records (2002–present) Former 1017 Brick Squad Records 143 Records 4AD Records (1992–1998) (US only) Action Theory Records American Recordings (1988–1997 [US], 2005–2007 [worldwide]) Autumn Records (1963–1965) Bearsville Records (1970–1984) BME Recordings Blacksmith Records (2005–2008) Brute/Beaute Records (2004–2007) Capricorn Records (1972–1977), (1990–1995) Chrysalis Records (1972–1976) (US only) Cold Chillin' Records (1987–1994)\n\n[NEW 7] 09–present) Machine Shop Records (2001–present) Masked Records (2018–present) Mind of a Genius (2016–present) Nonesuch Records (2004–present) Parlophone (2014–present) REMember Music (2014–present) Reprise Records (1963–present) Sire Records (1978–1995, 2003–present) Tha Lights Global The Benton Music Records (2018–present) Word Records (2002–present) Former 1017 Brick Squad Records 143 Records 4AD Records (1992–1998) (US only) Action Theory Records American Recordings (1988–1997 [US], 2005–2007 [worldwide]) Autumn Records (1963–1965) Bearsville Records (1970–1984) BME Recordings Blacksmith Records (2005–2008) Brute/Beaute Records (2004–2007) Capricorn Records (1972–1977), (1990–1995) Chrysalis Records (1972–1976) (US only) Cold Chillin' Records (1987–1994)\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 7 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 1, 0, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1610, + "osl": 606, + "total_tokens": 2216, + "latency_ms": 7223.49, + "tokens_per_second": 83.89 + }, + "context": { + "num_documents_evaluated": 7, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "1370256f-96a4-41e2-a502-70b7026b37c5", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:32:18.982986Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What is the name of the vocalist from the first band to make it in the top 200 under the record label that produced the third studio album for Dismal Euphony?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] 1999 studio album by Dismal Euphony All Little Devils The cover was designed by Thomas Ewerhard and its text was designed by Vibeke Tveiten. Studio album by Dismal Euphony Released 22 March 1999 Recorded The Woodhouse Studios in September 1998 Genre Symphonic black metal , heavy metal Length 39 : 00 Language English Label Nuclear Blast Records Producer Waldemar Sorychta Professional ratings Review scores Source Rating AllMusic All Little Devils is the third studio album by the Norwegian gothic metal band Dismal Euphony . It was released in 1999, and was the band's first album with Nuclear Blast . Track listing \"Days of Sodom\" (5:22) \"Rage of Fire\" (3:44) \"Victory\" (4:31) \"All Little Devils\" (4:12) \"Lunatic\" (4:19) \"Psycho Path\" (4:03) \"Shine for Me, Misery\"\n\n[DOC 2] Norwegian metal band Dismal Euphony Background information Origin Stavanger , Norway Genres Symphonic black metal , melodic black metal , gothic metal Years active 1994–2001 Labels Nuclear Blast , Napalm Past members See below Dismal Euphony was a Norwegian dark metal band that mixed styles including gothic metal , black metal , death metal , melodic metal, and classical music . History The history of Dismal Euphony began in 1992 in Stavanger , Norway by bassist Ole K. Helgesen and drummer Kristoffer Vold Austrheim under the name The Headless Children, a cover band of Slayer and Kreator . A year later, singer Erik Borgen and guitarist Kenneth Bergsagel joined the group. They changed the name to Carnal Tomb, then Borgen left the band and Helgesen became the s\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents provide the record label (Nuclear Blast) for Dismal Euphony's third studio album, but they contain no information about any other band that made it into the top 200 under Nuclear Blast, nor the vocalist of such a band. Therefore, the required facts to answer the question are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 708, + "osl": 278, + "total_tokens": 986, + "latency_ms": 1974.29, + "tokens_per_second": 140.81 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "6ccad67e-6843-42e8-b4fb-b828995d32ef", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:32:26.858929Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What is the name of the vocalist from the first band to make it in the top 200 under the record label that produced the third studio album for Dismal Euphony?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] 1999 studio album by Dismal Euphony All Little Devils The cover was designed by Thomas Ewerhard and its text was designed by Vibeke Tveiten. Studio album by Dismal Euphony Released 22 March 1999 Recorded The Woodhouse Studios in September 1998 Genre Symphonic black metal , heavy metal Length 39 : 00 Language English Label Nuclear Blast Records Producer Waldemar Sorychta Professional ratings Review scores Source Rating AllMusic All Little Devils is the third studio album by the Norwegian gothic metal band Dismal Euphony . It was released in 1999, and was the band's first album with Nuclear Blast . Track listing \"Days of Sodom\" (5:22) \"Rage of Fire\" (3:44) \"Victory\" (4:31) \"All Little Devils\" (4:12) \"Lunatic\" (4:19) \"Psycho Path\" (4:03) \"Shine for Me, Misery\"\n\n[DOC 2] Norwegian metal band Dismal Euphony Background information Origin Stavanger , Norway Genres Symphonic black metal , melodic black metal , gothic metal Years active 1994–2001 Labels Nuclear Blast , Napalm Past members See below Dismal Euphony was a Norwegian dark metal band that mixed styles including gothic metal , black metal , death metal , melodic metal, and classical music . History The history of Dismal Euphony began in 1992 in Stavanger , Norway by bassist Ole K. Helgesen and drummer Kristoffer Vold Austrheim under the name The Headless Children, a cover band of Slayer and Kreator . A year later, singer Erik Borgen and guitarist Kenneth Bergsagel joined the group. They changed the name to Carnal Tomb, then Borgen left the band and Helgesen became the s\n\n\nSEARCH HISTORY: - Query: 'Dismal Euphony third album' → 5 docs\n- Query: 'Dismal Euphony record label' → 2 docs\n\nFEEDBACK: First query aims to identify the third studio album of Dismal Euphony, which will reveal the record label that produced it. The second query directly seeks the record label associated with Dismal Euphony. Once the label is known, a subsequent query can target the first band that entered the Billboard Top 200 under that label to retrieve the vocalist's name.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Nuclear Blast Billboard 200\",\n \"first Nuclear Blast band\",\n \"first Nuclear Blast chart\"\n ],\n \"feedback\": \"We have identified the record label (Nuclear Blast) from the third Dismal Euphony album. The next step is to locate the earliest band on that label that entered the Top 200, then retrieve that band's vocalist name.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 959, + "osl": 455, + "total_tokens": 1414, + "latency_ms": 7875.36, + "tokens_per_second": 57.78 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 2 + } + }, + { + "call_id": "2fbc3677-5ea0-45ed-84d2-18922170ff50", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T02:33:35.704203Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: What is the name of the vocalist from the first band to make it in the top 200 under the record label that produced the third studio album for Dismal Euphony?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] ds also charted on the Billboard 200, peaking at number 120. References ^ ^ ^ a b ^ ^ ^ ^ a b c d ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ a b c d e ^ a b Pillsbury 2006 , p. 2 ^ Christe 2003 , p. 86 ^ ^ ^ ^ a b Whitburn, Joel . Top Pop Albums (2001): 578 ^ ^ ^ a b ^ a b c d e ^ a b c ^ Christe 2003 , p. 130 ^ ^ ^ ^ a b ^ a b ^ ^ ^ ^ Christe 2003 , p. 196 ^ ^ ^ ^ a b \"Rockin' on an Island\". Kerrang! 258 . September 30, 1989. ^ ^ ^ ^ a b ^ ^ ^ ^ ^ a b Georg Purvis (2007). \"Queen: Complete Works\". p. 390. Reynolds & Hearn ^ ^ a b ^ a b ^ ^ a b c ^ a b c ^ a b c d e Some Kind of Monster feature film, Metallica, January 2004, Paramount Pictures. ^ a b ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^\n\n[NEW 2] 4 US Billboard 200 32 Certifications Region Certification Certified units /sales Japan ( RIAJ ) Gold 100,000 ^ ^ Shipments figures based on certification alone. References ^ ^ ^ a b ^ a b Wilson 2009 , pp. 145 ^ a b c Wilson 2009 , pp. 146 ^ Wilson 2009 , pp. 147 ^ Wilson 2009 , pp. 159–60 ^ a b Wilson 2009 , pp. 148 ^ a b c Wilson 2009 , pp. 149 ^ a b Wilson 2009 , pp. 150 ^ Wilson 2009 , pp. 16 ^ a b Wilson 2009 , pp. 151 ^ a b c Wilson 2009 , pp. 152 ^ a b c d e Wilson 2009 , pp. 153 ^ a b Wilson 2009 , pp. 149–50 ^ a b c d Wilson 2009 , pp. 160 ^ a b Wilson 2009 , pp. 161 ^ Wilson 2009 , pp. 371 ^ ^ a b c Wilson 2009 , pp. 159 ^ Wilson 2009 , pp. 160–1 ^ ^ a b c d e f Wilson 2009 , pp. 162 ^ {{ cite AV media notes }} : CS1 maint: others in cite AV media\n\n[NEW 3] ts Company) 11 US Hot Rock Songs ( Billboard ) 25 US Rock Airplay ( Billboard ) 16 Chart (2014) Position Brazil ( Crowley ) 50 Canada (Canadian Hot 100) 14 Italy (FIMI) 78 Netherlands (Single Top 100) 75 Sweden (Sverigetopplistan) 73 UK Singles (Official Charts Company) 79 US Billboard Hot 100 12 US Adult Contemporary ( Billboard ) 25 US Adult Top 40 ( Billboard ) 2 US Dance Club Songs ( Billboard ) 30 US Dance/Mix Show Airplay ( Billboard ) 14 US Mainstream Top 40 ( Billboard ) 11 US Hot Rock Songs ( Billboard ) 1 US Rock Airplay ( Billboard ) 6 Chart (2016) Position Brazil ( Brasil Hot 100 ) 50 Decade-end charts 2010s chart rankings for \"Pompeii\" Chart (2010–2019) Position UK Singles (Official Charts Company) 21 US Hot Rock Songs ( Billboard ) 11 Certifica\n\n[NEW 4] US Billboard 200 120 1993 Australian Albums Chart 55 2004 Canadian Metal Albums Chart ( Nielsen Soundscan ) 90 Finnish Albums Chart 19 French Albums Chart 149 Swedish Albums Chart 28 2007 Finnish Albums Chart 12 2008 Spanish Albums Chart 70 Swiss Albums Chart 65 2011 Swedish Albums Chart 39 2012 French Albums Chart 180 2016 German Albums Chart 58 Spanish Albums Chart 82 US Billboard 200 66 2017 Spanish Albums Chart 47 2018 Spanish Albums Chart 55 2019 French Albums Chart 179 2021 Polish Albums ( ZPAV ) 13 US Top Rock Albums ( Billboard ) 18 2023 chart performance for Kill 'Em All Chart (2023) Peak position Austrian Albums ( Ö3 Austria ) 54 German Albums ( Offizielle Top 100 ) 17 Hungarian Physical Albums ( MAHASZ ) 33 Swiss Albums ( Schweizer Hitparade ) 31\n\n[NEW 5] Official Finnish Charts ) 23 German Albums ( Offizielle Top 100 ) 16 Italian Albums ( Musica e Dischi ) 13 Japanese Albums ( Oricon ) 6 New Zealand Albums ( RMNZ ) 6 Norwegian Albums ( VG-lista ) 21 Spanish Albums Chart 26 Swedish Albums ( Sverigetopplistan ) 29 UK Albums ( OCC ) 1 US Billboard 200 2 2007 weekly chart performance for The Song Remains the Same Chart (2007) Peak position US Billboard Top Digital Albums Chart 24 US Billboard Top Internet Albums Chart 18 US Top Hard Rock Albums ( Billboard ) 11 US Indie Store Album Sales ( Billboard ) 11 2018 weekly chart performance for The Song Remains the Same Chart (2018) Peak position Austrian Albums ( Ö3 Austria ) 37 Belgian Albums ( Ultratop Flanders) 41 Belgian Albums ( Ultratop Wallonia) 37 Finnish Albu\n\n[NEW 6] S1 maint: numeric names: authors list ( link ) ^ ^ ^ a b ^ ^ External links Nuclear Blast Europe\n\n[NEW 7] American heavy metal band Anthrax Anthrax performing at Wacken Open Air 2019 Background information Origin Queens , New York City, U. S. Genres Thrash metal speed metal groove metal alternative metal Works Discography Years active 1981–present Labels Megaforce Island Elektra Ignition Tommy Boy Beyond Sanctuary Nuclear Blast Spinoffs Stormtroopers of Death Members Scott Ian Charlie Benante Frank Bello Joey Belladonna Jonathan Donais Past members See List of Anthrax members Website anthrax . com Logo Anthrax is an American heavy metal band from New York City, formed in 1981 by rhythm guitarist Scott Ian and bassist Dan Lilker . The group is considered one of the leaders of the thrash metal scene from the 1980s and is part of the \"Big Four\" of the genre, along\n\n[NEW 8] tem 1951 Greenhouse George 1951 Ivy Mike 1952 Soviet Union RDS-1 1949 RDS-6s 1953 RDS-37 1955 RDS-37 1955 United Kingdom Operation Hurricane 1952 Mosaic G1 1956 Grapple 1 1957 Grapple X 1957 China 596 1964 596L 1966 629 1966 639 1967 France Gerboise Bleue 1960 Rigel 1966 Canopus 1968 Canopus 1968 India Smiling Buddha 1974 Shakti I (unconfirmed) 1998 Shakti I (unconfirmed) 1998 n/a Pakistan Chagai I 1998 Chagai I 1998 n/a n/a North Korea #1 2006 #4 (unconfirmed) 2016 #6 (unconfirmed) 2017 n/a Israel See Nuclear weapons and Israel § Nuclear testing n/a South Africa See South Africa and weapons of mass destruction § Nuclear weapons n/a Types The Trinity test of the Manhattan Project was the first detonation of a nuclear weapon, which led J. Robert Oppenheimer t\n\n[NEW 9] sed device (see Trinity test and \"Fat Man\") whose plutonium was derived from uranium-238. Little Boy became the first nuclear weapon used in war when it was detonated over Hiroshima , Japan , on 6 August 1945. Exploding with a yield equivalent to 12,500 tonnes of TNT , the blast and thermal wave of the bomb destroyed nearly 50,000 buildings and killed about 75,000 people (see Atomic bombings of Hiroshima and Nagasaki ). In 1943 the Manhattan Project contracted two private companies, Union Carbide and Chevron , to quietly compile a survey of uranium deposits around the world. As the survey results came in, two geology professors studied the results and suggested general guidelines for new sources, including uranium associated with gold mines in the Rand area\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] 1999 studio album by Dismal Euphony All Little Devils The cover was designed by Thomas Ewerhard and its text was designed by Vibeke Tveiten. Studio album by Dismal Euphony Released 22 March 1999 Recorded The Woodhouse Studios in September 1998 Genre Symphonic black metal , heavy metal Length 39 : 00...\n[KEPT 2] Norwegian metal band Dismal Euphony Background information Origin Stavanger , Norway Genres Symphonic black metal , melodic black metal , gothic metal Years active 1994–2001 Labels Nuclear Blast , Napalm Past members See below Dismal Euphony was a Norwegian dark metal band that mixed styles includin...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 9 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0, 0, 1, 1, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2285, + "osl": 1355, + "total_tokens": 3640, + "latency_ms": 68245.37, + "tokens_per_second": 19.85 + }, + "context": { + "num_documents_evaluated": 9, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "d8c3e748-e786-4091-a316-d1478464b61d", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T02:33:37.657795Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What is the name of the vocalist from the first band to make it in the top 200 under the record label that produced the third studio album for Dismal Euphony?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] 1999 studio album by Dismal Euphony All Little Devils The cover was designed by Thomas Ewerhard and its text was designed by Vibeke Tveiten. Studio album by Dismal Euphony Released 22 March 1999 Recorded The Woodhouse Studios in September 1998 Genre Symphonic black metal , heavy metal Length 39 : 00 Language English Label Nuclear Blast Records Producer Waldemar Sorychta Professional ratings Review scores Source Rating AllMusic All Little Devils is the third studio album by the Norwegian gothic metal band Dismal Euphony . It was released in 1999, and was the band's first album with Nuclear Blast . Track listing \"Days of Sodom\" (5:22) \"Rage of Fire\" (3:44) \"Victory\" (4:31) \"All Little Devils\" (4:12) \"Lunatic\" (4:19) \"Psycho Path\" (4:03) \"Shine for Me, Misery\"\n\n[DOC 2] Norwegian metal band Dismal Euphony Background information Origin Stavanger , Norway Genres Symphonic black metal , melodic black metal , gothic metal Years active 1994–2001 Labels Nuclear Blast , Napalm Past members See below Dismal Euphony was a Norwegian dark metal band that mixed styles including gothic metal , black metal , death metal , melodic metal, and classical music . History The history of Dismal Euphony began in 1992 in Stavanger , Norway by bassist Ole K. Helgesen and drummer Kristoffer Vold Austrheim under the name The Headless Children, a cover band of Slayer and Kreator . A year later, singer Erik Borgen and guitarist Kenneth Bergsagel joined the group. They changed the name to Carnal Tomb, then Borgen left the band and Helgesen became the s\n\n[DOC 3] S1 maint: numeric names: authors list ( link ) ^ ^ ^ a b ^ ^ External links Nuclear Blast Europe\n\n[DOC 4] American heavy metal band Anthrax Anthrax performing at Wacken Open Air 2019 Background information Origin Queens , New York City, U. S. Genres Thrash metal speed metal groove metal alternative metal Works Discography Years active 1981–present Labels Megaforce Island Elektra Ignition Tommy Boy Beyond Sanctuary Nuclear Blast Spinoffs Stormtroopers of Death Members Scott Ian Charlie Benante Frank Bello Joey Belladonna Jonathan Donais Past members See List of Anthrax members Website anthrax . com Logo Anthrax is an American heavy metal band from New York City, formed in 1981 by rhythm guitarist Scott Ian and bassist Dan Lilker . The group is considered one of the leaders of the thrash metal scene from the 1980s and is part of the \"Big Four\" of the genre, along\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents identify Nuclear Blast Records as the label for Dismal Euphony's third studio album, but they contain no information about which band was the first under that label to reach the top 200, nor the vocalist of that band. Therefore the required answer cannot be derived from the provided documents.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 910, + "osl": 318, + "total_tokens": 1228, + "latency_ms": 1952.46, + "tokens_per_second": 162.87 + }, + "context": { + "kept_docs_count": 4, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "a6628a88-c5ec-479a-8064-76c5308b664f", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T02:33:45.906689Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What is the name of the vocalist from the first band to make it in the top 200 under the record label that produced the third studio album for Dismal Euphony?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] 1999 studio album by Dismal Euphony All Little Devils The cover was designed by Thomas Ewerhard and its text was designed by Vibeke Tveiten. Studio album by Dismal Euphony Released 22 March 1999 Recorded The Woodhouse Studios in September 1998 Genre Symphonic black metal , heavy metal Length 39 : 00 Language English Label Nuclear Blast Records Producer Waldemar Sorychta Professional ratings Review scores Source Rating AllMusic All Little Devils is the third studio album by the Norwegian gothic metal band Dismal Euphony . It was released in 1999, and was the band's first album with Nuclear Blast . Track listing \"Days of Sodom\" (5:22) \"Rage of Fire\" (3:44) \"Victory\" (4:31) \"All Little Devils\" (4:12) \"Lunatic\" (4:19) \"Psycho Path\" (4:03) \"Shine for Me, Misery\"\n\n[DOC 2] Norwegian metal band Dismal Euphony Background information Origin Stavanger , Norway Genres Symphonic black metal , melodic black metal , gothic metal Years active 1994–2001 Labels Nuclear Blast , Napalm Past members See below Dismal Euphony was a Norwegian dark metal band that mixed styles including gothic metal , black metal , death metal , melodic metal, and classical music . History The history of Dismal Euphony began in 1992 in Stavanger , Norway by bassist Ole K. Helgesen and drummer Kristoffer Vold Austrheim under the name The Headless Children, a cover band of Slayer and Kreator . A year later, singer Erik Borgen and guitarist Kenneth Bergsagel joined the group. They changed the name to Carnal Tomb, then Borgen left the band and Helgesen became the s\n\n[DOC 3] S1 maint: numeric names: authors list ( link ) ^ ^ ^ a b ^ ^ External links Nuclear Blast Europe\n\n[DOC 4] American heavy metal band Anthrax Anthrax performing at Wacken Open Air 2019 Background information Origin Queens , New York City, U. S. Genres Thrash metal speed metal groove metal alternative metal Works Discography Years active 1981–present Labels Megaforce Island Elektra Ignition Tommy Boy Beyond Sanctuary Nuclear Blast Spinoffs Stormtroopers of Death Members Scott Ian Charlie Benante Frank Bello Joey Belladonna Jonathan Donais Past members See List of Anthrax members Website anthrax . com Logo Anthrax is an American heavy metal band from New York City, formed in 1981 by rhythm guitarist Scott Ian and bassist Dan Lilker . The group is considered one of the leaders of the thrash metal scene from the 1980s and is part of the \"Big Four\" of the genre, along\n\n\nSEARCH HISTORY: - Query: 'Dismal Euphony third album' → 5 docs\n- Query: 'Dismal Euphony record label' → 2 docs\n- Query: 'Nuclear Blast Billboard 200' → 5 docs\n- Query: 'first Nuclear Blast band' → 2 docs\n- Query: 'first Nuclear Blast chart' → 2 docs\n\nFEEDBACK: First query aims to identify the third studio album of Dismal Euphony, which will reveal the record label that produced it. The second query directly seeks the record label associated with Dismal Euphony. Once the label is known, a subsequent query can target the first band that entered the Billboard Top 200 under that label to retrieve the vocalist's name.\nWe have identified the record label (Nuclear Blast) from the third Dismal Euphony album. The next step is to locate the earliest band on that label that entered the Top 200, then retrieve that band's vocalist name.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Nuclear Blast Billboard 200 entries\",\n \"first Nuclear Blast band charted\",\n \"first Nuclear Blast top 200 vocalist\"\n ],\n \"feedback\": \"Need to identify the earliest Nuclear Blast‑signed band that entered the Billboard Top 200, then retrieve the name of that band’s vocalist.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1225, + "osl": 72, + "total_tokens": 1297, + "latency_ms": 8248.17, + "tokens_per_second": 8.73 + }, + "context": { + "kept_docs_count": 4, + "iteration_state": "generating_queries", + "query_history_length": 5 + } + }, + { + "call_id": "5e66220c-27e5-4690-acd2-460bdb24a228", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T02:34:01.496816Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: What is the name of the vocalist from the first band to make it in the top 200 under the record label that produced the third studio album for Dismal Euphony?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] kit Flip 634,000 July 17 334,500 July 24 264,000 July 31 Millennium † Backstreet Boys Jive 272,000 August 7 244,900 August 14 Significant Other Limp Bizkit Flip 250,241 August 21 Millennium † Backstreet Boys Jive 233,000 August 28 250,000 September 4 242,000 September 11 Christina Aguilera Christina Aguilera RCA 252,800 September 18 Fly Dixie Chicks Monument 341,138 September 25 203,000 October 2 Ruff Ryders' First Lady Eve Ruff Ryders 213,000 October 9 The Fragile Nine Inch Nails Nothing 228,746 October 16 Human Clay Creed Wind-Up 315,000 October 23 190,055 October 30 Supernatural Santana Arista 169,500 November 6 183,000 November 13 199,000 November 20 The Battle of Los Angeles Rage Against the Machine Epic 430,000 November 27 Breathe Faith Hill Warner Bro\n\n[NEW 2] ains – \" A Looking in View \" Ozzy Osbourne – \" Let Me Hear You Scream \" Soundgarden – \" Black Rain \" Stone Temple Pilots – \" Between the Lines \" Each year is linked to the article about the Grammy Awards held that year. Multiple wins 2 wins Foo Fighters Living Colour The Smashing Pumpkins Multiple nominations 8 nominations Alice in Chains 5 nominations Pearl Jam 4 nominations AC/DC Metallica Queens of the Stone Age Rage Against the Machine Stone Temple Pilots 3 nominations Foo Fighters Guns N' Roses Living Colour Mötley Crüe Nine Inch Nails The Smashing Pumpkins Soundgarden System of a Down 2 nominations Audioslave Buckcherry Evanescence Faith No More Godsmack Jane's Addiction Kid Rock Limp Bizkit Linkin Park Nickelback Ozzy Osbourne P. O. D. Robert Plant Re\n\n[NEW 3] nger ( No Sinner ) Ginette Reno – singer Mike Reno – singer ( Loverboy ) Jessie Reyez – singer Donn Reynolds – yodeler; folk and country singer-songwriter Isabelle Rezazadeh – DJ and record producer ( Rezz ) Amanda Rheaume – folk singer-songwriter Kyle Riabko – singer, guitarist Alejandra Ribera – pop and jazz singer-songwriter Jackie Richardson – blues, jazz and gospel singer Sébastien Ricard – rapper ( Loco Locass ), actor Charles Richard-Hamelin – pianist Kim Richardson – pop, blues, jazz and gospel singer River Tiber – rhythm and blues musician Jesse Rivest – singer-songwriter Ian Robb – folk musician Vincent Roberge – indie-pop singer Robert Robert – pop/electronic singer and producer Brad Roberts – singer ( Crash Test Dummies ) Sam Roberts – rock music\n\n[NEW 4] Year Performing Artist 2000 Christina Aguilera Macy Gray Kid Rock Britney Spears Susan Tedeschi 2001 Shelby Lynne Brad Paisley Papa Roach Jill Scott Sisqó 2002 Alicia Keys India Arie Nelly Furtado David Gray Linkin Park 2003 Norah Jones Ashanti Michelle Branch Avril Lavigne John Mayer 2004 Evanescence 50 Cent Fountains of Wayne Heather Headley Sean Paul 2005 Maroon 5 Los Lonely Boys Joss Stone Kanye West Gretchen Wilson 2006 John Legend Ciara Fall Out Boy Keane Sugarland 2007 Carrie Underwood James Blunt Chris Brown Imogen Heap Corinne Bailey Rae 2008 Amy Winehouse Feist Ledisi Paramore Taylor Swift 2009 Adele Duffy Jonas Brothers Lady Antebellum Jazmine Sullivan 2010s Year Performing Artist 2010 Zac Brown Band Keri Hilson MGMT Silversun Pickups The Ting Tin\n\n\nKEPT DOCUMENTS (context only):\n[4 documents already kept as relevant]\n[KEPT 1] 1999 studio album by Dismal Euphony All Little Devils The cover was designed by Thomas Ewerhard and its text was designed by Vibeke Tveiten. Studio album by Dismal Euphony Released 22 March 1999 Recorded The Woodhouse Studios in September 1998 Genre Symphonic black metal , heavy metal Length 39 : 00...\n[KEPT 2] Norwegian metal band Dismal Euphony Background information Origin Stavanger , Norway Genres Symphonic black metal , melodic black metal , gothic metal Years active 1994–2001 Labels Nuclear Blast , Napalm Past members See below Dismal Euphony was a Norwegian dark metal band that mixed styles includin...\n[KEPT 3] S1 maint: numeric names: authors list ( link ) ^ ^ ^ a b ^ ^ External links Nuclear Blast Europe...\n[KEPT 4] American heavy metal band Anthrax Anthrax performing at Wacken Open Air 2019 Background information Origin Queens , New York City, U. S. Genres Thrash metal speed metal groove metal alternative metal Works Discography Years active 1981–present Labels Megaforce Island Elektra Ignition Tommy Boy Beyon...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 4 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1317, + "osl": 1400, + "total_tokens": 2717, + "latency_ms": 15000.89, + "tokens_per_second": 93.33 + }, + "context": { + "num_documents_evaluated": 4, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 4 + } + }, + { + "call_id": "48b9369b-63ec-4ce7-b1b9-c95826c886b8", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T02:34:03.847261Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What is the name of the vocalist from the first band to make it in the top 200 under the record label that produced the third studio album for Dismal Euphony?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] 1999 studio album by Dismal Euphony All Little Devils The cover was designed by Thomas Ewerhard and its text was designed by Vibeke Tveiten. Studio album by Dismal Euphony Released 22 March 1999 Recorded The Woodhouse Studios in September 1998 Genre Symphonic black metal , heavy metal Length 39 : 00 Language English Label Nuclear Blast Records Producer Waldemar Sorychta Professional ratings Review scores Source Rating AllMusic All Little Devils is the third studio album by the Norwegian gothic metal band Dismal Euphony . It was released in 1999, and was the band's first album with Nuclear Blast . Track listing \"Days of Sodom\" (5:22) \"Rage of Fire\" (3:44) \"Victory\" (4:31) \"All Little Devils\" (4:12) \"Lunatic\" (4:19) \"Psycho Path\" (4:03) \"Shine for Me, Misery\"\n\n[DOC 2] Norwegian metal band Dismal Euphony Background information Origin Stavanger , Norway Genres Symphonic black metal , melodic black metal , gothic metal Years active 1994–2001 Labels Nuclear Blast , Napalm Past members See below Dismal Euphony was a Norwegian dark metal band that mixed styles including gothic metal , black metal , death metal , melodic metal, and classical music . History The history of Dismal Euphony began in 1992 in Stavanger , Norway by bassist Ole K. Helgesen and drummer Kristoffer Vold Austrheim under the name The Headless Children, a cover band of Slayer and Kreator . A year later, singer Erik Borgen and guitarist Kenneth Bergsagel joined the group. They changed the name to Carnal Tomb, then Borgen left the band and Helgesen became the s\n\n[DOC 3] S1 maint: numeric names: authors list ( link ) ^ ^ ^ a b ^ ^ External links Nuclear Blast Europe\n\n[DOC 4] American heavy metal band Anthrax Anthrax performing at Wacken Open Air 2019 Background information Origin Queens , New York City, U. S. Genres Thrash metal speed metal groove metal alternative metal Works Discography Years active 1981–present Labels Megaforce Island Elektra Ignition Tommy Boy Beyond Sanctuary Nuclear Blast Spinoffs Stormtroopers of Death Members Scott Ian Charlie Benante Frank Bello Joey Belladonna Jonathan Donais Past members See List of Anthrax members Website anthrax . com Logo Anthrax is an American heavy metal band from New York City, formed in 1981 by rhythm guitarist Scott Ian and bassist Dan Lilker . The group is considered one of the leaders of the thrash metal scene from the 1980s and is part of the \"Big Four\" of the genre, along\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide the label (Nuclear Blast) for Dismal Euphony's third studio album and some band background, but they do not contain information about which band was the first under Nuclear Blast to reach the top 200, nor the name of that band's vocalist. Hence, a complete answer cannot be derived from the available documents.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 910, + "osl": 372, + "total_tokens": 1282, + "latency_ms": 2349.32, + "tokens_per_second": 158.34 + }, + "context": { + "kept_docs_count": 4, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "2c44b6af-920a-4a63-a0c7-943afe44698b", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T02:34:14.227483Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What is the name of the vocalist from the first band to make it in the top 200 under the record label that produced the third studio album for Dismal Euphony?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] 1999 studio album by Dismal Euphony All Little Devils The cover was designed by Thomas Ewerhard and its text was designed by Vibeke Tveiten. Studio album by Dismal Euphony Released 22 March 1999 Recorded The Woodhouse Studios in September 1998 Genre Symphonic black metal , heavy metal Length 39 : 00 Language English Label Nuclear Blast Records Producer Waldemar Sorychta Professional ratings Review scores Source Rating AllMusic All Little Devils is the third studio album by the Norwegian gothic metal band Dismal Euphony . It was released in 1999, and was the band's first album with Nuclear Blast . Track listing \"Days of Sodom\" (5:22) \"Rage of Fire\" (3:44) \"Victory\" (4:31) \"All Little Devils\" (4:12) \"Lunatic\" (4:19) \"Psycho Path\" (4:03) \"Shine for Me, Misery\"\n\n[DOC 2] Norwegian metal band Dismal Euphony Background information Origin Stavanger , Norway Genres Symphonic black metal , melodic black metal , gothic metal Years active 1994–2001 Labels Nuclear Blast , Napalm Past members See below Dismal Euphony was a Norwegian dark metal band that mixed styles including gothic metal , black metal , death metal , melodic metal, and classical music . History The history of Dismal Euphony began in 1992 in Stavanger , Norway by bassist Ole K. Helgesen and drummer Kristoffer Vold Austrheim under the name The Headless Children, a cover band of Slayer and Kreator . A year later, singer Erik Borgen and guitarist Kenneth Bergsagel joined the group. They changed the name to Carnal Tomb, then Borgen left the band and Helgesen became the s\n\n[DOC 3] S1 maint: numeric names: authors list ( link ) ^ ^ ^ a b ^ ^ External links Nuclear Blast Europe\n\n[DOC 4] American heavy metal band Anthrax Anthrax performing at Wacken Open Air 2019 Background information Origin Queens , New York City, U. S. Genres Thrash metal speed metal groove metal alternative metal Works Discography Years active 1981–present Labels Megaforce Island Elektra Ignition Tommy Boy Beyond Sanctuary Nuclear Blast Spinoffs Stormtroopers of Death Members Scott Ian Charlie Benante Frank Bello Joey Belladonna Jonathan Donais Past members See List of Anthrax members Website anthrax . com Logo Anthrax is an American heavy metal band from New York City, formed in 1981 by rhythm guitarist Scott Ian and bassist Dan Lilker . The group is considered one of the leaders of the thrash metal scene from the 1980s and is part of the \"Big Four\" of the genre, along\n\n\nSEARCH HISTORY: - Query: 'Dismal Euphony third album' → 5 docs\n- Query: 'Dismal Euphony record label' → 2 docs\n- Query: 'Nuclear Blast Billboard 200' → 5 docs\n- Query: 'first Nuclear Blast band' → 2 docs\n- Query: 'first Nuclear Blast chart' → 2 docs\n- Query: 'Nuclear Blast Billboard 200 entries' → 2 docs\n- Query: 'first Nuclear Blast band charted' → 0 docs\n- Query: 'first Nuclear Blast top 200 vocalist' → 2 docs\n\nFEEDBACK: First query aims to identify the third studio album of Dismal Euphony, which will reveal the record label that produced it. The second query directly seeks the record label associated with Dismal Euphony. Once the label is known, a subsequent query can target the first band that entered the Billboard Top 200 under that label to retrieve the vocalist's name.\nWe have identified the record label (Nuclear Blast) from the third Dismal Euphony album. The next step is to locate the earliest band on that label that entered the Top 200, then retrieve that band's vocalist name.\nNeed to identify the earliest Nuclear Blast‑signed band that entered the Billboard Top 200, then retrieve the name of that band’s vocalist.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Nuclear Blast Billboard 200\",\n \"first Nuclear Blast band Billboard 200\",\n \"Nuclear Blast first charting vocalist\"\n ],\n \"feedback\": \"The goal is to identify the earliest Nuclear Blast‑signed band that entered the Billboard Top 200, then retrieve that band’s vocalist. The first query targets the label’s Billboard 200 history; the second narrows to the first band to achieve that placement; the third seeks the vocalist of that pioneering act.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1304, + "osl": 106, + "total_tokens": 1410, + "latency_ms": 10379.63, + "tokens_per_second": 10.21 + }, + "context": { + "kept_docs_count": 4, + "iteration_state": "generating_queries", + "query_history_length": 8 + } + }, + { + "call_id": "ef83628a-523c-4514-86e1-a4e73bd92b70", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T02:34:44.692942Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: What is the name of the vocalist from the first band to make it in the top 200 under the record label that produced the third studio album for Dismal Euphony?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] he Billboard 200, selling over 46,000 copies in its first week. It was complete by September 1997, and scheduled to be released the following month, but got delayed by nine months after their label was taken over by Columbia Records . The album received a mixed critical reception, and was criticized for adopting characteristics of nu metal music such as tuned down guitars, murky chord structures, and churning beats. Blabbermouth. net reviewer Borivoj Krgin described the album as \"a feeble attempt at incorporating updated elements into the group's sound, the presence of which elevated the band's efforts somewhat and offered hope that Slayer could refrain from endlessly rehashing their previous material for their future output\", while Ben Ratliff of The New Yo\n\n[NEW 2] 982, and touring to increasing commercial and critical acclaim, becoming one of the world's most successful and influential rock bands. In 1984, Van Halen released 1984 , which peaked at No. 2 on the Billboard 200 and had the band's sole No. 1 hit on the Billboard Hot 100 , \" Jump \". After the tour promoting that album , Roth left the band due to artistic and personal tensions with Eddie. To replace Roth, Eddie picked Sammy Hagar , formerly of Montrose and at that time a very successful solo artist. Van Halen's first album with Hagar, 1986's 5150 , was the band's first No. 1 on the Billboard 200. The three studio albums that followed, OU812 , For Unlawful Carnal Knowledge and Balance , also topped the charts. In 1996, Hagar left Van Halen amidst similar tens\n\n[NEW 3] American rock musician (1967–2002) Layne Staley Staley performing with Alice in Chains in 1992 Background information Also known as Layne Elmer Born Layne Rutherford Staley ( 1967-08-22 ) August 22, 1967 Bellevue, Washington , U. S. Died April 5, 2002 (2002-04-05) (aged 34) Seattle , Washington, U. S. Genres Grunge alternative metal alternative rock hard rock glam metal (early) Occupations Musician singer songwriter Instruments Vocals guitar Years active 1984–1998 Formerly of Alice in Chains Mad Season Class of '99 Alice N' Chains Sleze Website layne-staley . com Musical artist Layne Thomas Staley (born Layne Rutherford Staley ; August 22, 1967 – April 5, 2002) was an American singer-songwriter. He was the original lead vocalist of Alice in Chains , which ro\n\n\nKEPT DOCUMENTS (context only):\n[4 documents already kept as relevant]\n[KEPT 1] 1999 studio album by Dismal Euphony All Little Devils The cover was designed by Thomas Ewerhard and its text was designed by Vibeke Tveiten. Studio album by Dismal Euphony Released 22 March 1999 Recorded The Woodhouse Studios in September 1998 Genre Symphonic black metal , heavy metal Length 39 : 00...\n[KEPT 2] Norwegian metal band Dismal Euphony Background information Origin Stavanger , Norway Genres Symphonic black metal , melodic black metal , gothic metal Years active 1994–2001 Labels Nuclear Blast , Napalm Past members See below Dismal Euphony was a Norwegian dark metal band that mixed styles includin...\n[KEPT 3] S1 maint: numeric names: authors list ( link ) ^ ^ ^ a b ^ ^ External links Nuclear Blast Europe...\n[KEPT 4] American heavy metal band Anthrax Anthrax performing at Wacken Open Air 2019 Background information Origin Queens , New York City, U. S. Genres Thrash metal speed metal groove metal alternative metal Works Discography Years active 1981–present Labels Megaforce Island Elektra Ignition Tommy Boy Beyon...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 3 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1073, + "osl": 2037, + "total_tokens": 3110, + "latency_ms": 29872.22, + "tokens_per_second": 68.19 + }, + "context": { + "num_documents_evaluated": 3, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 4 + } + }, + { + "call_id": "0188dcae-890a-40a6-950f-74366a33dbe4", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T02:34:47.425490Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What is the name of the vocalist from the first band to make it in the top 200 under the record label that produced the third studio album for Dismal Euphony?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] 1999 studio album by Dismal Euphony All Little Devils The cover was designed by Thomas Ewerhard and its text was designed by Vibeke Tveiten. Studio album by Dismal Euphony Released 22 March 1999 Recorded The Woodhouse Studios in September 1998 Genre Symphonic black metal , heavy metal Length 39 : 00 Language English Label Nuclear Blast Records Producer Waldemar Sorychta Professional ratings Review scores Source Rating AllMusic All Little Devils is the third studio album by the Norwegian gothic metal band Dismal Euphony . It was released in 1999, and was the band's first album with Nuclear Blast . Track listing \"Days of Sodom\" (5:22) \"Rage of Fire\" (3:44) \"Victory\" (4:31) \"All Little Devils\" (4:12) \"Lunatic\" (4:19) \"Psycho Path\" (4:03) \"Shine for Me, Misery\"\n\n[DOC 2] Norwegian metal band Dismal Euphony Background information Origin Stavanger , Norway Genres Symphonic black metal , melodic black metal , gothic metal Years active 1994–2001 Labels Nuclear Blast , Napalm Past members See below Dismal Euphony was a Norwegian dark metal band that mixed styles including gothic metal , black metal , death metal , melodic metal, and classical music . History The history of Dismal Euphony began in 1992 in Stavanger , Norway by bassist Ole K. Helgesen and drummer Kristoffer Vold Austrheim under the name The Headless Children, a cover band of Slayer and Kreator . A year later, singer Erik Borgen and guitarist Kenneth Bergsagel joined the group. They changed the name to Carnal Tomb, then Borgen left the band and Helgesen became the s\n\n[DOC 3] S1 maint: numeric names: authors list ( link ) ^ ^ ^ a b ^ ^ External links Nuclear Blast Europe\n\n[DOC 4] American heavy metal band Anthrax Anthrax performing at Wacken Open Air 2019 Background information Origin Queens , New York City, U. S. Genres Thrash metal speed metal groove metal alternative metal Works Discography Years active 1981–present Labels Megaforce Island Elektra Ignition Tommy Boy Beyond Sanctuary Nuclear Blast Spinoffs Stormtroopers of Death Members Scott Ian Charlie Benante Frank Bello Joey Belladonna Jonathan Donais Past members See List of Anthrax members Website anthrax . com Logo Anthrax is an American heavy metal band from New York City, formed in 1981 by rhythm guitarist Scott Ian and bassist Dan Lilker . The group is considered one of the leaders of the thrash metal scene from the 1980s and is part of the \"Big Four\" of the genre, along\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide the label for Dismal Euphony's third studio album (Nuclear Blast) and details about Dismal Euphony itself, but they do not contain any information about which band was the first under Nuclear Blast to enter the Top 200 chart, nor the name of that band's vocalist. Therefore, the required facts are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 910, + "osl": 527, + "total_tokens": 1437, + "latency_ms": 2731.39, + "tokens_per_second": 192.94 + }, + "context": { + "kept_docs_count": 4, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "3707f21b-5f21-4df5-9ddf-6c8d6ccc4459", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T02:34:53.200885Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: What is the name of the vocalist from the first band to make it in the top 200 under the record label that produced the third studio album for Dismal Euphony?\n\nDOCUMENTS:\n\n[DOC 1] 1999 studio album by Dismal Euphony All Little Devils The cover was designed by Thomas Ewerhard and its text was designed by Vibeke Tveiten. Studio album by Dismal Euphony Released 22 March 1999 Recorded The Woodhouse Studios in September 1998 Genre Symphonic black metal , heavy metal Length 39 : 00 Language English Label Nuclear Blast Records Producer Waldemar Sorychta Professional ratings Review scores Source Rating AllMusic All Little Devils is the third studio album by the Norwegian gothic metal band Dismal Euphony . It was released in 1999, and was the band's first album with Nuclear Blast . Track listing \"Days of Sodom\" (5:22) \"Rage of Fire\" (3:44) \"Victory\" (4:31) \"All Little Devils\" (4:12) \"Lunatic\" (4:19) \"Psycho Path\" (4:03) \"Shine for Me, Misery\"\n\n[DOC 2] Norwegian metal band Dismal Euphony Background information Origin Stavanger , Norway Genres Symphonic black metal , melodic black metal , gothic metal Years active 1994–2001 Labels Nuclear Blast , Napalm Past members See below Dismal Euphony was a Norwegian dark metal band that mixed styles including gothic metal , black metal , death metal , melodic metal, and classical music . History The history of Dismal Euphony began in 1992 in Stavanger , Norway by bassist Ole K. Helgesen and drummer Kristoffer Vold Austrheim under the name The Headless Children, a cover band of Slayer and Kreator . A year later, singer Erik Borgen and guitarist Kenneth Bergsagel joined the group. They changed the name to Carnal Tomb, then Borgen left the band and Helgesen became the s\n\n[DOC 3] S1 maint: numeric names: authors list ( link ) ^ ^ ^ a b ^ ^ External links Nuclear Blast Europe\n\n[DOC 4] American heavy metal band Anthrax Anthrax performing at Wacken Open Air 2019 Background information Origin Queens , New York City, U. S. Genres Thrash metal speed metal groove metal alternative metal Works Discography Years active 1981–present Labels Megaforce Island Elektra Ignition Tommy Boy Beyond Sanctuary Nuclear Blast Spinoffs Stormtroopers of Death Members Scott Ian Charlie Benante Frank Bello Joey Belladonna Jonathan Donais Past members See List of Anthrax members Website anthrax . com Logo Anthrax is an American heavy metal band from New York City, formed in 1981 by rhythm guitarist Scott Ian and bassist Dan Lilker . The group is considered one of the leaders of the thrash metal scene from the 1980s and is part of the \"Big Four\" of the genre, along\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 740, + "osl": 268, + "total_tokens": 1008, + "latency_ms": 5774.69, + "tokens_per_second": 46.41 + }, + "context": { + "num_documents_used": 4, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:34:53.201675Z", + "summary": { + "total_llm_calls": 13, + "total_hops": 5, + "total_input_tokens": 14410, + "total_output_tokens": 9216, + "total_tokens": 23626, + "total_latency_ms": 190637.54, + "average_tokens_per_second": 84.03, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Dismal_Euphony", + "https://en.wikipedia.org/wiki/All_Little_Devils", + "https://en.wikipedia.org/wiki/Nuclear_Blast", + "https://en.wikipedia.org/wiki/Meshuggah" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "Jens Kidman" + } + }, + { + "query_id": "5", + "original_query": "According to the 2000 United States census, what was the 2000 population of the birth city of the only 21st-century mayor of Austin, Texas who also served as mayor in the 1990s? Round your answer to the nearest thousand.", + "timestamp_start": "2026-05-16T02:34:53.206110Z", + "llm_calls": [ + { + "call_id": "67aaaa57-aafb-4c91-83f7-fc4ae80394c9", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:34:56.812366Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: According to the 2000 United States census, what was the 2000 population of the birth city of the only 21st-century mayor of Austin, Texas who also served as mayor in the 1990s? Round your answer to the nearest thousand.\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Kirk Watson Austin mayor\",\n \"Kirk Watson birthplace\",\n \"Wichita Kansas 2000 census population\"\n ],\n \"feedback\": \"Need to confirm the mayor who served both in the 1990s and 21st century, retrieve his birth city, then get the 2000 census population for that city.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 486, + "osl": 727, + "total_tokens": 1213, + "latency_ms": 3606.1, + "tokens_per_second": 201.6 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "ae3546b3-aa77-4449-8521-cb1f4ab8b33b", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:35:04.565094Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: According to the 2000 United States census, what was the 2000 population of the birth city of the only 21st-century mayor of Austin, Texas who also served as mayor in the 1990s? Round your answer to the nearest thousand.\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] American politician Kirk Watson 54th & 59th Mayor of Austin Incumbent Assumed office January 6, 2023 Preceded by Steve Adler In office June 15, 1997 – November 9, 2001 Preceded by Bruce Todd Succeeded by Gus Garcia President pro tempore of the Texas Senate In office January 8, 2019 – May 27, 2019 Preceded by Robert Nichols Succeeded by Joan Huffman Member of the Texas Senate from the 14th district In office January 9, 2007 – April 30, 2020 Preceded by Gonzalo Barrientos Succeeded by Sarah Eckhardt Personal details Born Kirk Preston Watson ( 1958-03-18 ) March 18, 1958 (age 68) Oklahoma City , Oklahoma, U. S. Party Democratic Spouse Liz Ann McDaniel Children 2 Education Baylor University ( BA , JD ) Kirk Preston Watson (born March 18, 1958) is an American att\n\n[NEW 2] Head of the Austin, Texas municipal government Mayor of Austin Flag of the City of Austin Incumbent Kirk Watson since January 6, 2023 Residence Private residence Term length Four years renewable once Inaugural holder Edwin Waller Formation 1840 Salary $134,191 Website austintexas . gov /mayor The mayor of Austin is the official head of the city of Austin in the U. S. state of Texas . The office was established in 1840 after Austin incorporated as a city in 1839. The mayor of Austin is elected to a four-year term and limited to serving no more than two terms. Kirk Watson took office as mayor on January 6, 2023, for a third term and was re-elected to a fourth term in 2024, having served as mayor from 1997 to 2001. Duties and powers Austin has a council–manager\n\n[NEW 3] al. The Watsons were inspired by their childhood in France and their father Chris Watson's wine business. The name Renais comes from the French word meaning \"rebirth\" and the gin is an ode to the Chablis wine region. In September 2023, Watson and Alex won the Soho House Award for \"Breakthrough Entrepreneur\" for Renais. In January 2025, it was reported that the company raised $6 million for investment. Personal life When asked about her religious beliefs in 2014, Watson described herself as a spiritual universalist . In February 2016, Watson was appointed visiting fellow at Lady Margaret Hall , Oxford University . In 2019, she described herself as single with the self-coined phrase \"self-partnered\". On coping with intense fame from a young age, she has said t\n\n[NEW 4] ing together, 12.1% had a female householder with no husband present, 4.0% had a male householder with no wife present, and 64.1% were non-families. 42.9% of all households were made up of individuals, and 7.8% had someone living alone who was 65 years of age or older. The average household size was 2.06 and the average family size was 2.92. The median age in the city was 25 years. 14.1% of residents were under the age of 18; 35.8% were between the ages of 18 and 24; 25.3% were from 25 to 44; 16.6% were from 45 to 64; and 8.3% were 65 years of age or older. The gender makeup of the city was 49.7% male and 50.3% female. 2000 census As of the census of 2000, there were 22,362 people, 8,551 households, and 3,377 families residing in the city. The population den\n\n[NEW 5] of the Topeka, Kansas media market, but it is close to the Wichita market, which encompasses two-thirds of the state (stretching to the border with Colorado ), including the cities of Dodge City , Garden City , Hutchinson and Salina . While the University of Kansas is in Lawrence, Kansas , it is officially part of the Kansas City television market, increasing the base into western Missouri . State Population Universities Arizona 7,431,344 * University of Arizona * Arizona State University Colorado 5,773,714 * University of Colorado Boulder Florida 22,610,726 * University of Central Florida Iowa 3,207,004 * Iowa State University Kansas 2,940,546 * University of Kansas * Kansas State University Ohio 11,785,935 * University of Cincinnati Oklahoma 4,053,824 * O\n\n[NEW 6] 25 to 44, 27.7% from 45 to 64, and 18.2% who were 65 or older. The median age was 41.4 years. For every 100 females, there were 85.2 males. For every 100 females age 18 and over, there were 81.2 males. The median income for a household in the city was $31,220, and the median income for a family was $42,645. Males had a median income of $36,778 versus $27,597 for females. The per capita income for the city was $20,430. About 18.1% of families and 22.0% of the population were below the poverty line, including 34.3% of those under age 18 and 12.8% of those age 65 or over. 2000 census As of the census of 2000, there were 26,307 people, 11,825 households, and 6,645 families residing in the city. The population density was 1,350.2 inhabitants per square mile (521\n\n[NEW 7] ican 4.2% Central American 4% Polish American 3.7% Scottish American (including Scots-Irish ) 2.8% Scandinavian Americans (including Icelandic , Finnish , and Baltic ) 2.8% Puerto Rican American 2.7% Central European (including Hungarian , Slovak , Slovenian , Czech , etc.) 2.3% 2000 census As of the census of 2000, there were 25,478 people, 11,016 households, and 5,463 families residing in the city. The population density is 1,653.3/km 2 (4,285.0/mi 2 ). There are 13,306 housing units at an average density of 863.4/km 2 (2,237.9/mi 2 ). The racial makeup of the city was 84.94% White , 9.28% Black or African American , 0.39% Native American , 1.29% Asian , 0.05% Pacific Islander , 1.86% from other races, and 2.18% from two or more races. 16.54% of the popula\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 7 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 1, 0, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1792, + "osl": 509, + "total_tokens": 2301, + "latency_ms": 7148.05, + "tokens_per_second": 71.21 + }, + "context": { + "num_documents_evaluated": 7, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "cd600bb0-7317-419a-b8df-c9c35d0cc0ee", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:35:09.731985Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: According to the 2000 United States census, what was the 2000 population of the birth city of the only 21st-century mayor of Austin, Texas who also served as mayor in the 1990s? Round your answer to the nearest thousand.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American politician Kirk Watson 54th & 59th Mayor of Austin Incumbent Assumed office January 6, 2023 Preceded by Steve Adler In office June 15, 1997 – November 9, 2001 Preceded by Bruce Todd Succeeded by Gus Garcia President pro tempore of the Texas Senate In office January 8, 2019 – May 27, 2019 Preceded by Robert Nichols Succeeded by Joan Huffman Member of the Texas Senate from the 14th district In office January 9, 2007 – April 30, 2020 Preceded by Gonzalo Barrientos Succeeded by Sarah Eckhardt Personal details Born Kirk Preston Watson ( 1958-03-18 ) March 18, 1958 (age 68) Oklahoma City , Oklahoma, U. S. Party Democratic Spouse Liz Ann McDaniel Children 2 Education Baylor University ( BA , JD ) Kirk Preston Watson (born March 18, 1958) is an American att\n\n[DOC 2] Head of the Austin, Texas municipal government Mayor of Austin Flag of the City of Austin Incumbent Kirk Watson since January 6, 2023 Residence Private residence Term length Four years renewable once Inaugural holder Edwin Waller Formation 1840 Salary $134,191 Website austintexas . gov /mayor The mayor of Austin is the official head of the city of Austin in the U. S. state of Texas . The office was established in 1840 after Austin incorporated as a city in 1839. The mayor of Austin is elected to a four-year term and limited to serving no more than two terms. Kirk Watson took office as mayor on January 6, 2023, for a third term and was re-elected to a fourth term in 2024, having served as mayor from 1997 to 2001. Duties and powers Austin has a council–manager\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents identify the mayor (Kirk Watson) and his birth city (Oklahoma City, Oklahoma), but they do not provide the 2000 United States census population for Oklahoma City, which is required to answer the question.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 671, + "osl": 62, + "total_tokens": 733, + "latency_ms": 5165.98, + "tokens_per_second": 12.0 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "14609356-9d85-44a9-83a1-82a79d7f475b", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:35:19.684889Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: According to the 2000 United States census, what was the 2000 population of the birth city of the only 21st-century mayor of Austin, Texas who also served as mayor in the 1990s? Round your answer to the nearest thousand.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American politician Kirk Watson 54th & 59th Mayor of Austin Incumbent Assumed office January 6, 2023 Preceded by Steve Adler In office June 15, 1997 – November 9, 2001 Preceded by Bruce Todd Succeeded by Gus Garcia President pro tempore of the Texas Senate In office January 8, 2019 – May 27, 2019 Preceded by Robert Nichols Succeeded by Joan Huffman Member of the Texas Senate from the 14th district In office January 9, 2007 – April 30, 2020 Preceded by Gonzalo Barrientos Succeeded by Sarah Eckhardt Personal details Born Kirk Preston Watson ( 1958-03-18 ) March 18, 1958 (age 68) Oklahoma City , Oklahoma, U. S. Party Democratic Spouse Liz Ann McDaniel Children 2 Education Baylor University ( BA , JD ) Kirk Preston Watson (born March 18, 1958) is an American att\n\n[DOC 2] Head of the Austin, Texas municipal government Mayor of Austin Flag of the City of Austin Incumbent Kirk Watson since January 6, 2023 Residence Private residence Term length Four years renewable once Inaugural holder Edwin Waller Formation 1840 Salary $134,191 Website austintexas . gov /mayor The mayor of Austin is the official head of the city of Austin in the U. S. state of Texas . The office was established in 1840 after Austin incorporated as a city in 1839. The mayor of Austin is elected to a four-year term and limited to serving no more than two terms. Kirk Watson took office as mayor on January 6, 2023, for a third term and was re-elected to a fourth term in 2024, having served as mayor from 1997 to 2001. Duties and powers Austin has a council–manager\n\n\nSEARCH HISTORY: - Query: 'Kirk Watson Austin mayor' → 2 docs\n- Query: 'Kirk Watson birthplace' → 1 docs\n- Query: 'Wichita Kansas 2000 census population' → 4 docs\n\nFEEDBACK: Need to confirm the mayor who served both in the 1990s and 21st century, retrieve his birth city, then get the 2000 census population for that city.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Oklahoma City 2000 population\",\n \"Oklahoma City 2000 census\",\n \"2000 census Oklahoma City population\"\n ],\n \"feedback\": \"We have identified Kirk Watson as the mayor in question; his birth city is Oklahoma City. The next step is to retrieve the 2000 United States census population for Oklahoma City.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 951, + "osl": 325, + "total_tokens": 1276, + "latency_ms": 9952.19, + "tokens_per_second": 32.66 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "2b94f57d-5281-42d2-a412-5ec4585dfce4", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T02:36:10.131462Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: According to the 2000 United States census, what was the 2000 population of the birth city of the only 21st-century mayor of Austin, Texas who also served as mayor in the 1990s? Round your answer to the nearest thousand.\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] 71 70 60 60 69 Average ultraviolet index 3 4 6 8 9 10 10 9 8 5 3 2 6.4 Source 1: NOAA (relative humidity and sun 1961−1990) Source 2: Weather Atlas(Daylight-UV) Demographics Population of Oklahoma City 1890-2022 Census Pop. Note %± 1890 4,151 — 1900 10,037 141.8% 1910 64,205 539.7% 1920 91,295 42.2% 1930 185,389 103.1% 1940 204,424 10.3% 1950 243,504 19.1% 1960 324,253 33.2% 1970 368,164 13.5% 1980 404,014 9.7% 1990 444,719 10.1% 2000 506,132 13.8% 2010 579,999 14.6% 2020 681,054 17.4% 2024 (est.) 712,919 4.7% U. S. Decennial Census 1790-1960 1900-1990 1990-2000 2010 In the 2010 census , there were 579,999 people, 230,233 households, and 144,120 families in the city. The population density was 956.4 inhabitants per square mile (321.9/km 2 ). There were 256,9\n\n[NEW 2] ebsite cityoftulsa. org Tulsa ( / ˈ t ʌ l s ə / i TUL -sə ) is the second-most-populous city in the U. S. state of Oklahoma and the 48th-most populous city in the United States. The population was 413,066 as of the 2020 census . It is the principal municipality of the Tulsa metropolitan area , a region with 1.06 million residents. The city serves as the county seat of Tulsa County , the most densely populated county in Oklahoma, with urban development extending into Osage , Rogers and Wagoner counties. Tulsa was settled between 1828 and 1836 by the Lochapoka band of Creek Native Americans, and was formally incorporated in 1898. Most of Tulsa is still part of the territory of the Muscogee (Creek) Nation . Northwest Tulsa lies in the Osage Nation whereas North\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] American politician Kirk Watson 54th & 59th Mayor of Austin Incumbent Assumed office January 6, 2023 Preceded by Steve Adler In office June 15, 1997 – November 9, 2001 Preceded by Bruce Todd Succeeded by Gus Garcia President pro tempore of the Texas Senate In office January 8, 2019 – May 27, 2019 Pr...\n[KEPT 2] Head of the Austin, Texas municipal government Mayor of Austin Flag of the City of Austin Incumbent Kirk Watson since January 6, 2023 Residence Private residence Term length Four years renewable once Inaugural holder Edwin Waller Formation 1840 Salary $134,191 Website austintexas . gov /mayor The ma...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 2 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 998, + "osl": 2576, + "total_tokens": 3574, + "latency_ms": 49821.88, + "tokens_per_second": 51.7 + }, + "context": { + "num_documents_evaluated": 2, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "502b8498-383b-45b9-9a3b-ff4e41345322", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T02:36:12.049457Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: According to the 2000 United States census, what was the 2000 population of the birth city of the only 21st-century mayor of Austin, Texas who also served as mayor in the 1990s? Round your answer to the nearest thousand.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American politician Kirk Watson 54th & 59th Mayor of Austin Incumbent Assumed office January 6, 2023 Preceded by Steve Adler In office June 15, 1997 – November 9, 2001 Preceded by Bruce Todd Succeeded by Gus Garcia President pro tempore of the Texas Senate In office January 8, 2019 – May 27, 2019 Preceded by Robert Nichols Succeeded by Joan Huffman Member of the Texas Senate from the 14th district In office January 9, 2007 – April 30, 2020 Preceded by Gonzalo Barrientos Succeeded by Sarah Eckhardt Personal details Born Kirk Preston Watson ( 1958-03-18 ) March 18, 1958 (age 68) Oklahoma City , Oklahoma, U. S. Party Democratic Spouse Liz Ann McDaniel Children 2 Education Baylor University ( BA , JD ) Kirk Preston Watson (born March 18, 1958) is an American att\n\n[DOC 2] Head of the Austin, Texas municipal government Mayor of Austin Flag of the City of Austin Incumbent Kirk Watson since January 6, 2023 Residence Private residence Term length Four years renewable once Inaugural holder Edwin Waller Formation 1840 Salary $134,191 Website austintexas . gov /mayor The mayor of Austin is the official head of the city of Austin in the U. S. state of Texas . The office was established in 1840 after Austin incorporated as a city in 1839. The mayor of Austin is elected to a four-year term and limited to serving no more than two terms. Kirk Watson took office as mayor on January 6, 2023, for a third term and was re-elected to a fourth term in 2024, having served as mayor from 1997 to 2001. Duties and powers Austin has a council–manager\n\n[DOC 3] 71 70 60 60 69 Average ultraviolet index 3 4 6 8 9 10 10 9 8 5 3 2 6.4 Source 1: NOAA (relative humidity and sun 1961−1990) Source 2: Weather Atlas(Daylight-UV) Demographics Population of Oklahoma City 1890-2022 Census Pop. Note %± 1890 4,151 — 1900 10,037 141.8% 1910 64,205 539.7% 1920 91,295 42.2% 1930 185,389 103.1% 1940 204,424 10.3% 1950 243,504 19.1% 1960 324,253 33.2% 1970 368,164 13.5% 1980 404,014 9.7% 1990 444,719 10.1% 2000 506,132 13.8% 2010 579,999 14.6% 2020 681,054 17.4% 2024 (est.) 712,919 4.7% U. S. Decennial Census 1790-1960 1900-1990 1990-2000 2010 In the 2010 census , there were 579,999 people, 230,233 households, and 144,120 families in the city. The population density was 956.4 inhabitants per square mile (321.9/km 2 ). There were 256,9\n\n[DOC 4] ebsite cityoftulsa. org Tulsa ( / ˈ t ʌ l s ə / i TUL -sə ) is the second-most-populous city in the U. S. state of Oklahoma and the 48th-most populous city in the United States. The population was 413,066 as of the 2020 census . It is the principal municipality of the Tulsa metropolitan area , a region with 1.06 million residents. The city serves as the county seat of Tulsa County , the most densely populated county in Oklahoma, with urban development extending into Osage , Rogers and Wagoner counties. Tulsa was settled between 1828 and 1836 by the Lochapoka band of Creek Native Americans, and was formally incorporated in 1898. Most of Tulsa is still part of the territory of the Muscogee (Creek) Nation . Northwest Tulsa lies in the Osage Nation whereas North\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"sufficient\": true, \"reasoning\": \"Doc 1 identifies Kirk Watson as the only 21st-century mayor of Austin who also served in the 1990s and states his birth city is Oklahoma City. Doc 3 provides the 2000 census population for Oklahoma City as 506,132, which can be rounded to the nearest thousand (506,000).\"}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1281, + "osl": 258, + "total_tokens": 1539, + "latency_ms": 1916.89, + "tokens_per_second": 134.59 + }, + "context": { + "kept_docs_count": 4, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "06fbe4f8-9d72-4386-bd88-47f72a693f17", + "component": "answer_generator", + "hop_count": 3, + "timestamp": "2026-05-16T02:36:18.709643Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: According to the 2000 United States census, what was the 2000 population of the birth city of the only 21st-century mayor of Austin, Texas who also served as mayor in the 1990s? Round your answer to the nearest thousand.\n\nDOCUMENTS:\n\n[DOC 1] American politician Kirk Watson 54th & 59th Mayor of Austin Incumbent Assumed office January 6, 2023 Preceded by Steve Adler In office June 15, 1997 – November 9, 2001 Preceded by Bruce Todd Succeeded by Gus Garcia President pro tempore of the Texas Senate In office January 8, 2019 – May 27, 2019 Preceded by Robert Nichols Succeeded by Joan Huffman Member of the Texas Senate from the 14th district In office January 9, 2007 – April 30, 2020 Preceded by Gonzalo Barrientos Succeeded by Sarah Eckhardt Personal details Born Kirk Preston Watson ( 1958-03-18 ) March 18, 1958 (age 68) Oklahoma City , Oklahoma, U. S. Party Democratic Spouse Liz Ann McDaniel Children 2 Education Baylor University ( BA , JD ) Kirk Preston Watson (born March 18, 1958) is an American att\n\n[DOC 2] Head of the Austin, Texas municipal government Mayor of Austin Flag of the City of Austin Incumbent Kirk Watson since January 6, 2023 Residence Private residence Term length Four years renewable once Inaugural holder Edwin Waller Formation 1840 Salary $134,191 Website austintexas . gov /mayor The mayor of Austin is the official head of the city of Austin in the U. S. state of Texas . The office was established in 1840 after Austin incorporated as a city in 1839. The mayor of Austin is elected to a four-year term and limited to serving no more than two terms. Kirk Watson took office as mayor on January 6, 2023, for a third term and was re-elected to a fourth term in 2024, having served as mayor from 1997 to 2001. Duties and powers Austin has a council–manager\n\n[DOC 3] 71 70 60 60 69 Average ultraviolet index 3 4 6 8 9 10 10 9 8 5 3 2 6.4 Source 1: NOAA (relative humidity and sun 1961−1990) Source 2: Weather Atlas(Daylight-UV) Demographics Population of Oklahoma City 1890-2022 Census Pop. Note %± 1890 4,151 — 1900 10,037 141.8% 1910 64,205 539.7% 1920 91,295 42.2% 1930 185,389 103.1% 1940 204,424 10.3% 1950 243,504 19.1% 1960 324,253 33.2% 1970 368,164 13.5% 1980 404,014 9.7% 1990 444,719 10.1% 2000 506,132 13.8% 2010 579,999 14.6% 2020 681,054 17.4% 2024 (est.) 712,919 4.7% U. S. Decennial Census 1790-1960 1900-1990 1990-2000 2010 In the 2010 census , there were 579,999 people, 230,233 households, and 144,120 families in the city. The population density was 956.4 inhabitants per square mile (321.9/km 2 ). There were 256,9\n\n[DOC 4] ebsite cityoftulsa. org Tulsa ( / ˈ t ʌ l s ə / i TUL -sə ) is the second-most-populous city in the U. S. state of Oklahoma and the 48th-most populous city in the United States. The population was 413,066 as of the 2020 census . It is the principal municipality of the Tulsa metropolitan area , a region with 1.06 million residents. The city serves as the county seat of Tulsa County , the most densely populated county in Oklahoma, with urban development extending into Osage , Rogers and Wagoner counties. Tulsa was settled between 1828 and 1836 by the Lochapoka band of Creek Native Americans, and was formally incorporated in 1898. Most of Tulsa is still part of the territory of the Muscogee (Creek) Nation . Northwest Tulsa lies in the Osage Nation whereas North\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "The birth city is Oklahoma City, whose 2000 census population was **506,000** (rounded to the nearest thousand).", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1138, + "osl": 477, + "total_tokens": 1615, + "latency_ms": 6659.45, + "tokens_per_second": 71.63 + }, + "context": { + "num_documents_used": 4, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:36:18.710412Z", + "summary": { + "total_llm_calls": 7, + "total_hops": 3, + "total_input_tokens": 7317, + "total_output_tokens": 4934, + "total_tokens": 12251, + "total_latency_ms": 84270.54, + "average_tokens_per_second": 82.2, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Mayor_of_Austin", + "https://en.wikipedia.org/wiki/Kirk_Watson", + "https://en.wikipedia.org/wiki/Oklahoma_City" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "The birth city is Oklahoma City, whose 2000 census population was **506,000** (rounded to the nearest thousand).", + "ground_truth_answer": "506000" + } + }, + { + "query_id": "6", + "original_query": "I have an element in mind and would like you to identify the person it was named after. Here's a clue: The element's atomic number is 9 higher than that of an element discovered by the scientist who discovered Zirconium in the same year.", + "timestamp_start": "2026-05-16T02:36:18.715689Z", + "llm_calls": [ + { + "call_id": "cf3a35a3-2e57-4c7a-b000-8d9a4f8a0366", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:36:37.283664Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: I have an element in mind and would like you to identify the person it was named after. Here's a clue: The element's atomic number is 9 higher than that of an element discovered by the scientist who discovered Zirconium in the same year.\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Klaproth zirconium uranium\",\n \"uranium atomic number\",\n \"Mendelevium named after\"\n ],\n \"feedback\": \"Need to confirm Klaproth discovered both zirconium and uranium in the same year (1789), get uranium's atomic number (92), add 9 to get element 101, and verify element 101 (Mendelevium) is named after Dmitri Mendeleev.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 473, + "osl": 859, + "total_tokens": 1332, + "latency_ms": 18567.84, + "tokens_per_second": 46.26 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "0e247a38-9a1a-42b9-84a7-5558de70f93f", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:36:44.565680Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: I have an element in mind and would like you to identify the person it was named after. Here's a clue: The element's atomic number is 9 higher than that of an element discovered by the scientist who discovered Zirconium in the same year.\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Nickel Copper Zinc Gallium Germanium Arsenic Selenium Bromine Krypton Rubidium Strontium Yttrium Zirconium Niobium Molybdenum Technetium Ruthenium Rhodium Palladium Silver Cadmium Indium Tin Antimony Tellurium Iodine Xenon Caesium Barium Lanthanum Cerium Praseodymium Neodymium Promethium Samarium Europium Gadolinium Terbium Dysprosium Holmium Erbium Thulium Ytterbium Lutetium Hafnium Tantalum Tungsten Rhenium Osmium Iridium Platinum Gold Mercury (element) Thallium Lead Bismuth Polonium Astatine Radon Francium Radium Actinium Thorium Protactinium Uranium Neptunium Plutonium Americium Curium Berkelium Californium Einsteinium Fermium Mendelevium Nobelium Lawrencium Rutherfordium Dubnium Seaborgium Bohrium Hassium Meitnerium Darmstadtium Roentgenium Copernicium\n\n[NEW 2] nd an independent inventor of gravimetric analysis . His attention to detail and refusal to ignore discrepancies in results led to improvements in the use of apparatus. He was a major figure in understanding the composition of minerals and characterizing the elements. Klaproth discovered uranium (1789) and zirconium (1789). He was also involved in the discovery or co-discovery of titanium (1795), strontium (1793), cerium (1803), and chromium (1797) and confirmed the previous discoveries of tellurium (1798) and beryllium (1798). Klaproth was a member and director of the Berlin Academy of Sciences . He was recognized internationally as a member of the Royal Society in London, the Institut de France , and the Royal Swedish Academy of Sciences . Career Klaproth\n\n[NEW 3] Chemical element with atomic number 92 (U) Uranium, 92 U Uranium Pronunciation / j ʊ ˈ r eɪ n i ə m / i ( yuu-RAY -nee-əm ) Appearance silvery gray metallic; corrodes to a spalling black oxide coat in air Standard atomic weight A r °(U) 238.028 91 ± 0.000 03 238.03 ± 0.01 ( abridged ) Uranium in the periodic table Hydrogen Helium Lithium Beryllium Boron Carbon Nitrogen Oxygen Fluorine Neon Sodium Magnesium Aluminium Silicon Phosphorus Sulfur Chlorine Argon Potassium Calcium Scandium Titanium Vanadium Chromium Manganese Iron Cobalt Nickel Copper Zinc Gallium Germanium Arsenic Selenium Bromine Krypton Rubidium Strontium Yttrium Zirconium Niobium Molybdenum Technetium Ruthenium Rhodium Palladium Silver Cadmium Indium Tin Antimony Tellurium Iodine Xenon Caesium\n\n[NEW 4] d. Initial experiments were carried out in September 1954. No alpha decay was seen from mendelevium atoms; thus, Ghiorso suggested that the mendelevium had all decayed by electron capture to fermium-256 , correctly believed to decay primarily by fission, and that the experiment should be repeated, this time searching for those spontaneous fission events. This version of the experiment was performed in February 1955. The element was named after Dmitri Mendeleev . On the day of discovery, 19 February, alpha irradiation of the einsteinium target occurred in three three-hour sessions. The cyclotron was in the University of California campus, while the Radiation Laboratory was on the next hill. To deal with this situation, a complex procedure was used: Ghiorso to\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 4 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[0,1,0,1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1071, + "osl": 819, + "total_tokens": 1890, + "latency_ms": 6651.46, + "tokens_per_second": 123.13 + }, + "context": { + "num_documents_evaluated": 4, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "7f434e2c-2150-4998-bcb8-dc4b6dc98309", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:36:50.223719Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: I have an element in mind and would like you to identify the person it was named after. Here's a clue: The element's atomic number is 9 higher than that of an element discovered by the scientist who discovered Zirconium in the same year.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] nd an independent inventor of gravimetric analysis . His attention to detail and refusal to ignore discrepancies in results led to improvements in the use of apparatus. He was a major figure in understanding the composition of minerals and characterizing the elements. Klaproth discovered uranium (1789) and zirconium (1789). He was also involved in the discovery or co-discovery of titanium (1795), strontium (1793), cerium (1803), and chromium (1797) and confirmed the previous discoveries of tellurium (1798) and beryllium (1798). Klaproth was a member and director of the Berlin Academy of Sciences . He was recognized internationally as a member of the Royal Society in London, the Institut de France , and the Royal Swedish Academy of Sciences . Career Klaproth\n\n[DOC 2] d. Initial experiments were carried out in September 1954. No alpha decay was seen from mendelevium atoms; thus, Ghiorso suggested that the mendelevium had all decayed by electron capture to fermium-256 , correctly believed to decay primarily by fission, and that the experiment should be repeated, this time searching for those spontaneous fission events. This version of the experiment was performed in February 1955. The element was named after Dmitri Mendeleev . On the day of discovery, 19 February, alpha irradiation of the einsteinium target occurred in three three-hour sessions. The cyclotron was in the University of California campus, while the Radiation Laboratory was on the next hill. To deal with this situation, a complex procedure was used: Ghiorso to\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide that Klaproth discovered zirconium and uranium in 1789 and that element 101 (mendelevium) is named after Dmitri Mendeleev. However, they do not contain the atomic numbers of uranium (92) or the element 9 higher (101), which are required to link the clue to mendelevium. Without these numeric facts, the answer cannot be fully derived from the provided documents.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 611, + "osl": 105, + "total_tokens": 716, + "latency_ms": 5657.27, + "tokens_per_second": 18.56 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "b4fc121e-5bcb-4ca4-9df4-18a787160272", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:36:51.559159Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: I have an element in mind and would like you to identify the person it was named after. Here's a clue: The element's atomic number is 9 higher than that of an element discovered by the scientist who discovered Zirconium in the same year.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] nd an independent inventor of gravimetric analysis . His attention to detail and refusal to ignore discrepancies in results led to improvements in the use of apparatus. He was a major figure in understanding the composition of minerals and characterizing the elements. Klaproth discovered uranium (1789) and zirconium (1789). He was also involved in the discovery or co-discovery of titanium (1795), strontium (1793), cerium (1803), and chromium (1797) and confirmed the previous discoveries of tellurium (1798) and beryllium (1798). Klaproth was a member and director of the Berlin Academy of Sciences . He was recognized internationally as a member of the Royal Society in London, the Institut de France , and the Royal Swedish Academy of Sciences . Career Klaproth\n\n[DOC 2] d. Initial experiments were carried out in September 1954. No alpha decay was seen from mendelevium atoms; thus, Ghiorso suggested that the mendelevium had all decayed by electron capture to fermium-256 , correctly believed to decay primarily by fission, and that the experiment should be repeated, this time searching for those spontaneous fission events. This version of the experiment was performed in February 1955. The element was named after Dmitri Mendeleev . On the day of discovery, 19 February, alpha irradiation of the einsteinium target occurred in three three-hour sessions. The cyclotron was in the University of California campus, while the Radiation Laboratory was on the next hill. To deal with this situation, a complex procedure was used: Ghiorso to\n\n\nSEARCH HISTORY: - Query: 'Klaproth zirconium uranium' → 2 docs\n- Query: 'uranium atomic number' → 1 docs\n- Query: 'Mendelevium named after' → 1 docs\n\nFEEDBACK: Need to confirm Klaproth discovered both zirconium and uranium in the same year (1789), get uranium's atomic number (92), add 9 to get element 101, and verify element 101 (Mendelevium) is named after Dmitri Mendeleev.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Klaproth 1789 zirconium uranium\",\n \"Uranium atomic number\",\n \"Mendelevium named after Dmitri Mendeleev\"\n ],\n \"feedback\": \"Need to verify Klaproth discovered both zirconium and uranium in 1789, retrieve uranium's atomic number (92), add 9 to get element 101, and confirm element 101 (Mendelevium) is named after Dmitri Mendeleev.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 923, + "osl": 353, + "total_tokens": 1276, + "latency_ms": 1334.82, + "tokens_per_second": 264.46 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "b078d0b9-227b-4b20-aab9-a4a1be4a40e5", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T02:37:09.792759Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: I have an element in mind and would like you to identify the person it was named after. Here's a clue: The element's atomic number is 9 higher than that of an element discovered by the scientist who discovered Zirconium in the same year.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] nd an independent inventor of gravimetric analysis . His attention to detail and refusal to ignore discrepancies in results led to improvements in the use of apparatus. He was a major figure in understanding the composition of minerals and characterizing the elements. Klaproth discovered uranium (1789) and zirconium (1789). He was also involved in the discovery or co-discovery of titanium (1795), strontium (1793), cerium (1803), and chromium (1797) and confirmed the previous discoveries of tellurium (1798) and beryllium (1798). Klaproth was a member and director of the Berlin Academy of Sciences . He was recognized internationally as a member of the Royal Society in London, the Institut de France , and the Royal Swedish Academy of Sciences . Career Klaproth\n\n[DOC 2] d. Initial experiments were carried out in September 1954. No alpha decay was seen from mendelevium atoms; thus, Ghiorso suggested that the mendelevium had all decayed by electron capture to fermium-256 , correctly believed to decay primarily by fission, and that the experiment should be repeated, this time searching for those spontaneous fission events. This version of the experiment was performed in February 1955. The element was named after Dmitri Mendeleev . On the day of discovery, 19 February, alpha irradiation of the einsteinium target occurred in three three-hour sessions. The cyclotron was in the University of California campus, while the Radiation Laboratory was on the next hill. To deal with this situation, a complex procedure was used: Ghiorso to\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents identify Klaproth as the discoverer of zirconium and uranium in 1789 (Doc 1) and state that mendelevium was named after Dmitri Mendeleev (Doc 2). However, they do not provide the atomic numbers of uranium (92) or mendelevium (101), nor the fact that mendelevium's atomic number is 9 higher than uranium's. Without these numeric details, the clue cannot be fully resolved to confirm that the element in question is mendelevium, and thus we cannot definitively identify the person it was named after from the given documents alone.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 653, + "osl": 749, + "total_tokens": 1402, + "latency_ms": 17645.51, + "tokens_per_second": 42.45 + }, + "context": { + "kept_docs_count": 2, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "427d706b-cd7a-4288-ad3b-4e915c279bbd", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T02:37:14.956535Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: I have an element in mind and would like you to identify the person it was named after. Here's a clue: The element's atomic number is 9 higher than that of an element discovered by the scientist who discovered Zirconium in the same year.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] nd an independent inventor of gravimetric analysis . His attention to detail and refusal to ignore discrepancies in results led to improvements in the use of apparatus. He was a major figure in understanding the composition of minerals and characterizing the elements. Klaproth discovered uranium (1789) and zirconium (1789). He was also involved in the discovery or co-discovery of titanium (1795), strontium (1793), cerium (1803), and chromium (1797) and confirmed the previous discoveries of tellurium (1798) and beryllium (1798). Klaproth was a member and director of the Berlin Academy of Sciences . He was recognized internationally as a member of the Royal Society in London, the Institut de France , and the Royal Swedish Academy of Sciences . Career Klaproth\n\n[DOC 2] d. Initial experiments were carried out in September 1954. No alpha decay was seen from mendelevium atoms; thus, Ghiorso suggested that the mendelevium had all decayed by electron capture to fermium-256 , correctly believed to decay primarily by fission, and that the experiment should be repeated, this time searching for those spontaneous fission events. This version of the experiment was performed in February 1955. The element was named after Dmitri Mendeleev . On the day of discovery, 19 February, alpha irradiation of the einsteinium target occurred in three three-hour sessions. The cyclotron was in the University of California campus, while the Radiation Laboratory was on the next hill. To deal with this situation, a complex procedure was used: Ghiorso to\n\n\nSEARCH HISTORY: - Query: 'Klaproth zirconium uranium' → 2 docs\n- Query: 'uranium atomic number' → 1 docs\n- Query: 'Mendelevium named after' → 1 docs\n- Query: 'Klaproth 1789 zirconium uranium' → 0 docs\n- Query: 'Uranium atomic number' → 0 docs\n- Query: 'Mendelevium named after Dmitri Mendeleev' → 0 docs\n\nFEEDBACK: Need to confirm Klaproth discovered both zirconium and uranium in the same year (1789), get uranium's atomic number (92), add 9 to get element 101, and verify element 101 (Mendelevium) is named after Dmitri Mendeleev.\nNeed to verify Klaproth discovered both zirconium and uranium in 1789, retrieve uranium's atomic number (92), add 9 to get element 101, and confirm element 101 (Mendelevium) is named after Dmitri Mendeleev.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Uranium atomic number periodic table\",\n \"Martin Klaproth zirconium uranium 1789\",\n \"Mendelevium named after Dmitri Mendeleev\"\n ],\n \"feedback\": \"Need to verify Klaproth discovered both zirconium and uranium in 1789, get uranium's atomic number (92), add 9 to reach element 101, and confirm element 101 (Mendelevium) is named after Dmitri Mendeleev.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 980, + "osl": 109, + "total_tokens": 1089, + "latency_ms": 5162.69, + "tokens_per_second": 21.11 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "18ba0ad1-ff78-432a-b0d5-099f4b16b86b", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T02:37:17.783119Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: I have an element in mind and would like you to identify the person it was named after. Here's a clue: The element's atomic number is 9 higher than that of an element discovered by the scientist who discovered Zirconium in the same year.\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] linium Terbium Dysprosium Holmium Erbium Thulium Ytterbium Lutetium Hafnium Tantalum Tungsten Rhenium Osmium Iridium Platinum Gold Mercury (element) Thallium Lead Bismuth Polonium Astatine Radon Francium Radium Actinium Thorium Protactinium Uranium Neptunium Plutonium Americium Curium Berkelium Californium Einsteinium Fermium Mendelevium Nobelium Lawrencium Rutherfordium Dubnium Seaborgium Bohrium Hassium Meitnerium Darmstadtium Roentgenium Copernicium Nihonium Flerovium Moscovium Livermorium Tennessine Oganesson Cu ↑ Ag ↓ Au palladium ← silver → cadmium Atomic number ( Z ) 47 Group group 11 Period period 5 Block d-block Electron configuration [ Kr ] 4d 10 5s 1 Electrons per shell 2, 8, 18, 18, 1 Physical properties Phase at STP solid Melting point 1234.93 K\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] nd an independent inventor of gravimetric analysis . His attention to detail and refusal to ignore discrepancies in results led to improvements in the use of apparatus. He was a major figure in understanding the composition of minerals and characterizing the elements. Klaproth discovered uranium (17...\n[KEPT 2] d. Initial experiments were carried out in September 1954. No alpha decay was seen from mendelevium atoms; thus, Ghiorso suggested that the mendelevium had all decayed by electron capture to fermium-256 , correctly believed to decay primarily by fission, and that the experiment should be repeated, t...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 658, + "osl": 546, + "total_tokens": 1204, + "latency_ms": 2216.48, + "tokens_per_second": 246.34 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "70cb5642-c20f-43cc-a0ee-2822a89e5b16", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T02:37:32.368355Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: I have an element in mind and would like you to identify the person it was named after. Here's a clue: The element's atomic number is 9 higher than that of an element discovered by the scientist who discovered Zirconium in the same year.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] nd an independent inventor of gravimetric analysis . His attention to detail and refusal to ignore discrepancies in results led to improvements in the use of apparatus. He was a major figure in understanding the composition of minerals and characterizing the elements. Klaproth discovered uranium (1789) and zirconium (1789). He was also involved in the discovery or co-discovery of titanium (1795), strontium (1793), cerium (1803), and chromium (1797) and confirmed the previous discoveries of tellurium (1798) and beryllium (1798). Klaproth was a member and director of the Berlin Academy of Sciences . He was recognized internationally as a member of the Royal Society in London, the Institut de France , and the Royal Swedish Academy of Sciences . Career Klaproth\n\n[DOC 2] d. Initial experiments were carried out in September 1954. No alpha decay was seen from mendelevium atoms; thus, Ghiorso suggested that the mendelevium had all decayed by electron capture to fermium-256 , correctly believed to decay primarily by fission, and that the experiment should be repeated, this time searching for those spontaneous fission events. This version of the experiment was performed in February 1955. The element was named after Dmitri Mendeleev . On the day of discovery, 19 February, alpha irradiation of the einsteinium target occurred in three three-hour sessions. The cyclotron was in the University of California campus, while the Radiation Laboratory was on the next hill. To deal with this situation, a complex procedure was used: Ghiorso to\n\n[DOC 3] linium Terbium Dysprosium Holmium Erbium Thulium Ytterbium Lutetium Hafnium Tantalum Tungsten Rhenium Osmium Iridium Platinum Gold Mercury (element) Thallium Lead Bismuth Polonium Astatine Radon Francium Radium Actinium Thorium Protactinium Uranium Neptunium Plutonium Americium Curium Berkelium Californium Einsteinium Fermium Mendelevium Nobelium Lawrencium Rutherfordium Dubnium Seaborgium Bohrium Hassium Meitnerium Darmstadtium Roentgenium Copernicium Nihonium Flerovium Moscovium Livermorium Tennessine Oganesson Cu ↑ Ag ↓ Au palladium ← silver → cadmium Atomic number ( Z ) 47 Group group 11 Period period 5 Block d-block Electron configuration [ Kr ] 4d 10 5s 1 Electrons per shell 2, 8, 18, 18, 1 Physical properties Phase at STP solid Melting point 1234.93 K\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents confirm that Klaproth discovered zirconium and also discovered uranium in the same year (DOC 1), and that mendelevium is named after Dmitri Mendeleev (DOC 2). However, they do not provide the atomic numbers of uranium (92) or the element 9 higher (101), nor a direct link showing that element 101 is the one named after Mendeleev. Without the atomic numbers, the required numeric relationship cannot be verified solely from the kept documents.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 886, + "osl": 670, + "total_tokens": 1556, + "latency_ms": 14584.51, + "tokens_per_second": 45.94 + }, + "context": { + "kept_docs_count": 3, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "4e07761c-f357-4b43-a2a9-4269bb863357", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T02:37:34.615044Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: I have an element in mind and would like you to identify the person it was named after. Here's a clue: The element's atomic number is 9 higher than that of an element discovered by the scientist who discovered Zirconium in the same year.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] nd an independent inventor of gravimetric analysis . His attention to detail and refusal to ignore discrepancies in results led to improvements in the use of apparatus. He was a major figure in understanding the composition of minerals and characterizing the elements. Klaproth discovered uranium (1789) and zirconium (1789). He was also involved in the discovery or co-discovery of titanium (1795), strontium (1793), cerium (1803), and chromium (1797) and confirmed the previous discoveries of tellurium (1798) and beryllium (1798). Klaproth was a member and director of the Berlin Academy of Sciences . He was recognized internationally as a member of the Royal Society in London, the Institut de France , and the Royal Swedish Academy of Sciences . Career Klaproth\n\n[DOC 2] d. Initial experiments were carried out in September 1954. No alpha decay was seen from mendelevium atoms; thus, Ghiorso suggested that the mendelevium had all decayed by electron capture to fermium-256 , correctly believed to decay primarily by fission, and that the experiment should be repeated, this time searching for those spontaneous fission events. This version of the experiment was performed in February 1955. The element was named after Dmitri Mendeleev . On the day of discovery, 19 February, alpha irradiation of the einsteinium target occurred in three three-hour sessions. The cyclotron was in the University of California campus, while the Radiation Laboratory was on the next hill. To deal with this situation, a complex procedure was used: Ghiorso to\n\n[DOC 3] linium Terbium Dysprosium Holmium Erbium Thulium Ytterbium Lutetium Hafnium Tantalum Tungsten Rhenium Osmium Iridium Platinum Gold Mercury (element) Thallium Lead Bismuth Polonium Astatine Radon Francium Radium Actinium Thorium Protactinium Uranium Neptunium Plutonium Americium Curium Berkelium Californium Einsteinium Fermium Mendelevium Nobelium Lawrencium Rutherfordium Dubnium Seaborgium Bohrium Hassium Meitnerium Darmstadtium Roentgenium Copernicium Nihonium Flerovium Moscovium Livermorium Tennessine Oganesson Cu ↑ Ag ↓ Au palladium ← silver → cadmium Atomic number ( Z ) 47 Group group 11 Period period 5 Block d-block Electron configuration [ Kr ] 4d 10 5s 1 Electrons per shell 2, 8, 18, 18, 1 Physical properties Phase at STP solid Melting point 1234.93 K\n\n\nSEARCH HISTORY: - Query: 'Klaproth zirconium uranium' → 2 docs\n- Query: 'uranium atomic number' → 1 docs\n- Query: 'Mendelevium named after' → 1 docs\n- Query: 'Klaproth 1789 zirconium uranium' → 0 docs\n- Query: 'Uranium atomic number' → 0 docs\n- Query: 'Mendelevium named after Dmitri Mendeleev' → 0 docs\n- Query: 'Uranium atomic number periodic table' → 1 docs\n- Query: 'Martin Klaproth zirconium uranium 1789' → 0 docs\n- Query: 'Mendelevium named after Dmitri Mendeleev' → 0 docs\n\nFEEDBACK: Need to confirm Klaproth discovered both zirconium and uranium in the same year (1789), get uranium's atomic number (92), add 9 to get element 101, and verify element 101 (Mendelevium) is named after Dmitri Mendeleev.\nNeed to verify Klaproth discovered both zirconium and uranium in 1789, retrieve uranium's atomic number (92), add 9 to get element 101, and confirm element 101 (Mendelevium) is named after Dmitri Mendeleev.\nNeed to verify Klaproth discovered both zirconium and uranium in 1789, get uranium's atomic number (92), add 9 to reach element 101, and confirm element 101 (Mendelevium) is named after Dmitri Mendeleev.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Martin Klaproth zirconium uranium 1789\",\n \"Uranium element atomic number\",\n \"Mendelevium\"\n ],\n \"feedback\": \"Need to verify Klaproth's dual discovery in 1789, obtain uranium's atomic number (92), and confirm element 101 (Mendelevium) is named after Dmitri Mendeleev.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1383, + "osl": 631, + "total_tokens": 2014, + "latency_ms": 2245.95, + "tokens_per_second": 280.95 + }, + "context": { + "kept_docs_count": 3, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "3ede6962-f25c-4a5b-b1cd-48f1fc352255", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T02:37:41.730054Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: I have an element in mind and would like you to identify the person it was named after. Here's a clue: The element's atomic number is 9 higher than that of an element discovered by the scientist who discovered Zirconium in the same year.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] nd an independent inventor of gravimetric analysis . His attention to detail and refusal to ignore discrepancies in results led to improvements in the use of apparatus. He was a major figure in understanding the composition of minerals and characterizing the elements. Klaproth discovered uranium (1789) and zirconium (1789). He was also involved in the discovery or co-discovery of titanium (1795), strontium (1793), cerium (1803), and chromium (1797) and confirmed the previous discoveries of tellurium (1798) and beryllium (1798). Klaproth was a member and director of the Berlin Academy of Sciences . He was recognized internationally as a member of the Royal Society in London, the Institut de France , and the Royal Swedish Academy of Sciences . Career Klaproth\n\n[DOC 2] d. Initial experiments were carried out in September 1954. No alpha decay was seen from mendelevium atoms; thus, Ghiorso suggested that the mendelevium had all decayed by electron capture to fermium-256 , correctly believed to decay primarily by fission, and that the experiment should be repeated, this time searching for those spontaneous fission events. This version of the experiment was performed in February 1955. The element was named after Dmitri Mendeleev . On the day of discovery, 19 February, alpha irradiation of the einsteinium target occurred in three three-hour sessions. The cyclotron was in the University of California campus, while the Radiation Laboratory was on the next hill. To deal with this situation, a complex procedure was used: Ghiorso to\n\n[DOC 3] linium Terbium Dysprosium Holmium Erbium Thulium Ytterbium Lutetium Hafnium Tantalum Tungsten Rhenium Osmium Iridium Platinum Gold Mercury (element) Thallium Lead Bismuth Polonium Astatine Radon Francium Radium Actinium Thorium Protactinium Uranium Neptunium Plutonium Americium Curium Berkelium Californium Einsteinium Fermium Mendelevium Nobelium Lawrencium Rutherfordium Dubnium Seaborgium Bohrium Hassium Meitnerium Darmstadtium Roentgenium Copernicium Nihonium Flerovium Moscovium Livermorium Tennessine Oganesson Cu ↑ Ag ↓ Au palladium ← silver → cadmium Atomic number ( Z ) 47 Group group 11 Period period 5 Block d-block Electron configuration [ Kr ] 4d 10 5s 1 Electrons per shell 2, 8, 18, 18, 1 Physical properties Phase at STP solid Melting point 1234.93 K\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": true,\n \"reasoning\": \"Doc 1 identifies Martin Heinrich Klaproth as the scientist who discovered Zirconium and also discovered uranium in the same year (1789). The element discovered in that year (uranium) has atomic number 92. Adding 9 gives atomic number 101, which corresponds to mendelevium. Doc 2 states that mendelevium was named after Dmitri Mendeleev. Therefore, the required person is Dmitri Mendeleev.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 852, + "osl": 109, + "total_tokens": 961, + "latency_ms": 6511.21, + "tokens_per_second": 16.74 + }, + "context": { + "kept_docs_count": 3, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "a9340cd0-93d7-434f-b219-956024dfe619", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T02:37:45.785013Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: I have an element in mind and would like you to identify the person it was named after. Here's a clue: The element's atomic number is 9 higher than that of an element discovered by the scientist who discovered Zirconium in the same year.\n\nDOCUMENTS:\n\n[DOC 1] nd an independent inventor of gravimetric analysis . His attention to detail and refusal to ignore discrepancies in results led to improvements in the use of apparatus. He was a major figure in understanding the composition of minerals and characterizing the elements. Klaproth discovered uranium (1789) and zirconium (1789). He was also involved in the discovery or co-discovery of titanium (1795), strontium (1793), cerium (1803), and chromium (1797) and confirmed the previous discoveries of tellurium (1798) and beryllium (1798). Klaproth was a member and director of the Berlin Academy of Sciences . He was recognized internationally as a member of the Royal Society in London, the Institut de France , and the Royal Swedish Academy of Sciences . Career Klaproth\n\n[DOC 2] d. Initial experiments were carried out in September 1954. No alpha decay was seen from mendelevium atoms; thus, Ghiorso suggested that the mendelevium had all decayed by electron capture to fermium-256 , correctly believed to decay primarily by fission, and that the experiment should be repeated, this time searching for those spontaneous fission events. This version of the experiment was performed in February 1955. The element was named after Dmitri Mendeleev . On the day of discovery, 19 February, alpha irradiation of the einsteinium target occurred in three three-hour sessions. The cyclotron was in the University of California campus, while the Radiation Laboratory was on the next hill. To deal with this situation, a complex procedure was used: Ghiorso to\n\n[DOC 3] linium Terbium Dysprosium Holmium Erbium Thulium Ytterbium Lutetium Hafnium Tantalum Tungsten Rhenium Osmium Iridium Platinum Gold Mercury (element) Thallium Lead Bismuth Polonium Astatine Radon Francium Radium Actinium Thorium Protactinium Uranium Neptunium Plutonium Americium Curium Berkelium Californium Einsteinium Fermium Mendelevium Nobelium Lawrencium Rutherfordium Dubnium Seaborgium Bohrium Hassium Meitnerium Darmstadtium Roentgenium Copernicium Nihonium Flerovium Moscovium Livermorium Tennessine Oganesson Cu ↑ Ag ↓ Au palladium ← silver → cadmium Atomic number ( Z ) 47 Group group 11 Period period 5 Block d-block Electron configuration [ Kr ] 4d 10 5s 1 Electrons per shell 2, 8, 18, 18, 1 Physical properties Phase at STP solid Melting point 1234.93 K\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Dmitri Mendeleev", + "finish_reason": "stop" + }, + "metrics": { + "isl": 755, + "osl": 286, + "total_tokens": 1041, + "latency_ms": 4054.14, + "tokens_per_second": 70.55 + }, + "context": { + "num_documents_used": 3, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:37:45.785739Z", + "summary": { + "total_llm_calls": 11, + "total_hops": 5, + "total_input_tokens": 9245, + "total_output_tokens": 5236, + "total_tokens": 14481, + "total_latency_ms": 84631.88, + "average_tokens_per_second": 106.95, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Zirconium", + "https://en.wikipedia.org/wiki/Martin_Heinrich_Klaproth", + "https://en.wikipedia.org/wiki/Uranium", + "https://en.wikipedia.org/wiki/Mendelevium", + "https://en.wikipedia.org/wiki/Periodic_table" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Dmitri Mendeleev", + "ground_truth_answer": "Mendelevium is named after Dmitri Mendeleev." + } + }, + { + "query_id": "7", + "original_query": "As of Aug 3, 2024, the artist who released the album \"Father of Asahd\" went to the same high school as an Olympic diver. How many Olympic teams did this diver participate on?", + "timestamp_start": "2026-05-16T02:37:45.791945Z", + "llm_calls": [ + { + "call_id": "2eaacd8d-40a4-4886-bad7-9fe24f1217e4", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:37:48.180332Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of Aug 3, 2024, the artist who released the album \"Father of Asahd\" went to the same high school as an Olympic diver. How many Olympic teams did this diver participate on?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Father of Asahd artist\",\n \"DJ Khaled high school\",\n \"Olympic diver Miami Northwestern\"\n ],\n \"feedback\": \"First query identifies DJ Khaled as the album artist. Second retrieves the high school he attended. Third finds the Olympic diver who went to the same high school, enabling us to determine the number of Olympic teams the diver participated on.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 490, + "osl": 647, + "total_tokens": 1137, + "latency_ms": 2388.27, + "tokens_per_second": 270.91 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "9c3fed0d-2dce-44ce-91e6-69a7f6b9ee76", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:37:53.805247Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of Aug 3, 2024, the artist who released the album \"Father of Asahd\" went to the same high school as an Olympic diver. How many Olympic teams did this diver participate on?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Type Father of Asahd DJ Khaled in the \"Search:\" field. ^\n\n[NEW 2] ed Mohammed Khaled ( / ˈ k æ l ɪ d / ; Arabic : خالد محمد خالد , romanized : Khālid Muḥammad Khālid ; born November 26, 1975), known professionally as DJ Khaled , is an American DJ and record producer. Originally a Miami -based radio personality, Khaled has since become known for enlisting high-profile music industry artists to perform on singles and albums, for which he often serves as producer and hype man . Known as the \" Anthem King \", his distinctions are his booming voice presence, \"motivational\" abstractions, maximalist production style and numerous catchphrases . Khaled was born in New Orleans , Louisiana, to Palestinian immigrant parents. He first gained recognition as a radio host in the 1990s for the radio station 99 Jamz , and translated his popu\n\n[NEW 3] Fellowship , Aceyalone , Living Legends , Wu-Tang Clan , Mobb Deep , Nas , and Talib Kweli as his major influences. Haggerty attended Garfield High School and Nathan Hale High School . At Hale, he developed the stage name \"Professor Macklemore\" for an art project involving a made-up superhero, and at Garfield, he started a hip-hop group named Elevated Elements with other students. The group released an album, Progress , in 2000. Haggerty enrolled at the College of Santa Fe for a year, later saying in an interview that \"[i]t was a very pivotal time in a lot of ways. Santa Fe was the place where I got good at rapping\". After not getting into the music program, he moved back to Seattle. Haggerty later enrolled at Evergreen State College in Olympia and complete\n\n[NEW 4] Puerto Rican diver Mark Ruiz Personal information Born ( 1979-04-09 ) April 9, 1979 (age 46) Río Piedras, Puerto Rico Medal record Men's diving Pan American Games Representing Puerto Rico 1999 Winnipeg 3m Springboard Representing the United States 2003 S. Domingo Platform Synchro Mark Ruiz (born April 9, 1979 in Río Piedras, Puerto Rico ), is an Olympic diver from Puerto Rico . Ruiz represented the United States in the 2000 Olympic Games and the 2004 Olympic Games . Childhood Ruiz took up diving at the age of 9. Upon a coach's suggestion, he moved to the U. S. at 12 to take advantage of the better training system. During the spring nationals in 1999, he swept all three events (10-meter platform, 3-meter springboard and the non-Olympic event of 1-meter spring\n\n[NEW 5] x United States Athletics 2012 Summer F 3 0 0 3 Dana Vollmer United States Swimming 2012 Summer F 3 0 0 3 Marit Bjørgen Norway Cross-country skiing 2014 Winter F 3 0 0 3 Darya Domracheva Belarus Biathlon 2014 Winter F 3 0 0 3 Usain Bolt Jamaica Athletics 2016 Summer M 3 0 0 3 Jason Kenny Great Britain Cycling 2016 Summer M 3 0 0 3 Danuta Kozák Hungary Canoeing 2016 Summer F 3 0 0 3 Ryan Murphy United States Swimming 2016 Summer M 3 0 0 3 Martin Fourcade France Biathlon 2018 Winter M 3 0 0 3 Johannes Høsflot Klæbo Norway Cross-country skiing 2018 Winter M 3 0 0 3 An San South Korea Archery 2020 Summer F 3 0 0 3 Lisa Carrington New Zealand Canoeing 2020 Summer F 3 0 0 3 Elaine Thompson-Herah Jamaica Athletics 2020 Summer F 3 0 0 3 Therese Johaug Norway Cross-c\n\n[NEW 6] ngju 4×200 m freestyle 2023 Fukuoka 400 m freestyle 2023 Fukuoka 4×200 m freestyle 2025 Singapore 4×200 m freestyle 2025 Singapore 400 m freestyle Pan Pacific Championships (LC) 2014 Gold Coast 200 m freestyle 2014 Gold Coast 400 m freestyle 2014 Gold Coast 800 m freestyle 2014 Gold Coast 1500 m freestyle 2014 Gold Coast 4×200 m freestyle 2018 Tokyo 400 m freestyle 2018 Tokyo 800 m freestyle 2018 Tokyo 1500 m freestyle 2018 Tokyo 4×200 m freestyle 2018 Tokyo 200 m freestyle Kathleen Genevieve Ledecky ( / l ə ˈ d ɛ k i / i lə-DEK -ee ; born March 17, 1997) is an American competitive swimmer . She is the most decorated female swimmer in history and the most decorated American woman in Olympic history, with a total of 14 Olympic medals, including nine golds. Sh\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 6 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 1, 0, 1, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1352, + "osl": 515, + "total_tokens": 1867, + "latency_ms": 5090.08, + "tokens_per_second": 101.18 + }, + "context": { + "num_documents_evaluated": 6, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "493b71fa-8949-4b26-a3d0-5e1137306bc8", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:37:56.355316Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of Aug 3, 2024, the artist who released the album \"Father of Asahd\" went to the same high school as an Olympic diver. How many Olympic teams did this diver participate on?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Type Father of Asahd DJ Khaled in the \"Search:\" field. ^\n\n[DOC 2] ed Mohammed Khaled ( / ˈ k æ l ɪ d / ; Arabic : خالد محمد خالد , romanized : Khālid Muḥammad Khālid ; born November 26, 1975), known professionally as DJ Khaled , is an American DJ and record producer. Originally a Miami -based radio personality, Khaled has since become known for enlisting high-profile music industry artists to perform on singles and albums, for which he often serves as producer and hype man . Known as the \" Anthem King \", his distinctions are his booming voice presence, \"motivational\" abstractions, maximalist production style and numerous catchphrases . Khaled was born in New Orleans , Louisiana, to Palestinian immigrant parents. He first gained recognition as a radio host in the 1990s for the radio station 99 Jamz , and translated his popu\n\n[DOC 3] Puerto Rican diver Mark Ruiz Personal information Born ( 1979-04-09 ) April 9, 1979 (age 46) Río Piedras, Puerto Rico Medal record Men's diving Pan American Games Representing Puerto Rico 1999 Winnipeg 3m Springboard Representing the United States 2003 S. Domingo Platform Synchro Mark Ruiz (born April 9, 1979 in Río Piedras, Puerto Rico ), is an Olympic diver from Puerto Rico . Ruiz represented the United States in the 2000 Olympic Games and the 2004 Olympic Games . Childhood Ruiz took up diving at the age of 9. Upon a coach's suggestion, he moved to the U. S. at 12 to take advantage of the better training system. During the spring nationals in 1999, he swept all three events (10-meter platform, 3-meter springboard and the non-Olympic event of 1-meter spring\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents identify DJ Khaled as the artist who released the album \\\"Father of Asahd\\\" and provide details about diver Mark Ruiz, including his participation in the 2000 and 2004 Olympic Games (indicating two Olympic teams). However, none of the documents contain information about the high school attended by DJ Khaled or by Mark Ruiz, so we cannot confirm that they went to the same high school. Without that link, we cannot answer the question.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 716, + "osl": 360, + "total_tokens": 1076, + "latency_ms": 2549.12, + "tokens_per_second": 141.23 + }, + "context": { + "kept_docs_count": 3, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "cda586b6-134e-4584-8dce-98aa942d4f01", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:38:01.963832Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of Aug 3, 2024, the artist who released the album \"Father of Asahd\" went to the same high school as an Olympic diver. How many Olympic teams did this diver participate on?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Type Father of Asahd DJ Khaled in the \"Search:\" field. ^\n\n[DOC 2] ed Mohammed Khaled ( / ˈ k æ l ɪ d / ; Arabic : خالد محمد خالد , romanized : Khālid Muḥammad Khālid ; born November 26, 1975), known professionally as DJ Khaled , is an American DJ and record producer. Originally a Miami -based radio personality, Khaled has since become known for enlisting high-profile music industry artists to perform on singles and albums, for which he often serves as producer and hype man . Known as the \" Anthem King \", his distinctions are his booming voice presence, \"motivational\" abstractions, maximalist production style and numerous catchphrases . Khaled was born in New Orleans , Louisiana, to Palestinian immigrant parents. He first gained recognition as a radio host in the 1990s for the radio station 99 Jamz , and translated his popu\n\n[DOC 3] Puerto Rican diver Mark Ruiz Personal information Born ( 1979-04-09 ) April 9, 1979 (age 46) Río Piedras, Puerto Rico Medal record Men's diving Pan American Games Representing Puerto Rico 1999 Winnipeg 3m Springboard Representing the United States 2003 S. Domingo Platform Synchro Mark Ruiz (born April 9, 1979 in Río Piedras, Puerto Rico ), is an Olympic diver from Puerto Rico . Ruiz represented the United States in the 2000 Olympic Games and the 2004 Olympic Games . Childhood Ruiz took up diving at the age of 9. Upon a coach's suggestion, he moved to the U. S. at 12 to take advantage of the better training system. During the spring nationals in 1999, he swept all three events (10-meter platform, 3-meter springboard and the non-Olympic event of 1-meter spring\n\n\nSEARCH HISTORY: - Query: 'Father of Asahd artist' → 1 docs\n- Query: 'DJ Khaled high school' → 2 docs\n- Query: 'Olympic diver Miami Northwestern' → 3 docs\n\nFEEDBACK: First query identifies DJ Khaled as the album artist. Second retrieves the high school he attended. Third finds the Olympic diver who went to the same high school, enabling us to determine the number of Olympic teams the diver participated on.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"DJ Khaled high school\",\n \"Mark Ruiz high school\",\n \"Mark Ruiz Olympic teams\"\n ],\n \"feedback\": \"First query confirms DJ Khaled attended Miami Northwestern Senior High School. Second query links diver Mark Ruiz to the same high school. Third query provides the number of Olympic teams (i.e., Olympic Games) Mark Ruiz competed on.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 960, + "osl": 317, + "total_tokens": 1277, + "latency_ms": 5607.85, + "tokens_per_second": 56.53 + }, + "context": { + "kept_docs_count": 3, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "d0285d27-3b6f-40a5-be78-60eb5c68179d", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T02:38:04.397925Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of Aug 3, 2024, the artist who released the album \"Father of Asahd\" went to the same high school as an Olympic diver. How many Olympic teams did this diver participate on?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] er United States Hector Hogan Australia 1960 Rome details Armin Hary United Team of Germany Dave Sime United States Peter Radford Great Britain 1964 Tokyo details Bob Hayes United States Enrique Figuerola Cuba Harry Jerome Canada 1968 Mexico City details Jim Hines United States Lennox Miller Jamaica Charles Greene United States 1972 Munich details Valeriy Borzov Soviet Union Robert Taylor United States Lennox Miller Jamaica 1976 Montreal details Hasely Crawford Trinidad and Tobago Don Quarrie Jamaica Valeriy Borzov Soviet Union 1980 Moscow details Allan Wells Great Britain Silvio Leonard Cuba Petar Petrov Bulgaria 1984 Los Angeles details Carl Lewis United States Sam Graddy United States Ben Johnson Canada 1988 Seoul details Carl Lewis United States Linford\n\n\nKEPT DOCUMENTS (context only):\n[3 documents already kept as relevant]\n[KEPT 1] Type Father of Asahd DJ Khaled in the \"Search:\" field. ^...\n[KEPT 2] ed Mohammed Khaled ( / ˈ k æ l ɪ d / ; Arabic : خالد محمد خالد , romanized : Khālid Muḥammad Khālid ; born November 26, 1975), known professionally as DJ Khaled , is an American DJ and record producer. Originally a Miami -based radio personality, Khaled has since become known for enlisting high-prof...\n[KEPT 3] Puerto Rican diver Mark Ruiz Personal information Born ( 1979-04-09 ) April 9, 1979 (age 46) Río Piedras, Puerto Rico Medal record Men's diving Pan American Games Representing Puerto Rico 1999 Winnipeg 3m Springboard Representing the United States 2003 S. Domingo Platform Synchro Mark Ruiz (born Apr...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 638, + "osl": 383, + "total_tokens": 1021, + "latency_ms": 1873.01, + "tokens_per_second": 204.48 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 3 + } + }, + { + "call_id": "94b5c1b4-7dcf-420f-958e-cc10e80b3c92", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T02:38:07.055965Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of Aug 3, 2024, the artist who released the album \"Father of Asahd\" went to the same high school as an Olympic diver. How many Olympic teams did this diver participate on?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Type Father of Asahd DJ Khaled in the \"Search:\" field. ^\n\n[DOC 2] ed Mohammed Khaled ( / ˈ k æ l ɪ d / ; Arabic : خالد محمد خالد , romanized : Khālid Muḥammad Khālid ; born November 26, 1975), known professionally as DJ Khaled , is an American DJ and record producer. Originally a Miami -based radio personality, Khaled has since become known for enlisting high-profile music industry artists to perform on singles and albums, for which he often serves as producer and hype man . Known as the \" Anthem King \", his distinctions are his booming voice presence, \"motivational\" abstractions, maximalist production style and numerous catchphrases . Khaled was born in New Orleans , Louisiana, to Palestinian immigrant parents. He first gained recognition as a radio host in the 1990s for the radio station 99 Jamz , and translated his popu\n\n[DOC 3] Puerto Rican diver Mark Ruiz Personal information Born ( 1979-04-09 ) April 9, 1979 (age 46) Río Piedras, Puerto Rico Medal record Men's diving Pan American Games Representing Puerto Rico 1999 Winnipeg 3m Springboard Representing the United States 2003 S. Domingo Platform Synchro Mark Ruiz (born April 9, 1979 in Río Piedras, Puerto Rico ), is an Olympic diver from Puerto Rico . Ruiz represented the United States in the 2000 Olympic Games and the 2004 Olympic Games . Childhood Ruiz took up diving at the age of 9. Upon a coach's suggestion, he moved to the U. S. at 12 to take advantage of the better training system. During the spring nationals in 1999, he swept all three events (10-meter platform, 3-meter springboard and the non-Olympic event of 1-meter spring\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide the artist (DJ Khaled) and the diver (Mark Ruiz) and note that the diver participated in two Olympic Games (2000 and 2004). However, none of the kept documents contain information about the high schools attended by DJ Khaled or Mark Ruiz, so we cannot confirm that they went to the same high school. The required fact linking the two individuals is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 728, + "osl": 420, + "total_tokens": 1148, + "latency_ms": 2657.34, + "tokens_per_second": 158.05 + }, + "context": { + "kept_docs_count": 3, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "022421c0-0e3b-4e91-8756-84ca34d45afa", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T02:38:12.088244Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of Aug 3, 2024, the artist who released the album \"Father of Asahd\" went to the same high school as an Olympic diver. How many Olympic teams did this diver participate on?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Type Father of Asahd DJ Khaled in the \"Search:\" field. ^\n\n[DOC 2] ed Mohammed Khaled ( / ˈ k æ l ɪ d / ; Arabic : خالد محمد خالد , romanized : Khālid Muḥammad Khālid ; born November 26, 1975), known professionally as DJ Khaled , is an American DJ and record producer. Originally a Miami -based radio personality, Khaled has since become known for enlisting high-profile music industry artists to perform on singles and albums, for which he often serves as producer and hype man . Known as the \" Anthem King \", his distinctions are his booming voice presence, \"motivational\" abstractions, maximalist production style and numerous catchphrases . Khaled was born in New Orleans , Louisiana, to Palestinian immigrant parents. He first gained recognition as a radio host in the 1990s for the radio station 99 Jamz , and translated his popu\n\n[DOC 3] Puerto Rican diver Mark Ruiz Personal information Born ( 1979-04-09 ) April 9, 1979 (age 46) Río Piedras, Puerto Rico Medal record Men's diving Pan American Games Representing Puerto Rico 1999 Winnipeg 3m Springboard Representing the United States 2003 S. Domingo Platform Synchro Mark Ruiz (born April 9, 1979 in Río Piedras, Puerto Rico ), is an Olympic diver from Puerto Rico . Ruiz represented the United States in the 2000 Olympic Games and the 2004 Olympic Games . Childhood Ruiz took up diving at the age of 9. Upon a coach's suggestion, he moved to the U. S. at 12 to take advantage of the better training system. During the spring nationals in 1999, he swept all three events (10-meter platform, 3-meter springboard and the non-Olympic event of 1-meter spring\n\n\nSEARCH HISTORY: - Query: 'Father of Asahd artist' → 1 docs\n- Query: 'DJ Khaled high school' → 2 docs\n- Query: 'Olympic diver Miami Northwestern' → 3 docs\n- Query: 'DJ Khaled high school' → 0 docs\n- Query: 'Mark Ruiz high school' → 0 docs\n- Query: 'Mark Ruiz Olympic teams' → 1 docs\n\nFEEDBACK: First query identifies DJ Khaled as the album artist. Second retrieves the high school he attended. Third finds the Olympic diver who went to the same high school, enabling us to determine the number of Olympic teams the diver participated on.\nFirst query confirms DJ Khaled attended Miami Northwestern Senior High School. Second query links diver Mark Ruiz to the same high school. Third query provides the number of Olympic teams (i.e., Olympic Games) Mark Ruiz competed on.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"DJ Khaled high school\",\n \"Mark Ruiz Miami Northwestern\",\n \"Mark Ruiz Olympic Games\"\n ],\n \"feedback\": \"Need to confirm DJ Khaled's high school, identify the diver who attended the same high school (Mark Ruiz), and determine how many Olympic Games/teams he competed on.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1002, + "osl": 71, + "total_tokens": 1073, + "latency_ms": 5031.67, + "tokens_per_second": 14.11 + }, + "context": { + "kept_docs_count": 3, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "36a35116-948d-4ea2-97fb-b39c4e78993d", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T02:38:20.697803Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of Aug 3, 2024, the artist who released the album \"Father of Asahd\" went to the same high school as an Olympic diver. How many Olympic teams did this diver participate on?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] higan State Big Ten from San Francisco via Denver 4 138 Kansas City Chiefs L'Jarius Sneed S Louisiana Tech C-USA 4* 139 Las Vegas Raiders Amik Robertson CB Louisiana Tech C-USA from Tampa Bay via New England 4* 140 Jacksonville Jaguars Shaquille Quarterman ILB Miami (FL) ACC from Chicago 4* 141 Houston Texans John Reid CB Penn State Big Ten from Miami 4* 142 Washington Redskins Antonio Gandy-Golden WR Liberty Ind. (FBS) 4* 143 Baltimore Ravens Ben Bredeson G Michigan Big Ten 4* 144 Seattle Seahawks DeeJay Dallas RB Miami (FL) ACC 4* 145 Philadelphia Eagles Jack Driscoll G Auburn SEC 4* 146 Dallas Cowboys Tyler Biadasz † C Wisconsin Big Ten from Philadelphia 5 147 Cincinnati Bengals Khalid Kareem DE Notre Dame Ind. (FBS) 5 148 Seattle Seahawks Alton Robinson\n\n\nKEPT DOCUMENTS (context only):\n[3 documents already kept as relevant]\n[KEPT 1] Type Father of Asahd DJ Khaled in the \"Search:\" field. ^...\n[KEPT 2] ed Mohammed Khaled ( / ˈ k æ l ɪ d / ; Arabic : خالد محمد خالد , romanized : Khālid Muḥammad Khālid ; born November 26, 1975), known professionally as DJ Khaled , is an American DJ and record producer. Originally a Miami -based radio personality, Khaled has since become known for enlisting high-prof...\n[KEPT 3] Puerto Rican diver Mark Ruiz Personal information Born ( 1979-04-09 ) April 9, 1979 (age 46) Río Piedras, Puerto Rico Medal record Men's diving Pan American Games Representing Puerto Rico 1999 Winnipeg 3m Springboard Representing the United States 2003 S. Domingo Platform Synchro Mark Ruiz (born Apr...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 685, + "osl": 430, + "total_tokens": 1115, + "latency_ms": 8053.56, + "tokens_per_second": 53.39 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 3 + } + }, + { + "call_id": "692e5f26-a4d0-48aa-b0f2-8aeb03d907ff", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T02:38:23.104333Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of Aug 3, 2024, the artist who released the album \"Father of Asahd\" went to the same high school as an Olympic diver. How many Olympic teams did this diver participate on?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Type Father of Asahd DJ Khaled in the \"Search:\" field. ^\n\n[DOC 2] ed Mohammed Khaled ( / ˈ k æ l ɪ d / ; Arabic : خالد محمد خالد , romanized : Khālid Muḥammad Khālid ; born November 26, 1975), known professionally as DJ Khaled , is an American DJ and record producer. Originally a Miami -based radio personality, Khaled has since become known for enlisting high-profile music industry artists to perform on singles and albums, for which he often serves as producer and hype man . Known as the \" Anthem King \", his distinctions are his booming voice presence, \"motivational\" abstractions, maximalist production style and numerous catchphrases . Khaled was born in New Orleans , Louisiana, to Palestinian immigrant parents. He first gained recognition as a radio host in the 1990s for the radio station 99 Jamz , and translated his popu\n\n[DOC 3] Puerto Rican diver Mark Ruiz Personal information Born ( 1979-04-09 ) April 9, 1979 (age 46) Río Piedras, Puerto Rico Medal record Men's diving Pan American Games Representing Puerto Rico 1999 Winnipeg 3m Springboard Representing the United States 2003 S. Domingo Platform Synchro Mark Ruiz (born April 9, 1979 in Río Piedras, Puerto Rico ), is an Olympic diver from Puerto Rico . Ruiz represented the United States in the 2000 Olympic Games and the 2004 Olympic Games . Childhood Ruiz took up diving at the age of 9. Upon a coach's suggestion, he moved to the U. S. at 12 to take advantage of the better training system. During the spring nationals in 1999, he swept all three events (10-meter platform, 3-meter springboard and the non-Olympic event of 1-meter spring\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide that DJ Khaled released the album \\\"Father of Asahd\\\" (Doc 2) and that diver Mark Ruiz competed in the 2000 and 2004 Olympics (Doc 3), indicating he was on two Olympic teams. However, there is no information linking DJ Khaled and Mark Ruiz to the same high school, which is required to confirm that the diver in question is indeed Mark Ruiz. Therefore, the necessary fact connecting the artist to the diver's high school is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 728, + "osl": 423, + "total_tokens": 1151, + "latency_ms": 2405.64, + "tokens_per_second": 175.84 + }, + "context": { + "kept_docs_count": 3, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "dfdc7750-7fcb-496c-a015-d51389ed1b5e", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T02:38:33.349021Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of Aug 3, 2024, the artist who released the album \"Father of Asahd\" went to the same high school as an Olympic diver. How many Olympic teams did this diver participate on?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Type Father of Asahd DJ Khaled in the \"Search:\" field. ^\n\n[DOC 2] ed Mohammed Khaled ( / ˈ k æ l ɪ d / ; Arabic : خالد محمد خالد , romanized : Khālid Muḥammad Khālid ; born November 26, 1975), known professionally as DJ Khaled , is an American DJ and record producer. Originally a Miami -based radio personality, Khaled has since become known for enlisting high-profile music industry artists to perform on singles and albums, for which he often serves as producer and hype man . Known as the \" Anthem King \", his distinctions are his booming voice presence, \"motivational\" abstractions, maximalist production style and numerous catchphrases . Khaled was born in New Orleans , Louisiana, to Palestinian immigrant parents. He first gained recognition as a radio host in the 1990s for the radio station 99 Jamz , and translated his popu\n\n[DOC 3] Puerto Rican diver Mark Ruiz Personal information Born ( 1979-04-09 ) April 9, 1979 (age 46) Río Piedras, Puerto Rico Medal record Men's diving Pan American Games Representing Puerto Rico 1999 Winnipeg 3m Springboard Representing the United States 2003 S. Domingo Platform Synchro Mark Ruiz (born April 9, 1979 in Río Piedras, Puerto Rico ), is an Olympic diver from Puerto Rico . Ruiz represented the United States in the 2000 Olympic Games and the 2004 Olympic Games . Childhood Ruiz took up diving at the age of 9. Upon a coach's suggestion, he moved to the U. S. at 12 to take advantage of the better training system. During the spring nationals in 1999, he swept all three events (10-meter platform, 3-meter springboard and the non-Olympic event of 1-meter spring\n\n\nSEARCH HISTORY: - Query: 'Father of Asahd artist' → 1 docs\n- Query: 'DJ Khaled high school' → 2 docs\n- Query: 'Olympic diver Miami Northwestern' → 3 docs\n- Query: 'DJ Khaled high school' → 0 docs\n- Query: 'Mark Ruiz high school' → 0 docs\n- Query: 'Mark Ruiz Olympic teams' → 1 docs\n- Query: 'DJ Khaled high school' → 0 docs\n- Query: 'Mark Ruiz Miami Northwestern' → 1 docs\n- Query: 'Mark Ruiz Olympic Games' → 0 docs\n\nFEEDBACK: First query identifies DJ Khaled as the album artist. Second retrieves the high school he attended. Third finds the Olympic diver who went to the same high school, enabling us to determine the number of Olympic teams the diver participated on.\nFirst query confirms DJ Khaled attended Miami Northwestern Senior High School. Second query links diver Mark Ruiz to the same high school. Third query provides the number of Olympic teams (i.e., Olympic Games) Mark Ruiz competed on.\nNeed to confirm DJ Khaled's high school, identify the diver who attended the same high school (Mark Ruiz), and determine how many Olympic Games/teams he competed on.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"DJ Khaled alma mater\",\n \"Mark Ruiz diver high school\",\n \"Mark Ruiz Olympic appearances\"\n ],\n \"feedback\": \"We need to confirm DJ Khaled's high school, identify the diver who attended the same school, and determine how many Olympic Games (teams) the diver competed on.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1111, + "osl": 582, + "total_tokens": 1693, + "latency_ms": 10244.15, + "tokens_per_second": 56.81 + }, + "context": { + "kept_docs_count": 3, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "c4b36404-11c1-4748-9083-f5fc22603abd", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T02:38:40.280506Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of Aug 3, 2024, the artist who released the album \"Father of Asahd\" went to the same high school as an Olympic diver. How many Olympic teams did this diver participate on?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] ^ a b ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ Updated 2013 by Sig Gissler. ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ Office of Undergraduate Admissions site about Campus Life Archived April 19, 2012, at the Wayback Machine . Retrieved September 12, 2007. ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ Baker Field: Birthplace of Sports Television Archived October 7, 2008, at the Wayback Machine . Columbia University. ^ ^ ^ Losing streak Archived January 15, 2021, at the Wayback Machine . Wikicu. com. Retrieved on September 7, 2013. ^ ^ a b ^ ^ a b ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ The Age (2009). Melbourne-raised Gorman new chief of Morgan Stanley Archived January 15, 2021, at the Wayback Machine\n\n\nKEPT DOCUMENTS (context only):\n[3 documents already kept as relevant]\n[KEPT 1] Type Father of Asahd DJ Khaled in the \"Search:\" field. ^...\n[KEPT 2] ed Mohammed Khaled ( / ˈ k æ l ɪ d / ; Arabic : خالد محمد خالد , romanized : Khālid Muḥammad Khālid ; born November 26, 1975), known professionally as DJ Khaled , is an American DJ and record producer. Originally a Miami -based radio personality, Khaled has since become known for enlisting high-prof...\n[KEPT 3] Puerto Rican diver Mark Ruiz Personal information Born ( 1979-04-09 ) April 9, 1979 (age 46) Río Piedras, Puerto Rico Medal record Men's diving Pan American Games Representing Puerto Rico 1999 Winnipeg 3m Springboard Representing the United States 2003 S. Domingo Platform Synchro Mark Ruiz (born Apr...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 739, + "osl": 630, + "total_tokens": 1369, + "latency_ms": 6411.15, + "tokens_per_second": 98.27 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 3 + } + }, + { + "call_id": "35aee94f-2073-48e0-ab54-ab7cffdf42e3", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T02:38:44.288041Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of Aug 3, 2024, the artist who released the album \"Father of Asahd\" went to the same high school as an Olympic diver. How many Olympic teams did this diver participate on?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Type Father of Asahd DJ Khaled in the \"Search:\" field. ^\n\n[DOC 2] ed Mohammed Khaled ( / ˈ k æ l ɪ d / ; Arabic : خالد محمد خالد , romanized : Khālid Muḥammad Khālid ; born November 26, 1975), known professionally as DJ Khaled , is an American DJ and record producer. Originally a Miami -based radio personality, Khaled has since become known for enlisting high-profile music industry artists to perform on singles and albums, for which he often serves as producer and hype man . Known as the \" Anthem King \", his distinctions are his booming voice presence, \"motivational\" abstractions, maximalist production style and numerous catchphrases . Khaled was born in New Orleans , Louisiana, to Palestinian immigrant parents. He first gained recognition as a radio host in the 1990s for the radio station 99 Jamz , and translated his popu\n\n[DOC 3] Puerto Rican diver Mark Ruiz Personal information Born ( 1979-04-09 ) April 9, 1979 (age 46) Río Piedras, Puerto Rico Medal record Men's diving Pan American Games Representing Puerto Rico 1999 Winnipeg 3m Springboard Representing the United States 2003 S. Domingo Platform Synchro Mark Ruiz (born April 9, 1979 in Río Piedras, Puerto Rico ), is an Olympic diver from Puerto Rico . Ruiz represented the United States in the 2000 Olympic Games and the 2004 Olympic Games . Childhood Ruiz took up diving at the age of 9. Upon a coach's suggestion, he moved to the U. S. at 12 to take advantage of the better training system. During the spring nationals in 1999, he swept all three events (10-meter platform, 3-meter springboard and the non-Olympic event of 1-meter spring\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Documents provide the artist (DJ Khaled) and a diver (Mark Ruiz) with his Olympic participation (2000 and 2004, two teams). However, none of the kept documents mention the high school attendance of DJ Khaled or any link confirming that he attended the same high school as Mark Ruiz. The crucial fact linking the two individuals is missing, so we cannot confidently answer the question.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 728, + "osl": 697, + "total_tokens": 1425, + "latency_ms": 4006.67, + "tokens_per_second": 173.96 + }, + "context": { + "kept_docs_count": 3, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "9690ee2d-aca9-4e61-973d-a0dad7d911d1", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T02:38:52.262173Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: As of Aug 3, 2024, the artist who released the album \"Father of Asahd\" went to the same high school as an Olympic diver. How many Olympic teams did this diver participate on?\n\nDOCUMENTS:\n\n[DOC 1] Type Father of Asahd DJ Khaled in the \"Search:\" field. ^\n\n[DOC 2] ed Mohammed Khaled ( / ˈ k æ l ɪ d / ; Arabic : خالد محمد خالد , romanized : Khālid Muḥammad Khālid ; born November 26, 1975), known professionally as DJ Khaled , is an American DJ and record producer. Originally a Miami -based radio personality, Khaled has since become known for enlisting high-profile music industry artists to perform on singles and albums, for which he often serves as producer and hype man . Known as the \" Anthem King \", his distinctions are his booming voice presence, \"motivational\" abstractions, maximalist production style and numerous catchphrases . Khaled was born in New Orleans , Louisiana, to Palestinian immigrant parents. He first gained recognition as a radio host in the 1990s for the radio station 99 Jamz , and translated his popu\n\n[DOC 3] Puerto Rican diver Mark Ruiz Personal information Born ( 1979-04-09 ) April 9, 1979 (age 46) Río Piedras, Puerto Rico Medal record Men's diving Pan American Games Representing Puerto Rico 1999 Winnipeg 3m Springboard Representing the United States 2003 S. Domingo Platform Synchro Mark Ruiz (born April 9, 1979 in Río Piedras, Puerto Rico ), is an Olympic diver from Puerto Rico . Ruiz represented the United States in the 2000 Olympic Games and the 2004 Olympic Games . Childhood Ruiz took up diving at the age of 9. Upon a coach's suggestion, he moved to the U. S. at 12 to take advantage of the better training system. During the spring nationals in 1999, he swept all three events (10-meter platform, 3-meter springboard and the non-Olympic event of 1-meter spring\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "2", + "finish_reason": "stop" + }, + "metrics": { + "isl": 558, + "osl": 392, + "total_tokens": 950, + "latency_ms": 7973.54, + "tokens_per_second": 49.16 + }, + "context": { + "num_documents_used": 3, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:38:52.262925Z", + "summary": { + "total_llm_calls": 13, + "total_hops": 5, + "total_input_tokens": 10435, + "total_output_tokens": 5867, + "total_tokens": 16302, + "total_latency_ms": 64292.05, + "average_tokens_per_second": 119.53, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Father_of_Asahd", + "https://en.wikipedia.org/wiki/DJ_Khaled", + "https://en.wikipedia.org/wiki/Dr._Phillips_High_School", + "https://en.wikipedia.org/wiki/Mark_Ruiz" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "2", + "ground_truth_answer": "2" + } + }, + { + "query_id": "8", + "original_query": "A general motors vehicle is named after the largest ward in the country of Monaco. How many people had walked on the moon as of the first model year of the vehicle? Note: the model year is not the same as the year the model was first produced.", + "timestamp_start": "2026-05-16T02:38:52.270156Z", + "llm_calls": [ + { + "call_id": "140ebcfb-4c1d-4c1f-95d6-47ca53b27fd0", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:39:07.882659Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: A general motors vehicle is named after the largest ward in the country of Monaco. How many people had walked on the moon as of the first model year of the vehicle? Note: the model year is not the same as the year the model was first produced.\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Monaco wards largest\",\n \"Chevrolet Monte Carlo first model year\",\n \"moonwalkers 1970\"\n ],\n \"feedback\": \"Need to identify the largest ward in Monaco, determine the first model year of the Chevrolet Monte Carlo named after that ward, and find how many astronauts had walked on the Moon by that year.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 475, + "osl": 823, + "total_tokens": 1298, + "latency_ms": 15612.34, + "tokens_per_second": 52.71 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "8182496a-d334-4ddc-a0f1-5d36bd4c9ee9", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:39:13.069552Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: A general motors vehicle is named after the largest ward in the country of Monaco. How many people had walked on the moon as of the first model year of the vehicle? Note: the model year is not the same as the year the model was first produced.\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] ry EU VAT area Dublin Regulation Monaco ( relations ) Negotiating Yes De facto Partial Yes Yes No Twin cities Monaco's twin cities are: Ostend , Belgium since 1958 Macau, China since 1992 Lucciana , Corsica , France since 2009 Campagna , Campania , Italy since 2015 Security and military Palace guards in Monaco The wider defence of the nation is provided by France. Monaco has no navy or air force, but on both a per-capita and per-area basis, Monaco has one of the largest police forces (515 police officers for about 38,000 people) and police presences in the world. Its police includes a special unit which operates patrol and surveillance boats jointly with the military. Police forces in Monaco are commanded by a French officer. There is also a small military f\n\n[NEW 2] Two-door coupe manufactured by General Motors Motor vehicle Chevrolet Monte Carlo 2006 Chevrolet Monte Carlo LS Overview Manufacturer Chevrolet ( General Motors ) Production 1969–1987 1994–2007 Model years 1970–1988 1995–2007 Body and chassis Class Personal luxury car Body style 2-door coupé Layout FR layout (1970–1987) FF layout (1995–2007) The Chevrolet Monte Carlo is a two-door coupe that was manufactured and marketed by the Chevrolet division of General Motors . Deriving its name from the city in Monaco , the Monte Carlo was marketed as the first personal luxury car of the Chevrolet brand. Introduced for the 1970 model year, the model line was produced across six generations through the 2007 model year, with a hiatus from 1989 until 1994. The Monte Carlo\n\n[NEW 3] 1969 January February March April May June July August September October November December From top to bottom, left to right: Apollo 11 lands the first humans on the Moon as Neil Armstrong and Buzz Aldrin walk on its surface; Woodstock draws over 400,000 people and becomes a counterculture landmark; the Stonewall riots ignite the modern LGBT rights movement; the Sino-Soviet border conflict heightens tensions between China and the Soviet Union; the 1969 Libyan revolution led by Muammar Gaddafi overthrows King Idris I ; the Chappaquiddick incident involving Senator Edward Kennedy results in Mary Jo Kopechne 's death; the 1969 Curaçao uprising erupts over labor and racial issues; Hurricane Camille devastates the U. S. Gulf Coast; and Sesame Street premieres, re\n\n[NEW 4] s Report 1970 , pp. III‐17, III-33, III-40. ^ Cortright 1975 , pp. 254–257. ^ a b ^ a b c Cortright 1975 , pp. 262–263. ^ ^ ^ Cortright 1975 , pp. 257–263. ^ ^ a b c d e ^ ^ ^ Orloff & Harland 2006 , pp. 370–371. ^ ^ ^ ^ Apollo 13 Mission Report 1970 , p. 1-2. ^ Orloff & Harland 2006 , p. 371. ^ ^ Apollo 13 Mission Report 1970 , p. 10-5. ^ ^ ^ a b c NASA 1970 , p. 15. ^ Benson & Faherty 1979 , pp. 489–494. ^ Chaikin 1995 , p. 316. ^ ^ Accident report , pp. 1-1–1-4. ^ Accident report , p. 15. ^ Accident report , p. 4-36. ^ Orloff & Harland 2006 , pp. 372–373. ^ Accident report , pp. 5-6–5-7, 5-12–5-13. ^ Accident report , p. 4-37. ^ Accident report , p. 4-40. ^ Orloff & Harland 2006 , p. 372. ^ Accident report , p. 4-43. ^ a b Orloff & Harland 2006 , p. 375.\n\n[NEW 5] bruary 27, 1969] - Reconstruction\n\n[NEW 6] on to the Moon Indiana Jones and the Dial of Destiny (2023), fifth Indiana Jones film, in which Jürgen Voller, a NASA member and ex-Nazi involved with the Apollo program, wants to time travel . The New York City parade for the Apollo 11 crew is portrayed as a plot point. See also Apollo 11 in popular culture Apollo Lunar Surface Experiments Package Artemis Program Exploration of the Moon Leslie Cantwell collection List of artificial objects on the Moon List of crewed spacecraft List of missions to the Moon Soviet crewed lunar programs Stolen and missing Moon rocks Notes References Citations ^ a b ^ ^ Murray & Cox 1989 , p. 55. ^ ^ ^ Brooks, Grimwood & Swenson 1979 , Ch. 1.7: \"Feasility Studies\" . pp. 16–21. ^ ^ Beschloss 1997 ^ Sidey 1963 , pp. 117–118 ^ Bes\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 6 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 1, 1, 1, 0, 1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1377, + "osl": 515, + "total_tokens": 1892, + "latency_ms": 4622.86, + "tokens_per_second": 111.4 + }, + "context": { + "num_documents_evaluated": 6, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "6bb5baaa-7b34-43bb-bba6-f0ef2547c6f2", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:39:24.132244Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: A general motors vehicle is named after the largest ward in the country of Monaco. How many people had walked on the moon as of the first model year of the vehicle? Note: the model year is not the same as the year the model was first produced.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Two-door coupe manufactured by General Motors Motor vehicle Chevrolet Monte Carlo 2006 Chevrolet Monte Carlo LS Overview Manufacturer Chevrolet ( General Motors ) Production 1969–1987 1994–2007 Model years 1970–1988 1995–2007 Body and chassis Class Personal luxury car Body style 2-door coupé Layout FR layout (1970–1987) FF layout (1995–2007) The Chevrolet Monte Carlo is a two-door coupe that was manufactured and marketed by the Chevrolet division of General Motors . Deriving its name from the city in Monaco , the Monte Carlo was marketed as the first personal luxury car of the Chevrolet brand. Introduced for the 1970 model year, the model line was produced across six generations through the 2007 model year, with a hiatus from 1989 until 1994. The Monte Carlo\n\n[DOC 2] 1969 January February March April May June July August September October November December From top to bottom, left to right: Apollo 11 lands the first humans on the Moon as Neil Armstrong and Buzz Aldrin walk on its surface; Woodstock draws over 400,000 people and becomes a counterculture landmark; the Stonewall riots ignite the modern LGBT rights movement; the Sino-Soviet border conflict heightens tensions between China and the Soviet Union; the 1969 Libyan revolution led by Muammar Gaddafi overthrows King Idris I ; the Chappaquiddick incident involving Senator Edward Kennedy results in Mary Jo Kopechne 's death; the 1969 Curaçao uprising erupts over labor and racial issues; Hurricane Camille devastates the U. S. Gulf Coast; and Sesame Street premieres, re\n\n[DOC 3] s Report 1970 , pp. III‐17, III-33, III-40. ^ Cortright 1975 , pp. 254–257. ^ a b ^ a b c Cortright 1975 , pp. 262–263. ^ ^ ^ Cortright 1975 , pp. 257–263. ^ ^ a b c d e ^ ^ ^ Orloff & Harland 2006 , pp. 370–371. ^ ^ ^ ^ Apollo 13 Mission Report 1970 , p. 1-2. ^ Orloff & Harland 2006 , p. 371. ^ ^ Apollo 13 Mission Report 1970 , p. 10-5. ^ ^ ^ a b c NASA 1970 , p. 15. ^ Benson & Faherty 1979 , pp. 489–494. ^ Chaikin 1995 , p. 316. ^ ^ Accident report , pp. 1-1–1-4. ^ Accident report , p. 15. ^ Accident report , p. 4-36. ^ Orloff & Harland 2006 , pp. 372–373. ^ Accident report , pp. 5-6–5-7, 5-12–5-13. ^ Accident report , p. 4-37. ^ Accident report , p. 4-40. ^ Orloff & Harland 2006 , p. 372. ^ Accident report , p. 4-43. ^ a b Orloff & Harland 2006 , p. 375.\n\n[DOC 4] on to the Moon Indiana Jones and the Dial of Destiny (2023), fifth Indiana Jones film, in which Jürgen Voller, a NASA member and ex-Nazi involved with the Apollo program, wants to time travel . The New York City parade for the Apollo 11 crew is portrayed as a plot point. See also Apollo 11 in popular culture Apollo Lunar Surface Experiments Package Artemis Program Exploration of the Moon Leslie Cantwell collection List of artificial objects on the Moon List of crewed spacecraft List of missions to the Moon Soviet crewed lunar programs Stolen and missing Moon rocks Notes References Citations ^ a b ^ ^ Murray & Cox 1989 , p. 55. ^ ^ ^ Brooks, Grimwood & Swenson 1979 , Ch. 1.7: \"Feasility Studies\" . pp. 16–21. ^ ^ Beschloss 1997 ^ Sidey 1963 , pp. 117–118 ^ Bes\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents confirm the vehicle (Chevrolet Monte Carlo) and its first model year (1970). However, they do not provide the total number of people who had walked on the Moon by that year. Document 2 mentions only the two astronauts from Apollo 11, and none of the documents list the Apollo 12 moonwalkers or give a cumulative count up to 1970. Therefore, the required fact is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1213, + "osl": 570, + "total_tokens": 1783, + "latency_ms": 11061.98, + "tokens_per_second": 51.53 + }, + "context": { + "kept_docs_count": 4, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "3f514888-9707-41ea-8726-cb4c819453dc", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:39:29.122441Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: A general motors vehicle is named after the largest ward in the country of Monaco. How many people had walked on the moon as of the first model year of the vehicle? Note: the model year is not the same as the year the model was first produced.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Two-door coupe manufactured by General Motors Motor vehicle Chevrolet Monte Carlo 2006 Chevrolet Monte Carlo LS Overview Manufacturer Chevrolet ( General Motors ) Production 1969–1987 1994–2007 Model years 1970–1988 1995–2007 Body and chassis Class Personal luxury car Body style 2-door coupé Layout FR layout (1970–1987) FF layout (1995–2007) The Chevrolet Monte Carlo is a two-door coupe that was manufactured and marketed by the Chevrolet division of General Motors . Deriving its name from the city in Monaco , the Monte Carlo was marketed as the first personal luxury car of the Chevrolet brand. Introduced for the 1970 model year, the model line was produced across six generations through the 2007 model year, with a hiatus from 1989 until 1994. The Monte Carlo\n\n[DOC 2] 1969 January February March April May June July August September October November December From top to bottom, left to right: Apollo 11 lands the first humans on the Moon as Neil Armstrong and Buzz Aldrin walk on its surface; Woodstock draws over 400,000 people and becomes a counterculture landmark; the Stonewall riots ignite the modern LGBT rights movement; the Sino-Soviet border conflict heightens tensions between China and the Soviet Union; the 1969 Libyan revolution led by Muammar Gaddafi overthrows King Idris I ; the Chappaquiddick incident involving Senator Edward Kennedy results in Mary Jo Kopechne 's death; the 1969 Curaçao uprising erupts over labor and racial issues; Hurricane Camille devastates the U. S. Gulf Coast; and Sesame Street premieres, re\n\n[DOC 3] s Report 1970 , pp. III‐17, III-33, III-40. ^ Cortright 1975 , pp. 254–257. ^ a b ^ a b c Cortright 1975 , pp. 262–263. ^ ^ ^ Cortright 1975 , pp. 257–263. ^ ^ a b c d e ^ ^ ^ Orloff & Harland 2006 , pp. 370–371. ^ ^ ^ ^ Apollo 13 Mission Report 1970 , p. 1-2. ^ Orloff & Harland 2006 , p. 371. ^ ^ Apollo 13 Mission Report 1970 , p. 10-5. ^ ^ ^ a b c NASA 1970 , p. 15. ^ Benson & Faherty 1979 , pp. 489–494. ^ Chaikin 1995 , p. 316. ^ ^ Accident report , pp. 1-1–1-4. ^ Accident report , p. 15. ^ Accident report , p. 4-36. ^ Orloff & Harland 2006 , pp. 372–373. ^ Accident report , pp. 5-6–5-7, 5-12–5-13. ^ Accident report , p. 4-37. ^ Accident report , p. 4-40. ^ Orloff & Harland 2006 , p. 372. ^ Accident report , p. 4-43. ^ a b Orloff & Harland 2006 , p. 375.\n\n[DOC 4] on to the Moon Indiana Jones and the Dial of Destiny (2023), fifth Indiana Jones film, in which Jürgen Voller, a NASA member and ex-Nazi involved with the Apollo program, wants to time travel . The New York City parade for the Apollo 11 crew is portrayed as a plot point. See also Apollo 11 in popular culture Apollo Lunar Surface Experiments Package Artemis Program Exploration of the Moon Leslie Cantwell collection List of artificial objects on the Moon List of crewed spacecraft List of missions to the Moon Soviet crewed lunar programs Stolen and missing Moon rocks Notes References Citations ^ a b ^ ^ Murray & Cox 1989 , p. 55. ^ ^ ^ Brooks, Grimwood & Swenson 1979 , Ch. 1.7: \"Feasility Studies\" . pp. 16–21. ^ ^ Beschloss 1997 ^ Sidey 1963 , pp. 117–118 ^ Bes\n\n\nSEARCH HISTORY: - Query: 'Monaco wards largest' → 1 docs\n- Query: 'Chevrolet Monte Carlo first model year' → 1 docs\n- Query: 'moonwalkers 1970' → 4 docs\n\nFEEDBACK: Need to identify the largest ward in Monaco, determine the first model year of the Chevrolet Monte Carlo named after that ward, and find how many astronauts had walked on the Moon by that year.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"largest ward Monaco\",\n \"Chevrolet Monte Carlo first model year\",\n \"moonwalkers by 1970\"\n ],\n \"feedback\": \"We need to verify that Monte Carlo is the largest ward in Monaco, confirm the Monte Carlo's introductory model year, and determine the total number of astronauts who had walked on the Moon up to (and including) that model year.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1403, + "osl": 87, + "total_tokens": 1490, + "latency_ms": 4989.59, + "tokens_per_second": 17.44 + }, + "context": { + "kept_docs_count": 4, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "6289f85d-5ae8-4ef9-a78b-2ed32a1da3a6", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T02:39:35.635425Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: A general motors vehicle is named after the largest ward in the country of Monaco. How many people had walked on the moon as of the first model year of the vehicle? Note: the model year is not the same as the year the model was first produced.\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] tion Team 1969 , pp. 164–167. ^ Carmichael 2010 , pp. 184–185. ^ Carmichael 2010 , pp. 186–188. ^ Carmichael 2010 , pp. 199–200. ^ Johnston, Dietlein & Berry 1975 , pp. 406–424. ^ ^ ^ Mission Evaluation Team 1969 , pp. 166, 171–173. ^ Extra-Terrestrial Exposure, 34 Federal Register 11975 (July 16, 1969), codified at Federal Aviation Regulation pt. 1200 Archived May 22, 2021, at the Wayback Machine ^ ^ Carmichael 2010 , p. 118. ^ Ertel, Newkirk & Brooks 1978 , p. 312. ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c ^ a b c Chaikin 2007 , p. 57. ^ Schefter 1999 , p. 288. ^ ^ Schefter 1999 , p. 283. ^ ^ ^ Chaikin 2007 , p. 58. ^ ^ ^ McCurdy 1997 , pp. 106–107. ^ Chaikin 1994 , p. 631. ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ Earth magazine, March 2011, pp. 42–51 ^ Bat\n\n\nKEPT DOCUMENTS (context only):\n[4 documents already kept as relevant]\n[KEPT 1] Two-door coupe manufactured by General Motors Motor vehicle Chevrolet Monte Carlo 2006 Chevrolet Monte Carlo LS Overview Manufacturer Chevrolet ( General Motors ) Production 1969–1987 1994–2007 Model years 1970–1988 1995–2007 Body and chassis Class Personal luxury car Body style 2-door coupé Layout ...\n[KEPT 2] 1969 January February March April May June July August September October November December From top to bottom, left to right: Apollo 11 lands the first humans on the Moon as Neil Armstrong and Buzz Aldrin walk on its surface; Woodstock draws over 400,000 people and becomes a counterculture landmark;...\n[KEPT 3] s Report 1970 , pp. III‐17, III-33, III-40. ^ Cortright 1975 , pp. 254–257. ^ a b ^ a b c Cortright 1975 , pp. 262–263. ^ ^ ^ Cortright 1975 , pp. 257–263. ^ ^ a b c d e ^ ^ ^ Orloff & Harland 2006 , pp. 370–371. ^ ^ ^ ^ Apollo 13 Mission Report 1970 , p. 1-2. ^ Orloff & Harland 2006 , p. 371. ^ ^ A...\n[KEPT 4] on to the Moon Indiana Jones and the Dial of Destiny (2023), fifth Indiana Jones film, in which Jürgen Voller, a NASA member and ex-Nazi involved with the Apollo program, wants to time travel . The New York City parade for the Apollo 11 crew is portrayed as a plot point. See also Apollo 11 in popula...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 982, + "osl": 457, + "total_tokens": 1439, + "latency_ms": 5948.68, + "tokens_per_second": 76.82 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 4 + } + }, + { + "call_id": "0fc6581f-3167-4ce9-ac6d-1bb39bb45162", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T02:39:39.723344Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: A general motors vehicle is named after the largest ward in the country of Monaco. How many people had walked on the moon as of the first model year of the vehicle? Note: the model year is not the same as the year the model was first produced.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Two-door coupe manufactured by General Motors Motor vehicle Chevrolet Monte Carlo 2006 Chevrolet Monte Carlo LS Overview Manufacturer Chevrolet ( General Motors ) Production 1969–1987 1994–2007 Model years 1970–1988 1995–2007 Body and chassis Class Personal luxury car Body style 2-door coupé Layout FR layout (1970–1987) FF layout (1995–2007) The Chevrolet Monte Carlo is a two-door coupe that was manufactured and marketed by the Chevrolet division of General Motors . Deriving its name from the city in Monaco , the Monte Carlo was marketed as the first personal luxury car of the Chevrolet brand. Introduced for the 1970 model year, the model line was produced across six generations through the 2007 model year, with a hiatus from 1989 until 1994. The Monte Carlo\n\n[DOC 2] 1969 January February March April May June July August September October November December From top to bottom, left to right: Apollo 11 lands the first humans on the Moon as Neil Armstrong and Buzz Aldrin walk on its surface; Woodstock draws over 400,000 people and becomes a counterculture landmark; the Stonewall riots ignite the modern LGBT rights movement; the Sino-Soviet border conflict heightens tensions between China and the Soviet Union; the 1969 Libyan revolution led by Muammar Gaddafi overthrows King Idris I ; the Chappaquiddick incident involving Senator Edward Kennedy results in Mary Jo Kopechne 's death; the 1969 Curaçao uprising erupts over labor and racial issues; Hurricane Camille devastates the U. S. Gulf Coast; and Sesame Street premieres, re\n\n[DOC 3] s Report 1970 , pp. III‐17, III-33, III-40. ^ Cortright 1975 , pp. 254–257. ^ a b ^ a b c Cortright 1975 , pp. 262–263. ^ ^ ^ Cortright 1975 , pp. 257–263. ^ ^ a b c d e ^ ^ ^ Orloff & Harland 2006 , pp. 370–371. ^ ^ ^ ^ Apollo 13 Mission Report 1970 , p. 1-2. ^ Orloff & Harland 2006 , p. 371. ^ ^ Apollo 13 Mission Report 1970 , p. 10-5. ^ ^ ^ a b c NASA 1970 , p. 15. ^ Benson & Faherty 1979 , pp. 489–494. ^ Chaikin 1995 , p. 316. ^ ^ Accident report , pp. 1-1–1-4. ^ Accident report , p. 15. ^ Accident report , p. 4-36. ^ Orloff & Harland 2006 , pp. 372–373. ^ Accident report , pp. 5-6–5-7, 5-12–5-13. ^ Accident report , p. 4-37. ^ Accident report , p. 4-40. ^ Orloff & Harland 2006 , p. 372. ^ Accident report , p. 4-43. ^ a b Orloff & Harland 2006 , p. 375.\n\n[DOC 4] on to the Moon Indiana Jones and the Dial of Destiny (2023), fifth Indiana Jones film, in which Jürgen Voller, a NASA member and ex-Nazi involved with the Apollo program, wants to time travel . The New York City parade for the Apollo 11 crew is portrayed as a plot point. See also Apollo 11 in popular culture Apollo Lunar Surface Experiments Package Artemis Program Exploration of the Moon Leslie Cantwell collection List of artificial objects on the Moon List of crewed spacecraft List of missions to the Moon Soviet crewed lunar programs Stolen and missing Moon rocks Notes References Citations ^ a b ^ ^ Murray & Cox 1989 , p. 55. ^ ^ ^ Brooks, Grimwood & Swenson 1979 , Ch. 1.7: \"Feasility Studies\" . pp. 16–21. ^ ^ Beschloss 1997 ^ Sidey 1963 , pp. 117–118 ^ Bes\n\n[DOC 5] tion Team 1969 , pp. 164–167. ^ Carmichael 2010 , pp. 184–185. ^ Carmichael 2010 , pp. 186–188. ^ Carmichael 2010 , pp. 199–200. ^ Johnston, Dietlein & Berry 1975 , pp. 406–424. ^ ^ ^ Mission Evaluation Team 1969 , pp. 166, 171–173. ^ Extra-Terrestrial Exposure, 34 Federal Register 11975 (July 16, 1969), codified at Federal Aviation Regulation pt. 1200 Archived May 22, 2021, at the Wayback Machine ^ ^ Carmichael 2010 , p. 118. ^ Ertel, Newkirk & Brooks 1978 , p. 312. ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c ^ a b c Chaikin 2007 , p. 57. ^ Schefter 1999 , p. 288. ^ ^ Schefter 1999 , p. 283. ^ ^ ^ Chaikin 2007 , p. 58. ^ ^ ^ McCurdy 1997 , pp. 106–107. ^ Chaikin 1994 , p. 631. ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ Earth magazine, March 2011, pp. 42–51 ^ Bat\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Document 1 provides the vehicle name (Chevrolet Monte Carlo) and its first model year (1970). However, none of the kept documents give a complete count of how many people had walked on the Moon by 1970. Document 2 mentions the two astronauts from Apollo 11, but there is no information about the additional two astronauts from Apollo 12, which also occurred before 1970. Therefore, the required fact (total moonwalkers as of 1970) is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1542, + "osl": 610, + "total_tokens": 2152, + "latency_ms": 4087.05, + "tokens_per_second": 149.25 + }, + "context": { + "kept_docs_count": 5, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "9f129b8e-57a1-4e14-a744-835b69e3f3f5", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T02:39:54.255273Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: A general motors vehicle is named after the largest ward in the country of Monaco. How many people had walked on the moon as of the first model year of the vehicle? Note: the model year is not the same as the year the model was first produced.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Two-door coupe manufactured by General Motors Motor vehicle Chevrolet Monte Carlo 2006 Chevrolet Monte Carlo LS Overview Manufacturer Chevrolet ( General Motors ) Production 1969–1987 1994–2007 Model years 1970–1988 1995–2007 Body and chassis Class Personal luxury car Body style 2-door coupé Layout FR layout (1970–1987) FF layout (1995–2007) The Chevrolet Monte Carlo is a two-door coupe that was manufactured and marketed by the Chevrolet division of General Motors . Deriving its name from the city in Monaco , the Monte Carlo was marketed as the first personal luxury car of the Chevrolet brand. Introduced for the 1970 model year, the model line was produced across six generations through the 2007 model year, with a hiatus from 1989 until 1994. The Monte Carlo\n\n[DOC 2] 1969 January February March April May June July August September October November December From top to bottom, left to right: Apollo 11 lands the first humans on the Moon as Neil Armstrong and Buzz Aldrin walk on its surface; Woodstock draws over 400,000 people and becomes a counterculture landmark; the Stonewall riots ignite the modern LGBT rights movement; the Sino-Soviet border conflict heightens tensions between China and the Soviet Union; the 1969 Libyan revolution led by Muammar Gaddafi overthrows King Idris I ; the Chappaquiddick incident involving Senator Edward Kennedy results in Mary Jo Kopechne 's death; the 1969 Curaçao uprising erupts over labor and racial issues; Hurricane Camille devastates the U. S. Gulf Coast; and Sesame Street premieres, re\n\n[DOC 3] s Report 1970 , pp. III‐17, III-33, III-40. ^ Cortright 1975 , pp. 254–257. ^ a b ^ a b c Cortright 1975 , pp. 262–263. ^ ^ ^ Cortright 1975 , pp. 257–263. ^ ^ a b c d e ^ ^ ^ Orloff & Harland 2006 , pp. 370–371. ^ ^ ^ ^ Apollo 13 Mission Report 1970 , p. 1-2. ^ Orloff & Harland 2006 , p. 371. ^ ^ Apollo 13 Mission Report 1970 , p. 10-5. ^ ^ ^ a b c NASA 1970 , p. 15. ^ Benson & Faherty 1979 , pp. 489–494. ^ Chaikin 1995 , p. 316. ^ ^ Accident report , pp. 1-1–1-4. ^ Accident report , p. 15. ^ Accident report , p. 4-36. ^ Orloff & Harland 2006 , pp. 372–373. ^ Accident report , pp. 5-6–5-7, 5-12–5-13. ^ Accident report , p. 4-37. ^ Accident report , p. 4-40. ^ Orloff & Harland 2006 , p. 372. ^ Accident report , p. 4-43. ^ a b Orloff & Harland 2006 , p. 375.\n\n[DOC 4] on to the Moon Indiana Jones and the Dial of Destiny (2023), fifth Indiana Jones film, in which Jürgen Voller, a NASA member and ex-Nazi involved with the Apollo program, wants to time travel . The New York City parade for the Apollo 11 crew is portrayed as a plot point. See also Apollo 11 in popular culture Apollo Lunar Surface Experiments Package Artemis Program Exploration of the Moon Leslie Cantwell collection List of artificial objects on the Moon List of crewed spacecraft List of missions to the Moon Soviet crewed lunar programs Stolen and missing Moon rocks Notes References Citations ^ a b ^ ^ Murray & Cox 1989 , p. 55. ^ ^ ^ Brooks, Grimwood & Swenson 1979 , Ch. 1.7: \"Feasility Studies\" . pp. 16–21. ^ ^ Beschloss 1997 ^ Sidey 1963 , pp. 117–118 ^ Bes\n\n[DOC 5] tion Team 1969 , pp. 164–167. ^ Carmichael 2010 , pp. 184–185. ^ Carmichael 2010 , pp. 186–188. ^ Carmichael 2010 , pp. 199–200. ^ Johnston, Dietlein & Berry 1975 , pp. 406–424. ^ ^ ^ Mission Evaluation Team 1969 , pp. 166, 171–173. ^ Extra-Terrestrial Exposure, 34 Federal Register 11975 (July 16, 1969), codified at Federal Aviation Regulation pt. 1200 Archived May 22, 2021, at the Wayback Machine ^ ^ Carmichael 2010 , p. 118. ^ Ertel, Newkirk & Brooks 1978 , p. 312. ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c ^ a b c Chaikin 2007 , p. 57. ^ Schefter 1999 , p. 288. ^ ^ Schefter 1999 , p. 283. ^ ^ ^ Chaikin 2007 , p. 58. ^ ^ ^ McCurdy 1997 , pp. 106–107. ^ Chaikin 1994 , p. 631. ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ Earth magazine, March 2011, pp. 42–51 ^ Bat\n\n\nSEARCH HISTORY: - Query: 'Monaco wards largest' → 1 docs\n- Query: 'Chevrolet Monte Carlo first model year' → 1 docs\n- Query: 'moonwalkers 1970' → 4 docs\n- Query: 'largest ward Monaco' → 0 docs\n- Query: 'Chevrolet Monte Carlo first model year' → 0 docs\n- Query: 'moonwalkers by 1970' → 1 docs\n\nFEEDBACK: Need to identify the largest ward in Monaco, determine the first model year of the Chevrolet Monte Carlo named after that ward, and find how many astronauts had walked on the Moon by that year.\nWe need to verify that Monte Carlo is the largest ward in Monaco, confirm the Monte Carlo's introductory model year, and determine the total number of astronauts who had walked on the Moon up to (and including) that model year.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Monte Carlo largest ward Monaco\",\n \"Chevrolet Monte Carlo 1970 model year\",\n \"astronauts walked on Moon by 1970\"\n ],\n \"feedback\": \"Need confirmation that Monte Carlo is the largest ward in Monaco, the exact first model year of the Chevrolet Monte Carlo, and the total number of people who had walked on the Moon up to (and including) that model year.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1858, + "osl": 658, + "total_tokens": 2516, + "latency_ms": 14531.28, + "tokens_per_second": 45.28 + }, + "context": { + "kept_docs_count": 5, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "a59826dd-2b8e-4ab8-afae-e23f19f10ab2", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T02:39:58.829679Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: A general motors vehicle is named after the largest ward in the country of Monaco. How many people had walked on the moon as of the first model year of the vehicle? Note: the model year is not the same as the year the model was first produced.\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] l EVA time 2h 31m Missions Gemini 8 Apollo 11 Mission insignia Retirement August 1971 Signature Neil Alden Armstrong (August 5, 1930 – August 25, 2012) was an American astronaut and aeronautical engineer who, as the commander of the 1969 Apollo 11 mission, became the first person to walk on the Moon . He was also a naval aviator , test pilot and university professor. Armstrong was born and raised near Wapakoneta, Ohio . He entered Purdue University , studying aeronautical engineering , with the United States Navy paying his tuition under the Holloway Plan . He became a midshipman in 1949 and a naval aviator the following year. He saw action in the Korean War , flying the Grumman F9F Panther from the aircraft carrier USS Essex . After the war, he completed hi\n\n\nKEPT DOCUMENTS (context only):\n[5 documents already kept as relevant]\n[KEPT 1] Two-door coupe manufactured by General Motors Motor vehicle Chevrolet Monte Carlo 2006 Chevrolet Monte Carlo LS Overview Manufacturer Chevrolet ( General Motors ) Production 1969–1987 1994–2007 Model years 1970–1988 1995–2007 Body and chassis Class Personal luxury car Body style 2-door coupé Layout ...\n[KEPT 2] 1969 January February March April May June July August September October November December From top to bottom, left to right: Apollo 11 lands the first humans on the Moon as Neil Armstrong and Buzz Aldrin walk on its surface; Woodstock draws over 400,000 people and becomes a counterculture landmark;...\n[KEPT 3] s Report 1970 , pp. III‐17, III-33, III-40. ^ Cortright 1975 , pp. 254–257. ^ a b ^ a b c Cortright 1975 , pp. 262–263. ^ ^ ^ Cortright 1975 , pp. 257–263. ^ ^ a b c d e ^ ^ ^ Orloff & Harland 2006 , pp. 370–371. ^ ^ ^ ^ Apollo 13 Mission Report 1970 , p. 1-2. ^ Orloff & Harland 2006 , p. 371. ^ ^ A...\n[KEPT 4] on to the Moon Indiana Jones and the Dial of Destiny (2023), fifth Indiana Jones film, in which Jürgen Voller, a NASA member and ex-Nazi involved with the Apollo program, wants to time travel . The New York City parade for the Apollo 11 crew is portrayed as a plot point. See also Apollo 11 in popula...\n[KEPT 5] tion Team 1969 , pp. 164–167. ^ Carmichael 2010 , pp. 184–185. ^ Carmichael 2010 , pp. 186–188. ^ Carmichael 2010 , pp. 199–200. ^ Johnston, Dietlein & Berry 1975 , pp. 406–424. ^ ^ ^ Mission Evaluation Team 1969 , pp. 166, 171–173. ^ Extra-Terrestrial Exposure, 34 Federal Register 11975 (July 16, 1...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 968, + "osl": 412, + "total_tokens": 1380, + "latency_ms": 3999.54, + "tokens_per_second": 103.01 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 5 + } + }, + { + "call_id": "6a0c7ab5-51a1-4618-a2f6-de0617412ecd", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T02:40:02.166835Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: A general motors vehicle is named after the largest ward in the country of Monaco. How many people had walked on the moon as of the first model year of the vehicle? Note: the model year is not the same as the year the model was first produced.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Two-door coupe manufactured by General Motors Motor vehicle Chevrolet Monte Carlo 2006 Chevrolet Monte Carlo LS Overview Manufacturer Chevrolet ( General Motors ) Production 1969–1987 1994–2007 Model years 1970–1988 1995–2007 Body and chassis Class Personal luxury car Body style 2-door coupé Layout FR layout (1970–1987) FF layout (1995–2007) The Chevrolet Monte Carlo is a two-door coupe that was manufactured and marketed by the Chevrolet division of General Motors . Deriving its name from the city in Monaco , the Monte Carlo was marketed as the first personal luxury car of the Chevrolet brand. Introduced for the 1970 model year, the model line was produced across six generations through the 2007 model year, with a hiatus from 1989 until 1994. The Monte Carlo\n\n[DOC 2] 1969 January February March April May June July August September October November December From top to bottom, left to right: Apollo 11 lands the first humans on the Moon as Neil Armstrong and Buzz Aldrin walk on its surface; Woodstock draws over 400,000 people and becomes a counterculture landmark; the Stonewall riots ignite the modern LGBT rights movement; the Sino-Soviet border conflict heightens tensions between China and the Soviet Union; the 1969 Libyan revolution led by Muammar Gaddafi overthrows King Idris I ; the Chappaquiddick incident involving Senator Edward Kennedy results in Mary Jo Kopechne 's death; the 1969 Curaçao uprising erupts over labor and racial issues; Hurricane Camille devastates the U. S. Gulf Coast; and Sesame Street premieres, re\n\n[DOC 3] s Report 1970 , pp. III‐17, III-33, III-40. ^ Cortright 1975 , pp. 254–257. ^ a b ^ a b c Cortright 1975 , pp. 262–263. ^ ^ ^ Cortright 1975 , pp. 257–263. ^ ^ a b c d e ^ ^ ^ Orloff & Harland 2006 , pp. 370–371. ^ ^ ^ ^ Apollo 13 Mission Report 1970 , p. 1-2. ^ Orloff & Harland 2006 , p. 371. ^ ^ Apollo 13 Mission Report 1970 , p. 10-5. ^ ^ ^ a b c NASA 1970 , p. 15. ^ Benson & Faherty 1979 , pp. 489–494. ^ Chaikin 1995 , p. 316. ^ ^ Accident report , pp. 1-1–1-4. ^ Accident report , p. 15. ^ Accident report , p. 4-36. ^ Orloff & Harland 2006 , pp. 372–373. ^ Accident report , pp. 5-6–5-7, 5-12–5-13. ^ Accident report , p. 4-37. ^ Accident report , p. 4-40. ^ Orloff & Harland 2006 , p. 372. ^ Accident report , p. 4-43. ^ a b Orloff & Harland 2006 , p. 375.\n\n[DOC 4] on to the Moon Indiana Jones and the Dial of Destiny (2023), fifth Indiana Jones film, in which Jürgen Voller, a NASA member and ex-Nazi involved with the Apollo program, wants to time travel . The New York City parade for the Apollo 11 crew is portrayed as a plot point. See also Apollo 11 in popular culture Apollo Lunar Surface Experiments Package Artemis Program Exploration of the Moon Leslie Cantwell collection List of artificial objects on the Moon List of crewed spacecraft List of missions to the Moon Soviet crewed lunar programs Stolen and missing Moon rocks Notes References Citations ^ a b ^ ^ Murray & Cox 1989 , p. 55. ^ ^ ^ Brooks, Grimwood & Swenson 1979 , Ch. 1.7: \"Feasility Studies\" . pp. 16–21. ^ ^ Beschloss 1997 ^ Sidey 1963 , pp. 117–118 ^ Bes\n\n[DOC 5] tion Team 1969 , pp. 164–167. ^ Carmichael 2010 , pp. 184–185. ^ Carmichael 2010 , pp. 186–188. ^ Carmichael 2010 , pp. 199–200. ^ Johnston, Dietlein & Berry 1975 , pp. 406–424. ^ ^ ^ Mission Evaluation Team 1969 , pp. 166, 171–173. ^ Extra-Terrestrial Exposure, 34 Federal Register 11975 (July 16, 1969), codified at Federal Aviation Regulation pt. 1200 Archived May 22, 2021, at the Wayback Machine ^ ^ Carmichael 2010 , p. 118. ^ Ertel, Newkirk & Brooks 1978 , p. 312. ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c ^ a b c Chaikin 2007 , p. 57. ^ Schefter 1999 , p. 288. ^ ^ Schefter 1999 , p. 283. ^ ^ ^ Chaikin 2007 , p. 58. ^ ^ ^ McCurdy 1997 , pp. 106–107. ^ Chaikin 1994 , p. 631. ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ Earth magazine, March 2011, pp. 42–51 ^ Bat\n\n[DOC 6] l EVA time 2h 31m Missions Gemini 8 Apollo 11 Mission insignia Retirement August 1971 Signature Neil Alden Armstrong (August 5, 1930 – August 25, 2012) was an American astronaut and aeronautical engineer who, as the commander of the 1969 Apollo 11 mission, became the first person to walk on the Moon . He was also a naval aviator , test pilot and university professor. Armstrong was born and raised near Wapakoneta, Ohio . He entered Purdue University , studying aeronautical engineering , with the United States Navy paying his tuition under the Holloway Plan . He became a midshipman in 1949 and a naval aviator the following year. He saw action in the Korean War , flying the Grumman F9F Panther from the aircraft carrier USS Essex . After the war, he completed hi\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide the vehicle name (Chevrolet Monte Carlo) and its first model year (1970) from DOC 1, and they mention two astronauts walking on the Moon during Apollo 11 from DOC 2 and DOC 6. However, they do not provide information about the Apollo 12 mission or any additional astronauts who walked on the Moon before 1970. Consequently, the total number of people who had walked on the Moon as of 1970 cannot be determined from the available documents.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1743, + "osl": 710, + "total_tokens": 2453, + "latency_ms": 3336.34, + "tokens_per_second": 212.81 + }, + "context": { + "kept_docs_count": 6, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "62f72b58-9729-469a-9aea-7e83ebf2c1a8", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T02:40:13.162341Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: A general motors vehicle is named after the largest ward in the country of Monaco. How many people had walked on the moon as of the first model year of the vehicle? Note: the model year is not the same as the year the model was first produced.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Two-door coupe manufactured by General Motors Motor vehicle Chevrolet Monte Carlo 2006 Chevrolet Monte Carlo LS Overview Manufacturer Chevrolet ( General Motors ) Production 1969–1987 1994–2007 Model years 1970–1988 1995–2007 Body and chassis Class Personal luxury car Body style 2-door coupé Layout FR layout (1970–1987) FF layout (1995–2007) The Chevrolet Monte Carlo is a two-door coupe that was manufactured and marketed by the Chevrolet division of General Motors . Deriving its name from the city in Monaco , the Monte Carlo was marketed as the first personal luxury car of the Chevrolet brand. Introduced for the 1970 model year, the model line was produced across six generations through the 2007 model year, with a hiatus from 1989 until 1994. The Monte Carlo\n\n[DOC 2] 1969 January February March April May June July August September October November December From top to bottom, left to right: Apollo 11 lands the first humans on the Moon as Neil Armstrong and Buzz Aldrin walk on its surface; Woodstock draws over 400,000 people and becomes a counterculture landmark; the Stonewall riots ignite the modern LGBT rights movement; the Sino-Soviet border conflict heightens tensions between China and the Soviet Union; the 1969 Libyan revolution led by Muammar Gaddafi overthrows King Idris I ; the Chappaquiddick incident involving Senator Edward Kennedy results in Mary Jo Kopechne 's death; the 1969 Curaçao uprising erupts over labor and racial issues; Hurricane Camille devastates the U. S. Gulf Coast; and Sesame Street premieres, re\n\n[DOC 3] s Report 1970 , pp. III‐17, III-33, III-40. ^ Cortright 1975 , pp. 254–257. ^ a b ^ a b c Cortright 1975 , pp. 262–263. ^ ^ ^ Cortright 1975 , pp. 257–263. ^ ^ a b c d e ^ ^ ^ Orloff & Harland 2006 , pp. 370–371. ^ ^ ^ ^ Apollo 13 Mission Report 1970 , p. 1-2. ^ Orloff & Harland 2006 , p. 371. ^ ^ Apollo 13 Mission Report 1970 , p. 10-5. ^ ^ ^ a b c NASA 1970 , p. 15. ^ Benson & Faherty 1979 , pp. 489–494. ^ Chaikin 1995 , p. 316. ^ ^ Accident report , pp. 1-1–1-4. ^ Accident report , p. 15. ^ Accident report , p. 4-36. ^ Orloff & Harland 2006 , pp. 372–373. ^ Accident report , pp. 5-6–5-7, 5-12–5-13. ^ Accident report , p. 4-37. ^ Accident report , p. 4-40. ^ Orloff & Harland 2006 , p. 372. ^ Accident report , p. 4-43. ^ a b Orloff & Harland 2006 , p. 375.\n\n[DOC 4] on to the Moon Indiana Jones and the Dial of Destiny (2023), fifth Indiana Jones film, in which Jürgen Voller, a NASA member and ex-Nazi involved with the Apollo program, wants to time travel . The New York City parade for the Apollo 11 crew is portrayed as a plot point. See also Apollo 11 in popular culture Apollo Lunar Surface Experiments Package Artemis Program Exploration of the Moon Leslie Cantwell collection List of artificial objects on the Moon List of crewed spacecraft List of missions to the Moon Soviet crewed lunar programs Stolen and missing Moon rocks Notes References Citations ^ a b ^ ^ Murray & Cox 1989 , p. 55. ^ ^ ^ Brooks, Grimwood & Swenson 1979 , Ch. 1.7: \"Feasility Studies\" . pp. 16–21. ^ ^ Beschloss 1997 ^ Sidey 1963 , pp. 117–118 ^ Bes\n\n[DOC 5] tion Team 1969 , pp. 164–167. ^ Carmichael 2010 , pp. 184–185. ^ Carmichael 2010 , pp. 186–188. ^ Carmichael 2010 , pp. 199–200. ^ Johnston, Dietlein & Berry 1975 , pp. 406–424. ^ ^ ^ Mission Evaluation Team 1969 , pp. 166, 171–173. ^ Extra-Terrestrial Exposure, 34 Federal Register 11975 (July 16, 1969), codified at Federal Aviation Regulation pt. 1200 Archived May 22, 2021, at the Wayback Machine ^ ^ Carmichael 2010 , p. 118. ^ Ertel, Newkirk & Brooks 1978 , p. 312. ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c ^ a b c Chaikin 2007 , p. 57. ^ Schefter 1999 , p. 288. ^ ^ Schefter 1999 , p. 283. ^ ^ ^ Chaikin 2007 , p. 58. ^ ^ ^ McCurdy 1997 , pp. 106–107. ^ Chaikin 1994 , p. 631. ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ Earth magazine, March 2011, pp. 42–51 ^ Bat\n\n[DOC 6] l EVA time 2h 31m Missions Gemini 8 Apollo 11 Mission insignia Retirement August 1971 Signature Neil Alden Armstrong (August 5, 1930 – August 25, 2012) was an American astronaut and aeronautical engineer who, as the commander of the 1969 Apollo 11 mission, became the first person to walk on the Moon . He was also a naval aviator , test pilot and university professor. Armstrong was born and raised near Wapakoneta, Ohio . He entered Purdue University , studying aeronautical engineering , with the United States Navy paying his tuition under the Holloway Plan . He became a midshipman in 1949 and a naval aviator the following year. He saw action in the Korean War , flying the Grumman F9F Panther from the aircraft carrier USS Essex . After the war, he completed hi\n\n\nSEARCH HISTORY: - Query: 'Monaco wards largest' → 1 docs\n- Query: 'Chevrolet Monte Carlo first model year' → 1 docs\n- Query: 'moonwalkers 1970' → 4 docs\n- Query: 'largest ward Monaco' → 0 docs\n- Query: 'Chevrolet Monte Carlo first model year' → 0 docs\n- Query: 'moonwalkers by 1970' → 1 docs\n- Query: 'Monte Carlo largest ward Monaco' → 0 docs\n- Query: 'Chevrolet Monte Carlo 1970 model year' → 0 docs\n- Query: 'astronauts walked on Moon by 1970' → 1 docs\n\nFEEDBACK: Need to identify the largest ward in Monaco, determine the first model year of the Chevrolet Monte Carlo named after that ward, and find how many astronauts had walked on the Moon by that year.\nWe need to verify that Monte Carlo is the largest ward in Monaco, confirm the Monte Carlo's introductory model year, and determine the total number of astronauts who had walked on the Moon up to (and including) that model year.\nNeed confirmation that Monte Carlo is the largest ward in Monaco, the exact first model year of the Chevrolet Monte Carlo, and the total number of people who had walked on the Moon up to (and including) that model year.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Monte Carlo largest ward\",\n \"Chevrolet Monte Carlo 1970\",\n \"Moon walkers 1970\"\n ],\n \"feedback\": \"Need to confirm Monte Carlo is Monaco's largest ward, verify the Chevrolet Monte Carlo's first model year (1970), and determine how many astronauts had walked on the Moon by 1970.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2145, + "osl": 590, + "total_tokens": 2735, + "latency_ms": 10994.86, + "tokens_per_second": 53.66 + }, + "context": { + "kept_docs_count": 6, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "3e8bb1d1-1bfc-419c-b735-a5bf0615b189", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T02:40:16.229662Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: A general motors vehicle is named after the largest ward in the country of Monaco. How many people had walked on the moon as of the first model year of the vehicle? Note: the model year is not the same as the year the model was first produced.\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] rater Jim Lovell Ken Mattingly Fred Haise Apollo 14 January 31, 1971 Fra Mauro highlands Alan Shepard Stuart Roosa Edgar Mitchell Apollo 15 H November 1970 Littrow crater David Scott Alfred Worden James Irwin Apollo 15 July 26, 1971 Hadley Rille David Scott Alfred Worden James Irwin Apollo 16 J April 1971 Tycho crater John Young Jack Swigert Charles Duke Apollo 16 April 16, 1972 Descartes Highlands John Young Ken Mattingly Charles Duke Apollo 17 J September 1971 Marius Hills Gene Cernan Ronald Evans Joe Engle Apollo 17 December 7, 1972 Taurus-Littrow Gene Cernan Ronald Evans Harrison Schmitt Apollo 18 J February 1972 Schroter's Valley Richard F. Gordon Jr. Vance Brand Harrison Schmitt CANCELED September 1970 Apollo 19 J July 1972 Hyginus Rille Fred Haise Wil\n\n\nKEPT DOCUMENTS (context only):\n[6 documents already kept as relevant]\n[KEPT 1] Two-door coupe manufactured by General Motors Motor vehicle Chevrolet Monte Carlo 2006 Chevrolet Monte Carlo LS Overview Manufacturer Chevrolet ( General Motors ) Production 1969–1987 1994–2007 Model years 1970–1988 1995–2007 Body and chassis Class Personal luxury car Body style 2-door coupé Layout ...\n[KEPT 2] 1969 January February March April May June July August September October November December From top to bottom, left to right: Apollo 11 lands the first humans on the Moon as Neil Armstrong and Buzz Aldrin walk on its surface; Woodstock draws over 400,000 people and becomes a counterculture landmark;...\n[KEPT 3] s Report 1970 , pp. III‐17, III-33, III-40. ^ Cortright 1975 , pp. 254–257. ^ a b ^ a b c Cortright 1975 , pp. 262–263. ^ ^ ^ Cortright 1975 , pp. 257–263. ^ ^ a b c d e ^ ^ ^ Orloff & Harland 2006 , pp. 370–371. ^ ^ ^ ^ Apollo 13 Mission Report 1970 , p. 1-2. ^ Orloff & Harland 2006 , p. 371. ^ ^ A...\n[KEPT 4] on to the Moon Indiana Jones and the Dial of Destiny (2023), fifth Indiana Jones film, in which Jürgen Voller, a NASA member and ex-Nazi involved with the Apollo program, wants to time travel . The New York City parade for the Apollo 11 crew is portrayed as a plot point. See also Apollo 11 in popula...\n[KEPT 5] tion Team 1969 , pp. 164–167. ^ Carmichael 2010 , pp. 184–185. ^ Carmichael 2010 , pp. 186–188. ^ Carmichael 2010 , pp. 199–200. ^ Johnston, Dietlein & Berry 1975 , pp. 406–424. ^ ^ ^ Mission Evaluation Team 1969 , pp. 166, 171–173. ^ Extra-Terrestrial Exposure, 34 Federal Register 11975 (July 16, 1...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 991, + "osl": 270, + "total_tokens": 1261, + "latency_ms": 2433.18, + "tokens_per_second": 110.97 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 6 + } + }, + { + "call_id": "7773389a-e6e4-46ed-a185-deb45075fb80", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T02:40:23.563984Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: A general motors vehicle is named after the largest ward in the country of Monaco. How many people had walked on the moon as of the first model year of the vehicle? Note: the model year is not the same as the year the model was first produced.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Two-door coupe manufactured by General Motors Motor vehicle Chevrolet Monte Carlo 2006 Chevrolet Monte Carlo LS Overview Manufacturer Chevrolet ( General Motors ) Production 1969–1987 1994–2007 Model years 1970–1988 1995–2007 Body and chassis Class Personal luxury car Body style 2-door coupé Layout FR layout (1970–1987) FF layout (1995–2007) The Chevrolet Monte Carlo is a two-door coupe that was manufactured and marketed by the Chevrolet division of General Motors . Deriving its name from the city in Monaco , the Monte Carlo was marketed as the first personal luxury car of the Chevrolet brand. Introduced for the 1970 model year, the model line was produced across six generations through the 2007 model year, with a hiatus from 1989 until 1994. The Monte Carlo\n\n[DOC 2] 1969 January February March April May June July August September October November December From top to bottom, left to right: Apollo 11 lands the first humans on the Moon as Neil Armstrong and Buzz Aldrin walk on its surface; Woodstock draws over 400,000 people and becomes a counterculture landmark; the Stonewall riots ignite the modern LGBT rights movement; the Sino-Soviet border conflict heightens tensions between China and the Soviet Union; the 1969 Libyan revolution led by Muammar Gaddafi overthrows King Idris I ; the Chappaquiddick incident involving Senator Edward Kennedy results in Mary Jo Kopechne 's death; the 1969 Curaçao uprising erupts over labor and racial issues; Hurricane Camille devastates the U. S. Gulf Coast; and Sesame Street premieres, re\n\n[DOC 3] s Report 1970 , pp. III‐17, III-33, III-40. ^ Cortright 1975 , pp. 254–257. ^ a b ^ a b c Cortright 1975 , pp. 262–263. ^ ^ ^ Cortright 1975 , pp. 257–263. ^ ^ a b c d e ^ ^ ^ Orloff & Harland 2006 , pp. 370–371. ^ ^ ^ ^ Apollo 13 Mission Report 1970 , p. 1-2. ^ Orloff & Harland 2006 , p. 371. ^ ^ Apollo 13 Mission Report 1970 , p. 10-5. ^ ^ ^ a b c NASA 1970 , p. 15. ^ Benson & Faherty 1979 , pp. 489–494. ^ Chaikin 1995 , p. 316. ^ ^ Accident report , pp. 1-1–1-4. ^ Accident report , p. 15. ^ Accident report , p. 4-36. ^ Orloff & Harland 2006 , pp. 372–373. ^ Accident report , pp. 5-6–5-7, 5-12–5-13. ^ Accident report , p. 4-37. ^ Accident report , p. 4-40. ^ Orloff & Harland 2006 , p. 372. ^ Accident report , p. 4-43. ^ a b Orloff & Harland 2006 , p. 375.\n\n[DOC 4] on to the Moon Indiana Jones and the Dial of Destiny (2023), fifth Indiana Jones film, in which Jürgen Voller, a NASA member and ex-Nazi involved with the Apollo program, wants to time travel . The New York City parade for the Apollo 11 crew is portrayed as a plot point. See also Apollo 11 in popular culture Apollo Lunar Surface Experiments Package Artemis Program Exploration of the Moon Leslie Cantwell collection List of artificial objects on the Moon List of crewed spacecraft List of missions to the Moon Soviet crewed lunar programs Stolen and missing Moon rocks Notes References Citations ^ a b ^ ^ Murray & Cox 1989 , p. 55. ^ ^ ^ Brooks, Grimwood & Swenson 1979 , Ch. 1.7: \"Feasility Studies\" . pp. 16–21. ^ ^ Beschloss 1997 ^ Sidey 1963 , pp. 117–118 ^ Bes\n\n[DOC 5] tion Team 1969 , pp. 164–167. ^ Carmichael 2010 , pp. 184–185. ^ Carmichael 2010 , pp. 186–188. ^ Carmichael 2010 , pp. 199–200. ^ Johnston, Dietlein & Berry 1975 , pp. 406–424. ^ ^ ^ Mission Evaluation Team 1969 , pp. 166, 171–173. ^ Extra-Terrestrial Exposure, 34 Federal Register 11975 (July 16, 1969), codified at Federal Aviation Regulation pt. 1200 Archived May 22, 2021, at the Wayback Machine ^ ^ Carmichael 2010 , p. 118. ^ Ertel, Newkirk & Brooks 1978 , p. 312. ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c ^ a b c Chaikin 2007 , p. 57. ^ Schefter 1999 , p. 288. ^ ^ Schefter 1999 , p. 283. ^ ^ ^ Chaikin 2007 , p. 58. ^ ^ ^ McCurdy 1997 , pp. 106–107. ^ Chaikin 1994 , p. 631. ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ Earth magazine, March 2011, pp. 42–51 ^ Bat\n\n[DOC 6] l EVA time 2h 31m Missions Gemini 8 Apollo 11 Mission insignia Retirement August 1971 Signature Neil Alden Armstrong (August 5, 1930 – August 25, 2012) was an American astronaut and aeronautical engineer who, as the commander of the 1969 Apollo 11 mission, became the first person to walk on the Moon . He was also a naval aviator , test pilot and university professor. Armstrong was born and raised near Wapakoneta, Ohio . He entered Purdue University , studying aeronautical engineering , with the United States Navy paying his tuition under the Holloway Plan . He became a midshipman in 1949 and a naval aviator the following year. He saw action in the Korean War , flying the Grumman F9F Panther from the aircraft carrier USS Essex . After the war, he completed hi\n\n[DOC 7] rater Jim Lovell Ken Mattingly Fred Haise Apollo 14 January 31, 1971 Fra Mauro highlands Alan Shepard Stuart Roosa Edgar Mitchell Apollo 15 H November 1970 Littrow crater David Scott Alfred Worden James Irwin Apollo 15 July 26, 1971 Hadley Rille David Scott Alfred Worden James Irwin Apollo 16 J April 1971 Tycho crater John Young Jack Swigert Charles Duke Apollo 16 April 16, 1972 Descartes Highlands John Young Ken Mattingly Charles Duke Apollo 17 J September 1971 Marius Hills Gene Cernan Ronald Evans Joe Engle Apollo 17 December 7, 1972 Taurus-Littrow Gene Cernan Ronald Evans Harrison Schmitt Apollo 18 J February 1972 Schroter's Valley Richard F. Gordon Jr. Vance Brand Harrison Schmitt CANCELED September 1970 Apollo 19 J July 1972 Hyginus Rille Fred Haise Wil\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents identify the vehicle (Chevrolet Monte Carlo) and its first model year (1970), but they do not provide a definitive count of how many people had walked on the Moon by that year. While individual astronaut names and some Apollo missions are mentioned, there is no explicit statement of the total number of lunar walkers up to 1970, which is required to answer the question.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1896, + "osl": 93, + "total_tokens": 1989, + "latency_ms": 7333.49, + "tokens_per_second": 12.68 + }, + "context": { + "kept_docs_count": 7, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "e42e5c41-c176-4429-b805-a08158f0436d", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T02:40:30.122608Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: A general motors vehicle is named after the largest ward in the country of Monaco. How many people had walked on the moon as of the first model year of the vehicle? Note: the model year is not the same as the year the model was first produced.\n\nDOCUMENTS:\n\n[DOC 1] Two-door coupe manufactured by General Motors Motor vehicle Chevrolet Monte Carlo 2006 Chevrolet Monte Carlo LS Overview Manufacturer Chevrolet ( General Motors ) Production 1969–1987 1994–2007 Model years 1970–1988 1995–2007 Body and chassis Class Personal luxury car Body style 2-door coupé Layout FR layout (1970–1987) FF layout (1995–2007) The Chevrolet Monte Carlo is a two-door coupe that was manufactured and marketed by the Chevrolet division of General Motors . Deriving its name from the city in Monaco , the Monte Carlo was marketed as the first personal luxury car of the Chevrolet brand. Introduced for the 1970 model year, the model line was produced across six generations through the 2007 model year, with a hiatus from 1989 until 1994. The Monte Carlo\n\n[DOC 2] 1969 January February March April May June July August September October November December From top to bottom, left to right: Apollo 11 lands the first humans on the Moon as Neil Armstrong and Buzz Aldrin walk on its surface; Woodstock draws over 400,000 people and becomes a counterculture landmark; the Stonewall riots ignite the modern LGBT rights movement; the Sino-Soviet border conflict heightens tensions between China and the Soviet Union; the 1969 Libyan revolution led by Muammar Gaddafi overthrows King Idris I ; the Chappaquiddick incident involving Senator Edward Kennedy results in Mary Jo Kopechne 's death; the 1969 Curaçao uprising erupts over labor and racial issues; Hurricane Camille devastates the U. S. Gulf Coast; and Sesame Street premieres, re\n\n[DOC 3] s Report 1970 , pp. III‐17, III-33, III-40. ^ Cortright 1975 , pp. 254–257. ^ a b ^ a b c Cortright 1975 , pp. 262–263. ^ ^ ^ Cortright 1975 , pp. 257–263. ^ ^ a b c d e ^ ^ ^ Orloff & Harland 2006 , pp. 370–371. ^ ^ ^ ^ Apollo 13 Mission Report 1970 , p. 1-2. ^ Orloff & Harland 2006 , p. 371. ^ ^ Apollo 13 Mission Report 1970 , p. 10-5. ^ ^ ^ a b c NASA 1970 , p. 15. ^ Benson & Faherty 1979 , pp. 489–494. ^ Chaikin 1995 , p. 316. ^ ^ Accident report , pp. 1-1–1-4. ^ Accident report , p. 15. ^ Accident report , p. 4-36. ^ Orloff & Harland 2006 , pp. 372–373. ^ Accident report , pp. 5-6–5-7, 5-12–5-13. ^ Accident report , p. 4-37. ^ Accident report , p. 4-40. ^ Orloff & Harland 2006 , p. 372. ^ Accident report , p. 4-43. ^ a b Orloff & Harland 2006 , p. 375.\n\n[DOC 4] on to the Moon Indiana Jones and the Dial of Destiny (2023), fifth Indiana Jones film, in which Jürgen Voller, a NASA member and ex-Nazi involved with the Apollo program, wants to time travel . The New York City parade for the Apollo 11 crew is portrayed as a plot point. See also Apollo 11 in popular culture Apollo Lunar Surface Experiments Package Artemis Program Exploration of the Moon Leslie Cantwell collection List of artificial objects on the Moon List of crewed spacecraft List of missions to the Moon Soviet crewed lunar programs Stolen and missing Moon rocks Notes References Citations ^ a b ^ ^ Murray & Cox 1989 , p. 55. ^ ^ ^ Brooks, Grimwood & Swenson 1979 , Ch. 1.7: \"Feasility Studies\" . pp. 16–21. ^ ^ Beschloss 1997 ^ Sidey 1963 , pp. 117–118 ^ Bes\n\n[DOC 5] tion Team 1969 , pp. 164–167. ^ Carmichael 2010 , pp. 184–185. ^ Carmichael 2010 , pp. 186–188. ^ Carmichael 2010 , pp. 199–200. ^ Johnston, Dietlein & Berry 1975 , pp. 406–424. ^ ^ ^ Mission Evaluation Team 1969 , pp. 166, 171–173. ^ Extra-Terrestrial Exposure, 34 Federal Register 11975 (July 16, 1969), codified at Federal Aviation Regulation pt. 1200 Archived May 22, 2021, at the Wayback Machine ^ ^ Carmichael 2010 , p. 118. ^ Ertel, Newkirk & Brooks 1978 , p. 312. ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c ^ a b c Chaikin 2007 , p. 57. ^ Schefter 1999 , p. 288. ^ ^ Schefter 1999 , p. 283. ^ ^ ^ Chaikin 2007 , p. 58. ^ ^ ^ McCurdy 1997 , pp. 106–107. ^ Chaikin 1994 , p. 631. ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ Earth magazine, March 2011, pp. 42–51 ^ Bat\n\n[DOC 6] l EVA time 2h 31m Missions Gemini 8 Apollo 11 Mission insignia Retirement August 1971 Signature Neil Alden Armstrong (August 5, 1930 – August 25, 2012) was an American astronaut and aeronautical engineer who, as the commander of the 1969 Apollo 11 mission, became the first person to walk on the Moon . He was also a naval aviator , test pilot and university professor. Armstrong was born and raised near Wapakoneta, Ohio . He entered Purdue University , studying aeronautical engineering , with the United States Navy paying his tuition under the Holloway Plan . He became a midshipman in 1949 and a naval aviator the following year. He saw action in the Korean War , flying the Grumman F9F Panther from the aircraft carrier USS Essex . After the war, he completed hi\n\n[DOC 7] rater Jim Lovell Ken Mattingly Fred Haise Apollo 14 January 31, 1971 Fra Mauro highlands Alan Shepard Stuart Roosa Edgar Mitchell Apollo 15 H November 1970 Littrow crater David Scott Alfred Worden James Irwin Apollo 15 July 26, 1971 Hadley Rille David Scott Alfred Worden James Irwin Apollo 16 J April 1971 Tycho crater John Young Jack Swigert Charles Duke Apollo 16 April 16, 1972 Descartes Highlands John Young Ken Mattingly Charles Duke Apollo 17 J September 1971 Marius Hills Gene Cernan Ronald Evans Joe Engle Apollo 17 December 7, 1972 Taurus-Littrow Gene Cernan Ronald Evans Harrison Schmitt Apollo 18 J February 1972 Schroter's Valley Richard F. Gordon Jr. Vance Brand Harrison Schmitt CANCELED September 1970 Apollo 19 J July 1972 Hyginus Rille Fred Haise Wil\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1751, + "osl": 1, + "total_tokens": 1752, + "latency_ms": 6558.06, + "tokens_per_second": 0.15 + }, + "context": { + "num_documents_used": 7, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:40:30.123402Z", + "summary": { + "total_llm_calls": 13, + "total_hops": 5, + "total_input_tokens": 18344, + "total_output_tokens": 5796, + "total_tokens": 24140, + "total_latency_ms": 95509.25, + "average_tokens_per_second": 76.75, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Monaco", + "https://en.wikipedia.org/wiki/List_of_Chevrolet_vehicles", + "https://en.wikipedia.org/wiki/Chevrolet_Monte_Carlo", + "https://en.wikipedia.org/wiki/Moon", + "https://en.wikipedia.org/wiki/Apollo_program", + "https://en.wikipedia.org/wiki/List_of_Apollo_missions" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "4" + } + }, + { + "query_id": "9", + "original_query": "The Pope born Pietro Barbo ended a long-running war two years after his papacy began, which famous conflict, immortalized in tapestry took place 400 years earlier?", + "timestamp_start": "2026-05-16T02:40:30.132592Z", + "llm_calls": [ + { + "call_id": "ff4cc678-1820-4386-9296-1285283947c4", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:40:40.720047Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: The Pope born Pietro Barbo ended a long-running war two years after his papacy began, which famous conflict, immortalized in tapestry took place 400 years earlier?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Pope Paul II war ended 1466\",\n \"Bayeux Tapestry conflict depicted\",\n \"War of Ferrara 1465\"\n ],\n \"feedback\": \"Need to identify the war Pope Paul II (born Pietro Barbo) concluded two years into his papacy, then link it to a famous tapestry that records a conflict 400 years earlier (the Bayeux Tapestry depicting the Norman Conquest). The three queries target (1) the specific war and its end date, (2) the tapestry’s subject, and (3) a possible candidate war (War of Ferrara) to confirm the match.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 471, + "osl": 712, + "total_tokens": 1183, + "latency_ms": 10587.3, + "tokens_per_second": 67.25 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "1e7995c3-0374-4dce-b3c3-0cf3194dfa02", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:40:43.924065Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: The Pope born Pietro Barbo ended a long-running war two years after his papacy began, which famous conflict, immortalized in tapestry took place 400 years earlier?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] oops (1527). Forbade the divorce of King Henry VIII of England . Crowned Emperor Charles V at Bologna in 1530. Commissioned Michelangelo 's painting of The Last Judgment in the Sistine Chapel (1533). Approved Copernicus 's heliocentric model of the universe (1533). The niece of the pope was married to the future Henry II of France (1533). Recognized the Order of Friars Minor Capuchin (Capuchins). 220 13 October 1534 – 10 November 1549 (15 years, 28 days) Paul III PAVLVS Tertius Alessandro Farnese 29 February 1468 Canino , Papal States 66 / 81 Subject and later the sovereign of the Papal States . Recognized the Jesuits (1540). Opened the Council of Trent (1545). His illegitimate son became the first duke of Parma . Decreed the second and final excommunication\n\n[NEW 2] Head of the Catholic Church from 1464 to 1471 Pope Paul II Bishop of Rome Contemporary bust of Paul II by Mino da Fiesole , now in the Palazzo Venezia Church Catholic Church Papacy began 30 August 1464 Papacy ended 26 July 1471 Predecessor Pius II Successor Sixtus IV Previous posts Cardinal-Deacon of Santa Maria Nuova (1440–1451) Apostolic Administrator of Cervia (1440–1451) Archpriest of the Papal Basilica of Saint Peter (1445–?) Camerlengo of the Sacred College of Cardinals (1445–1446; 1460–1461) Bishop of Vicenza (1451–1464) Cardinal-Priest of San Marco (1451–1464) Bishop of Padova (1459–1460) Abbot Ordinary of Montecassino (1465–1471) Orders Created cardinal 1 July 1440 by Eugene IV Personal details Born Pietro Barbo 23 February 1417 Venice , Republic of\n\n[NEW 3] Embroidery depicting the 1066 Norman invasion of England A scene from the Bayeux Tapestry depicting Bishop Odo rallying Duke William 's army during the Battle of Hastings in 1066 The Bayeux Tapestry is an embroidered cloth nearly 70 metres (230 feet) long and 50 centimetres (20 inches) tall that depicts the events leading up to the Norman Conquest of England in 1066, led by William, Duke of Normandy , challenging Harold II, King of England , and culminating in the Battle of Hastings . It is thought to date to the 11th century, within a few years of the battle. Now widely accepted to have been made in England, perhaps as a gift for William, it tells the story from the point of view of the conquering Normans and for centuries has been preserved in Normandy. Ac\n\n[NEW 4] 1458–1459 Sir John Tempest 1459–1460 Sir Thomas Metham 1460–1461 Sir John Melton House of York 1461–1461 Sir John Savile 1461–1463 Sir Robert Constable 1463–1464 Sir John Constable 1464–1465 Sir Edward Hastings 1465–1466 Sir Richard FitzWilliam 1466–1467 Sir James Haryngton 1467–1468 Sir John Conyers 1468–1469 Sir James Strangways 1469–1470 Sir Henry Vavasour 1470–1471 Sir Edmund Hastings 1471–1473 Sir Ralph de Ashton 1473–1474 Sir Walter Griffith 1474–1475 Sir John Conyers 1475–1476 Sir James Haryngton 1476–1477 Sir Edmund Hastings 1477–1478 Sir William Ryther 1478–1479 Sir Robert Constable 1479–1480 Sir Hugh Hastings 1480–1481 Sir Marmaduke Constable 1481–1482 Sir Ralph Bygod 1482–1483 Sir William Eure 1483–1484 Sir Edmund Hastings 1484–1485 Sir Thomas Ma\n\n[NEW 5] Conflict between the Prussian Confederation, Poland, and the Teutonic Order Thirteen Years’ War Part of Polish–Teutonic Wars The Polish melee infantry (right), crossbowmen (left) and some foot/dismounted knights (middle). Date 4 February 1454 – 19 October 1466 Location Pomerelia , Prussia , Baltic Sea Result Polish—Prussian victory, Second Peace of Thorn Territorial changes Teutonic Order becomes vassal of Poland; returns Pomerelia to Poland, cedes the bishopric of Warmia , both of these lands become Royal Prussia under direct rule of the Polish King Belligerents Polish Crown Prussian Confederation Teutonic Order Denmark Livonian Order County of Holland [ clarification needed ] Duchy of Żagań Commanders and leaders Piotr Dunin Jan Bażyński Jan Taszka Koniecp\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 5 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 1, 1, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1330, + "osl": 593, + "total_tokens": 1923, + "latency_ms": 2649.13, + "tokens_per_second": 223.85 + }, + "context": { + "num_documents_evaluated": 5, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "6d8c40a8-0c3b-4a7a-9993-e988468f23f6", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:40:53.804505Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: The Pope born Pietro Barbo ended a long-running war two years after his papacy began, which famous conflict, immortalized in tapestry took place 400 years earlier?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Head of the Catholic Church from 1464 to 1471 Pope Paul II Bishop of Rome Contemporary bust of Paul II by Mino da Fiesole , now in the Palazzo Venezia Church Catholic Church Papacy began 30 August 1464 Papacy ended 26 July 1471 Predecessor Pius II Successor Sixtus IV Previous posts Cardinal-Deacon of Santa Maria Nuova (1440–1451) Apostolic Administrator of Cervia (1440–1451) Archpriest of the Papal Basilica of Saint Peter (1445–?) Camerlengo of the Sacred College of Cardinals (1445–1446; 1460–1461) Bishop of Vicenza (1451–1464) Cardinal-Priest of San Marco (1451–1464) Bishop of Padova (1459–1460) Abbot Ordinary of Montecassino (1465–1471) Orders Created cardinal 1 July 1440 by Eugene IV Personal details Born Pietro Barbo 23 February 1417 Venice , Republic of\n\n[DOC 2] Embroidery depicting the 1066 Norman invasion of England A scene from the Bayeux Tapestry depicting Bishop Odo rallying Duke William 's army during the Battle of Hastings in 1066 The Bayeux Tapestry is an embroidered cloth nearly 70 metres (230 feet) long and 50 centimetres (20 inches) tall that depicts the events leading up to the Norman Conquest of England in 1066, led by William, Duke of Normandy , challenging Harold II, King of England , and culminating in the Battle of Hastings . It is thought to date to the 11th century, within a few years of the battle. Now widely accepted to have been made in England, perhaps as a gift for William, it tells the story from the point of view of the conquering Normans and for centuries has been preserved in Normandy. Ac\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Missing: a document describing that Pope Paul II (born Pietro Barbo) ended a long‑running war two years after his papacy began (i.e., around 1466). While DOC 1 gives his birth name and papacy dates, and DOC 2 describes the Bayeux Tapestry and the 1066 Norman Conquest, there is no source linking Paul II to the termination of a war, which is required to connect the 400‑year gap and answer the question.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 710, + "osl": 531, + "total_tokens": 1241, + "latency_ms": 9879.73, + "tokens_per_second": 53.75 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "79f265d5-49f4-42f2-9230-1033a1f74d03", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:41:26.936692Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: The Pope born Pietro Barbo ended a long-running war two years after his papacy began, which famous conflict, immortalized in tapestry took place 400 years earlier?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Head of the Catholic Church from 1464 to 1471 Pope Paul II Bishop of Rome Contemporary bust of Paul II by Mino da Fiesole , now in the Palazzo Venezia Church Catholic Church Papacy began 30 August 1464 Papacy ended 26 July 1471 Predecessor Pius II Successor Sixtus IV Previous posts Cardinal-Deacon of Santa Maria Nuova (1440–1451) Apostolic Administrator of Cervia (1440–1451) Archpriest of the Papal Basilica of Saint Peter (1445–?) Camerlengo of the Sacred College of Cardinals (1445–1446; 1460–1461) Bishop of Vicenza (1451–1464) Cardinal-Priest of San Marco (1451–1464) Bishop of Padova (1459–1460) Abbot Ordinary of Montecassino (1465–1471) Orders Created cardinal 1 July 1440 by Eugene IV Personal details Born Pietro Barbo 23 February 1417 Venice , Republic of\n\n[DOC 2] Embroidery depicting the 1066 Norman invasion of England A scene from the Bayeux Tapestry depicting Bishop Odo rallying Duke William 's army during the Battle of Hastings in 1066 The Bayeux Tapestry is an embroidered cloth nearly 70 metres (230 feet) long and 50 centimetres (20 inches) tall that depicts the events leading up to the Norman Conquest of England in 1066, led by William, Duke of Normandy , challenging Harold II, King of England , and culminating in the Battle of Hastings . It is thought to date to the 11th century, within a few years of the battle. Now widely accepted to have been made in England, perhaps as a gift for William, it tells the story from the point of view of the conquering Normans and for centuries has been preserved in Normandy. Ac\n\n\nSEARCH HISTORY: - Query: 'Pope Paul II war ended 1466' → 2 docs\n- Query: 'Bayeux Tapestry conflict depicted' → 1 docs\n- Query: 'War of Ferrara 1465' → 2 docs\n\nFEEDBACK: Need to identify the war Pope Paul II (born Pietro Barbo) concluded two years into his papacy, then link it to a famous tapestry that records a conflict 400 years earlier (the Bayeux Tapestry depicting the Norman Conquest). The three queries target (1) the specific war and its end date, (2) the tapestry’s subject, and (3) a possible candidate war (War of Ferrara) to confirm the match.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Pope Paul II war 1466\",\n \"1466 peace treaty Pope\",\n \"Bayeux Tapestry conflict\"\n ],\n \"feedback\": \"Need to identify the war that ended in 1466 under Pope Paul II and confirm the conflict depicted in the Bayeux Tapestry (the Norman Conquest of 1066).\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 990, + "osl": 1292, + "total_tokens": 2282, + "latency_ms": 33131.63, + "tokens_per_second": 39.0 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "ef110ebd-7589-4b50-87af-49aadb65df69", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T02:41:36.600470Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: The Pope born Pietro Barbo ended a long-running war two years after his papacy began, which famous conflict, immortalized in tapestry took place 400 years earlier?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Head of the Catholic Church from 1464 to 1471 Pope Paul II Bishop of Rome Contemporary bust of Paul II by Mino da Fiesole , now in the Palazzo Venezia Church Catholic Church Papacy began 30 August 1464 Papacy ended 26 July 1471 Predecessor Pius II Successor Sixtus IV Previous posts Cardinal-Deacon of Santa Maria Nuova (1440–1451) Apostolic Administrator of Cervia (1440–1451) Archpriest of the Papal Basilica of Saint Peter (1445–?) Camerlengo of the Sacred College of Cardinals (1445–1446; 1460–1461) Bishop of Vicenza (1451–1464) Cardinal-Priest of San Marco (1451–1464) Bishop of Padova (1459–1460) Abbot Ordinary of Montecassino (1465–1471) Orders Created cardinal 1 July 1440 by Eugene IV Personal details Born Pietro Barbo 23 February 1417 Venice , Republic of\n\n[DOC 2] Embroidery depicting the 1066 Norman invasion of England A scene from the Bayeux Tapestry depicting Bishop Odo rallying Duke William 's army during the Battle of Hastings in 1066 The Bayeux Tapestry is an embroidered cloth nearly 70 metres (230 feet) long and 50 centimetres (20 inches) tall that depicts the events leading up to the Norman Conquest of England in 1066, led by William, Duke of Normandy , challenging Harold II, King of England , and culminating in the Battle of Hastings . It is thought to date to the 11th century, within a few years of the battle. Now widely accepted to have been made in England, perhaps as a gift for William, it tells the story from the point of view of the conquering Normans and for centuries has been preserved in Normandy. Ac\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": true,\n \"reasoning\": \"Doc 1 provides that Pope Paul II (born Pietro Barbo) began his papacy on 30 August 1464, so two years later is 1466. Doc 2 describes the Bayeux Tapestry, which records the Norman Conquest of England in 1066. The year 1066 is approximately 400 years before 1466, matching the required offset. Thus the documents contain the needed dates and the tapestry‑depicted conflict.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 710, + "osl": 632, + "total_tokens": 1342, + "latency_ms": 9136.06, + "tokens_per_second": 69.18 + }, + "context": { + "kept_docs_count": 2, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "224c9549-a907-4e99-877d-faf201b26c90", + "component": "answer_generator", + "hop_count": 3, + "timestamp": "2026-05-16T02:41:39.883812Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: The Pope born Pietro Barbo ended a long-running war two years after his papacy began, which famous conflict, immortalized in tapestry took place 400 years earlier?\n\nDOCUMENTS:\n\n[DOC 1] Head of the Catholic Church from 1464 to 1471 Pope Paul II Bishop of Rome Contemporary bust of Paul II by Mino da Fiesole , now in the Palazzo Venezia Church Catholic Church Papacy began 30 August 1464 Papacy ended 26 July 1471 Predecessor Pius II Successor Sixtus IV Previous posts Cardinal-Deacon of Santa Maria Nuova (1440–1451) Apostolic Administrator of Cervia (1440–1451) Archpriest of the Papal Basilica of Saint Peter (1445–?) Camerlengo of the Sacred College of Cardinals (1445–1446; 1460–1461) Bishop of Vicenza (1451–1464) Cardinal-Priest of San Marco (1451–1464) Bishop of Padova (1459–1460) Abbot Ordinary of Montecassino (1465–1471) Orders Created cardinal 1 July 1440 by Eugene IV Personal details Born Pietro Barbo 23 February 1417 Venice , Republic of\n\n[DOC 2] Embroidery depicting the 1066 Norman invasion of England A scene from the Bayeux Tapestry depicting Bishop Odo rallying Duke William 's army during the Battle of Hastings in 1066 The Bayeux Tapestry is an embroidered cloth nearly 70 metres (230 feet) long and 50 centimetres (20 inches) tall that depicts the events leading up to the Norman Conquest of England in 1066, led by William, Duke of Normandy , challenging Harold II, King of England , and culminating in the Battle of Hastings . It is thought to date to the 11th century, within a few years of the battle. Now widely accepted to have been made in England, perhaps as a gift for William, it tells the story from the point of view of the conquering Normans and for centuries has been preserved in Normandy. Ac\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "The conflict is the **Norman Conquest of England (the 1066 Norman invasion/Battle of Hastings)**, as depicted in the Bayeux Tapestry.", + "finish_reason": "stop" + }, + "metrics": { + "isl": 560, + "osl": 369, + "total_tokens": 929, + "latency_ms": 3282.45, + "tokens_per_second": 112.42 + }, + "context": { + "num_documents_used": 2, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:41:39.884556Z", + "summary": { + "total_llm_calls": 6, + "total_hops": 3, + "total_input_tokens": 4771, + "total_output_tokens": 4129, + "total_tokens": 8900, + "total_latency_ms": 68666.3, + "average_tokens_per_second": 94.24, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Pope_Paul_II", + "https://en.wikipedia.org/wiki/Thirteen_Years%27_War_(1454%E2%80%931466)", + "https://en.wikipedia.org/wiki/Bayeux_Tapestry", + "https://en.wikipedia.org/wiki/Battle_of_Hastings" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "The conflict is the **Norman Conquest of England (the 1066 Norman invasion/Battle of Hastings)**, as depicted in the Bayeux Tapestry.", + "ground_truth_answer": "The Battle of Hastings." + } + }, + { + "query_id": "10", + "original_query": "An Australian artist, born the same year as artist Janet Cumbrae Stewart and fellow member of the Melbourne Society of Women Painters and Sculptors, had her painting featured on the cover of Women's World Magazine in 1923. What is the name of the painting?", + "timestamp_start": "2026-05-16T02:41:39.893602Z", + "llm_calls": [ + { + "call_id": "e134d3a7-8f49-4f6c-bf84-bcf7d4f3e414", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:41:40.920067Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: An Australian artist, born the same year as artist Janet Cumbrae Stewart and fellow member of the Melbourne Society of Women Painters and Sculptors, had her painting featured on the cover of Women's World Magazine in 1923. What is the name of the painting?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Janet Cumbrae Stewart birth year\",\n \"Melbourne Society of Women Painters and Sculptors members 1923\",\n \"Women's World Magazine 1923 cover painting\"\n ],\n \"feedback\": \"First query will give the birth year needed to identify the fellow artist. Second query lists society members around that time to find the matching artist. Third query should reveal which painting appeared on the 1923 magazine cover, giving the answer.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 493, + "osl": 373, + "total_tokens": 866, + "latency_ms": 1026.35, + "tokens_per_second": 363.42 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "5e1b2a77-826f-4546-83bd-cdab628874fb", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:41:45.994251Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: An Australian artist, born the same year as artist Janet Cumbrae Stewart and fellow member of the Melbourne Society of Women Painters and Sculptors, had her painting featured on the cover of Women's World Magazine in 1923. What is the name of the painting?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Australian painter Janet Cumbrae Stewart Self-portrait, 1911 Born Janet Agnes Stewart ( 1883-12-23 ) 23 December 1883 Melbourne , Victoria , Australia Died 8 September 1960 (1960-09-08) (aged 76) Melbourne Known for Painting Partner Miss Argemore ffarington \"Bill\" Bellairs Janet Agnes Cumbrae Stewart (23 December 1883 – 8 September 1960) was an Australian painter. She spent the 1920s and 1930s painting in Britain, France and Italy. Biography Cumbrae Stewart was born on 23 December 1883 in Brighton, Victoria , Australia. She was born Janet Agnes Stewart , the youngest of ten children born to Francis Edward Stewart (1833–1904) and Agnes Park (1843–1927). Janet's eldest brother, Francis William Sutton Stewart, became convinced of a family connection to the Stua\n\n[NEW 2] Australian visual art organisation Melbourne Society of Women Painters and Sculptors Formation 1902 Headquarters Melbourne, Australia Website mswps . com . au The Melbourne Society of Women Painters and Sculptors , established in Melbourne , Victoria in 1902, is the oldest surviving women's art group in Australia . History The Melbourne Society of Women Painters and Sculptors (MSWPS) began in 1902 as a monthly gathering of eight former students of Frederick McCubbin from the National Gallery School which members called the Students' Art Club . It is known that among these founders were Daisy Stone, Tina Gowdie, Annie Gates, Kate Allan, Ella Thorn, Henrietta Maria Gulliver and a Miss Stock (otherwise unidentified, who died in 1906). In 1905 they added the ind\n\n[NEW 3] eau: revue internationale d'esthétique, 1920 . Gallica, Bibliothèque nationale de France\n\n[NEW 4] s – Robert Andrews Millikan Chemistry – Fritz Pregl Physiology or Medicine – Frederick Banting , John Macleod Literature – W. B. Yeats References Wikimedia Commons has media related to 1923 . ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ Named and commissioned October 10. ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^\n\n[NEW 5] ars\" Archived 25 August 2016 at the Wayback Machine in This Working-Day World: Women's Lives and Culture(s) in Britain, 1914–1945, ed. Sybil Oldfield. London: Taylor & Francis. p. 150. ^ a b Double Doors, Auppegard, France. Archived 17 January 2021 at the Wayback Machine Your Paintings: Ethel Sands. BBC. Retrieved 18 January 2014. ^ a b A Spare Room, Château d'Auppegard. Archived 17 January 2021 at the Wayback Machine Your Paintings: Ethel Sands. BBC. Retrieved 18 January 2014. ^ a b Nan Hudson Playing Patience at Auppegard, France. Archived 9 January 2021 at the Wayback Machine Your Paintings: Ethel Sands. BBC. Retrieved 18 January 2014. ^ a b Auppegard Church from the Château, France. Archived 20 July 2021 at the Wayback Machine Your Paintings: Ethel Sands\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 5 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 1, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1001, + "osl": 572, + "total_tokens": 1573, + "latency_ms": 4548.21, + "tokens_per_second": 125.76 + }, + "context": { + "num_documents_evaluated": 5, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "b4ee3cf2-5055-4c2e-8cfa-996cbd55992e", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:41:50.426456Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: An Australian artist, born the same year as artist Janet Cumbrae Stewart and fellow member of the Melbourne Society of Women Painters and Sculptors, had her painting featured on the cover of Women's World Magazine in 1923. What is the name of the painting?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Australian painter Janet Cumbrae Stewart Self-portrait, 1911 Born Janet Agnes Stewart ( 1883-12-23 ) 23 December 1883 Melbourne , Victoria , Australia Died 8 September 1960 (1960-09-08) (aged 76) Melbourne Known for Painting Partner Miss Argemore ffarington \"Bill\" Bellairs Janet Agnes Cumbrae Stewart (23 December 1883 – 8 September 1960) was an Australian painter. She spent the 1920s and 1930s painting in Britain, France and Italy. Biography Cumbrae Stewart was born on 23 December 1883 in Brighton, Victoria , Australia. She was born Janet Agnes Stewart , the youngest of ten children born to Francis Edward Stewart (1833–1904) and Agnes Park (1843–1927). Janet's eldest brother, Francis William Sutton Stewart, became convinced of a family connection to the Stua\n\n[DOC 2] Australian visual art organisation Melbourne Society of Women Painters and Sculptors Formation 1902 Headquarters Melbourne, Australia Website mswps . com . au The Melbourne Society of Women Painters and Sculptors , established in Melbourne , Victoria in 1902, is the oldest surviving women's art group in Australia . History The Melbourne Society of Women Painters and Sculptors (MSWPS) began in 1902 as a monthly gathering of eight former students of Frederick McCubbin from the National Gallery School which members called the Students' Art Club . It is known that among these founders were Daisy Stone, Tina Gowdie, Annie Gates, Kate Allan, Ella Thorn, Henrietta Maria Gulliver and a Miss Stock (otherwise unidentified, who died in 1906). In 1905 they added the ind\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The provided documents only give Janet Cumbrae Stewart's birth year (1883) and general information about the Melbourne Society of Women Painters and Sculptors. They do not identify any other artist born in 1883 who was a member of the society, nor do they mention a painting featured on the cover of Women's World Magazine in 1923. Therefore, the necessary facts to determine the artist and the painting name are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 644, + "osl": 104, + "total_tokens": 748, + "latency_ms": 4431.39, + "tokens_per_second": 23.47 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "224c9216-76ac-446f-8656-09202d54e44c", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:41:59.125726Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: An Australian artist, born the same year as artist Janet Cumbrae Stewart and fellow member of the Melbourne Society of Women Painters and Sculptors, had her painting featured on the cover of Women's World Magazine in 1923. What is the name of the painting?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Australian painter Janet Cumbrae Stewart Self-portrait, 1911 Born Janet Agnes Stewart ( 1883-12-23 ) 23 December 1883 Melbourne , Victoria , Australia Died 8 September 1960 (1960-09-08) (aged 76) Melbourne Known for Painting Partner Miss Argemore ffarington \"Bill\" Bellairs Janet Agnes Cumbrae Stewart (23 December 1883 – 8 September 1960) was an Australian painter. She spent the 1920s and 1930s painting in Britain, France and Italy. Biography Cumbrae Stewart was born on 23 December 1883 in Brighton, Victoria , Australia. She was born Janet Agnes Stewart , the youngest of ten children born to Francis Edward Stewart (1833–1904) and Agnes Park (1843–1927). Janet's eldest brother, Francis William Sutton Stewart, became convinced of a family connection to the Stua\n\n[DOC 2] Australian visual art organisation Melbourne Society of Women Painters and Sculptors Formation 1902 Headquarters Melbourne, Australia Website mswps . com . au The Melbourne Society of Women Painters and Sculptors , established in Melbourne , Victoria in 1902, is the oldest surviving women's art group in Australia . History The Melbourne Society of Women Painters and Sculptors (MSWPS) began in 1902 as a monthly gathering of eight former students of Frederick McCubbin from the National Gallery School which members called the Students' Art Club . It is known that among these founders were Daisy Stone, Tina Gowdie, Annie Gates, Kate Allan, Ella Thorn, Henrietta Maria Gulliver and a Miss Stock (otherwise unidentified, who died in 1906). In 1905 they added the ind\n\n\nSEARCH HISTORY: - Query: 'Janet Cumbrae Stewart birth year' → 1 docs\n- Query: 'Melbourne Society of Women Painters and Sculptors members 1923' → 1 docs\n- Query: 'Women's World Magazine 1923 cover painting' → 3 docs\n\nFEEDBACK: First query will give the birth year needed to identify the fellow artist. Second query lists society members around that time to find the matching artist. Third query should reveal which painting appeared on the 1923 magazine cover, giving the answer.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Melbourne Society of Women Painters and Sculptors members born 1883\",\n \"Women's World Magazine 1923 cover painting\",\n \"Australian woman painter 1883 magazine cover\"\n ],\n \"feedback\": \"We need to identify a MSWPS member born in 1883 (same year as Janet Cumbrae Stewart) whose artwork appeared on the 1923 Women's World Magazine cover, then retrieve the painting's title.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 936, + "osl": 425, + "total_tokens": 1361, + "latency_ms": 8698.65, + "tokens_per_second": 48.86 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "ba2cf8b9-714f-4a76-b4d5-d4335262a27e", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T02:42:00.674822Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: An Australian artist, born the same year as artist Janet Cumbrae Stewart and fellow member of the Melbourne Society of Women Painters and Sculptors, had her painting featured on the cover of Women's World Magazine in 1923. What is the name of the painting?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] , Woman Bathing , c. 1890–1891 See also Canada portal Art portal Kathleen Fenwick , first gallery curator, from 1929 to 1968 List of art museums List of largest art museums List of museums in Ontario List of national galleries National museums of Canada Notes ^ Martin was named the \"acting director\" of the institution since 1981, although he was not conferred as the institution's formal director until August 1983. References ^ a b ^ ^ ^ a b ^ a b ^ a b National Gallery of Canada at Structurae ^ ^ The Canadian Encyclopedia Archived 2009-02-13 at the Wayback Machine ^ a b c National Gallery of Canada – 1980 Archived 2010-09-19 at the Wayback Machine ^ a b c ^ a b c d e f g h Ord 2003 , p. 12. ^ a b ^ ^ a b c Ord 2003 , p. 13. ^ [ permanent dead link ] ^ a b c\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] Australian painter Janet Cumbrae Stewart Self-portrait, 1911 Born Janet Agnes Stewart ( 1883-12-23 ) 23 December 1883 Melbourne , Victoria , Australia Died 8 September 1960 (1960-09-08) (aged 76) Melbourne Known for Painting Partner Miss Argemore ffarington \"Bill\" Bellairs Janet Agnes Cumbrae Stewar...\n[KEPT 2] Australian visual art organisation Melbourne Society of Women Painters and Sculptors Formation 1902 Headquarters Melbourne, Australia Website mswps . com . au The Melbourne Society of Women Painters and Sculptors , established in Melbourne , Victoria in 1902, is the oldest surviving women's art grou...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 663, + "osl": 273, + "total_tokens": 936, + "latency_ms": 1011.55, + "tokens_per_second": 269.88 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "aeaf74ee-8dee-421a-a8d4-115b39983428", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T02:42:04.732039Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: An Australian artist, born the same year as artist Janet Cumbrae Stewart and fellow member of the Melbourne Society of Women Painters and Sculptors, had her painting featured on the cover of Women's World Magazine in 1923. What is the name of the painting?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Australian painter Janet Cumbrae Stewart Self-portrait, 1911 Born Janet Agnes Stewart ( 1883-12-23 ) 23 December 1883 Melbourne , Victoria , Australia Died 8 September 1960 (1960-09-08) (aged 76) Melbourne Known for Painting Partner Miss Argemore ffarington \"Bill\" Bellairs Janet Agnes Cumbrae Stewart (23 December 1883 – 8 September 1960) was an Australian painter. She spent the 1920s and 1930s painting in Britain, France and Italy. Biography Cumbrae Stewart was born on 23 December 1883 in Brighton, Victoria , Australia. She was born Janet Agnes Stewart , the youngest of ten children born to Francis Edward Stewart (1833–1904) and Agnes Park (1843–1927). Janet's eldest brother, Francis William Sutton Stewart, became convinced of a family connection to the Stua\n\n[DOC 2] Australian visual art organisation Melbourne Society of Women Painters and Sculptors Formation 1902 Headquarters Melbourne, Australia Website mswps . com . au The Melbourne Society of Women Painters and Sculptors , established in Melbourne , Victoria in 1902, is the oldest surviving women's art group in Australia . History The Melbourne Society of Women Painters and Sculptors (MSWPS) began in 1902 as a monthly gathering of eight former students of Frederick McCubbin from the National Gallery School which members called the Students' Art Club . It is known that among these founders were Daisy Stone, Tina Gowdie, Annie Gates, Kate Allan, Ella Thorn, Henrietta Maria Gulliver and a Miss Stock (otherwise unidentified, who died in 1906). In 1905 they added the ind\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents only provide information about Janet Cumbrae Stewart’s birth year (1883) and general details about the Melbourne Society of Women Painters and Sculptors. They do not identify any other artist born in 1883 who was a fellow member of the Society, nor do they mention a painting featured on the cover of Women's World Magazine in 1923. Therefore, the necessary facts to answer the question are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 695, + "osl": 288, + "total_tokens": 983, + "latency_ms": 4056.45, + "tokens_per_second": 71.0 + }, + "context": { + "kept_docs_count": 2, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "99ddd640-77b5-4a8d-9751-f6f5472d7cce", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T02:42:11.240496Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: An Australian artist, born the same year as artist Janet Cumbrae Stewart and fellow member of the Melbourne Society of Women Painters and Sculptors, had her painting featured on the cover of Women's World Magazine in 1923. What is the name of the painting?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Australian painter Janet Cumbrae Stewart Self-portrait, 1911 Born Janet Agnes Stewart ( 1883-12-23 ) 23 December 1883 Melbourne , Victoria , Australia Died 8 September 1960 (1960-09-08) (aged 76) Melbourne Known for Painting Partner Miss Argemore ffarington \"Bill\" Bellairs Janet Agnes Cumbrae Stewart (23 December 1883 – 8 September 1960) was an Australian painter. She spent the 1920s and 1930s painting in Britain, France and Italy. Biography Cumbrae Stewart was born on 23 December 1883 in Brighton, Victoria , Australia. She was born Janet Agnes Stewart , the youngest of ten children born to Francis Edward Stewart (1833–1904) and Agnes Park (1843–1927). Janet's eldest brother, Francis William Sutton Stewart, became convinced of a family connection to the Stua\n\n[DOC 2] Australian visual art organisation Melbourne Society of Women Painters and Sculptors Formation 1902 Headquarters Melbourne, Australia Website mswps . com . au The Melbourne Society of Women Painters and Sculptors , established in Melbourne , Victoria in 1902, is the oldest surviving women's art group in Australia . History The Melbourne Society of Women Painters and Sculptors (MSWPS) began in 1902 as a monthly gathering of eight former students of Frederick McCubbin from the National Gallery School which members called the Students' Art Club . It is known that among these founders were Daisy Stone, Tina Gowdie, Annie Gates, Kate Allan, Ella Thorn, Henrietta Maria Gulliver and a Miss Stock (otherwise unidentified, who died in 1906). In 1905 they added the ind\n\n\nSEARCH HISTORY: - Query: 'Janet Cumbrae Stewart birth year' → 1 docs\n- Query: 'Melbourne Society of Women Painters and Sculptors members 1923' → 1 docs\n- Query: 'Women's World Magazine 1923 cover painting' → 3 docs\n- Query: 'Melbourne Society of Women Painters and Sculptors members born 1883' → 0 docs\n- Query: 'Women's World Magazine 1923 cover painting' → 0 docs\n- Query: 'Australian woman painter 1883 magazine cover' → 1 docs\n\nFEEDBACK: First query will give the birth year needed to identify the fellow artist. Second query lists society members around that time to find the matching artist. Third query should reveal which painting appeared on the 1923 magazine cover, giving the answer.\nWe need to identify a MSWPS member born in 1883 (same year as Janet Cumbrae Stewart) whose artwork appeared on the 1923 Women's World Magazine cover, then retrieve the painting's title.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"queries\":[\"\\\"Women's World Magazine\\\" 1923 cover\",\"Australian woman painter 1883 Melbourne Society\",\"Melbourne Society of Women Painters and Sculptors members 1883\"],\"feedback\":\"We need to locate the artist (born 1883, MSWPS member) whose work appeared on the 1923 cover of Women's World Magazine, then identify the painting title. The first query targets the magazine cover specifics; the second seeks artists matching the birth year and society affiliation; the third attempts to list society members born in 1883 to narrow candidates.\"}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1010, + "osl": 116, + "total_tokens": 1126, + "latency_ms": 6507.87, + "tokens_per_second": 17.82 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "6a0bdb14-26b4-405f-9a5a-6de5ed0ee556", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T02:42:16.106225Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: An Australian artist, born the same year as artist Janet Cumbrae Stewart and fellow member of the Melbourne Society of Women Painters and Sculptors, had her painting featured on the cover of Women's World Magazine in 1923. What is the name of the painting?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] ^ ^ a b ^ ^ ^ ^ a b ^ ^ ^ ^ ^ a b ^ [ dead link ] ^ [ dead link ] ^ ^ ^ a b ^ ^ [ dead link ] ^ ^ ^ [ dead link ] ^ ^ [ dead link ] ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ( Boston Globe , 1929-1953. St. Louis Post-Dispatch , 1954-1963.) ^ ^ Further reading online Solomon, Barbara Miller (1985). In the Company of Educated Women: A History of Women and Higher Education in America . Yale University Press. External links Wikimedia Commons has media related to Vassar College . Wikisource has the text of the 1911 Encyclopædia Britannica article \" Vassar College \". Official website Athletics website Catalog and gallery of Vassar references in popular culture\n\n[NEW 2] pression\", The American Economic Review 73 , No. 3 (June 1983): 257–276, available from the St. Louis Federal Reserve Bank collection at Stlouisfed. org Archived 5 March 2016 at the Wayback Machine ^ ^ W. S. Woytinsky and E. S. Woytinsky, World population and production: trends and outlook (1953) p. 148 ^ Denyse Baillargeon, Making Do: Women, Family and Home in Montreal during the Great Depression (Wilfrid Laurier University Press, 1999), p. 159. ^ ^ ^ ^ Jessica S. Bean, \" 'To help keep the home going': female labour supply in interwar London\". Economic History Review (2015) 68#2 pp. 441–470. ^ Deirdre Beddoe, Back to Home and Duty: Women Between the Wars, 1918–1939 (1989). ^ ^ Ann E. McCleary, \" 'I Was Really Proud of Them': Canned Raspberries and Home Prod\n\n[NEW 3] g h i j k l m n ^ ^ a b c ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ a b ^ ^ ^ a b ^ ^ a b c d e f ^ ^ ^ a b c d ^ ^ ^ As of June 30, 2020. ^ {{ cite news }} : CS1 maint: deprecated archival service ( link ) ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c d ^ ^ ^ a b ^ a b ^ a b ^ ^ [ permanent dead link ] ^ ^ ^ a b ^ ^ ^ ^ ^ Further reading Bashaw, Carolyn Terry. \" 'To serve the people of the state of Kentucky': Sarah Gibson Blanding and the development of administrative skill, 1923–1941.\" Filson Club History Quarterly (1991) 65#2 pp. 281–301. Blanding was the innovative Dean of Women. Birdwhistell, Terry L., and Deirdre A. Scaggs. Our Rightful Place: A History of Women at the University of Kentucky, 1\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] Australian painter Janet Cumbrae Stewart Self-portrait, 1911 Born Janet Agnes Stewart ( 1883-12-23 ) 23 December 1883 Melbourne , Victoria , Australia Died 8 September 1960 (1960-09-08) (aged 76) Melbourne Known for Painting Partner Miss Argemore ffarington \"Bill\" Bellairs Janet Agnes Cumbrae Stewar...\n[KEPT 2] Australian visual art organisation Melbourne Society of Women Painters and Sculptors Formation 1902 Headquarters Melbourne, Australia Website mswps . com . au The Melbourne Society of Women Painters and Sculptors , established in Melbourne , Victoria in 1902, is the oldest surviving women's art grou...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 3 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1139, + "osl": 526, + "total_tokens": 1665, + "latency_ms": 4289.18, + "tokens_per_second": 122.63 + }, + "context": { + "num_documents_evaluated": 3, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "34665a03-08d6-4d1d-902d-d288fbefe072", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T02:42:19.555122Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: An Australian artist, born the same year as artist Janet Cumbrae Stewart and fellow member of the Melbourne Society of Women Painters and Sculptors, had her painting featured on the cover of Women's World Magazine in 1923. What is the name of the painting?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Australian painter Janet Cumbrae Stewart Self-portrait, 1911 Born Janet Agnes Stewart ( 1883-12-23 ) 23 December 1883 Melbourne , Victoria , Australia Died 8 September 1960 (1960-09-08) (aged 76) Melbourne Known for Painting Partner Miss Argemore ffarington \"Bill\" Bellairs Janet Agnes Cumbrae Stewart (23 December 1883 – 8 September 1960) was an Australian painter. She spent the 1920s and 1930s painting in Britain, France and Italy. Biography Cumbrae Stewart was born on 23 December 1883 in Brighton, Victoria , Australia. She was born Janet Agnes Stewart , the youngest of ten children born to Francis Edward Stewart (1833–1904) and Agnes Park (1843–1927). Janet's eldest brother, Francis William Sutton Stewart, became convinced of a family connection to the Stua\n\n[DOC 2] Australian visual art organisation Melbourne Society of Women Painters and Sculptors Formation 1902 Headquarters Melbourne, Australia Website mswps . com . au The Melbourne Society of Women Painters and Sculptors , established in Melbourne , Victoria in 1902, is the oldest surviving women's art group in Australia . History The Melbourne Society of Women Painters and Sculptors (MSWPS) began in 1902 as a monthly gathering of eight former students of Frederick McCubbin from the National Gallery School which members called the Students' Art Club . It is known that among these founders were Daisy Stone, Tina Gowdie, Annie Gates, Kate Allan, Ella Thorn, Henrietta Maria Gulliver and a Miss Stock (otherwise unidentified, who died in 1906). In 1905 they added the ind\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The provided documents give Janet Cumbrae Stewart's birth year (1883) and general information about the Melbourne Society of Women Painters and Sculptors, but they do not identify any other artist born in 1883 who was a fellow member, nor do they mention a painting featured on the cover of Women's World Magazine in 1923. The specific artist and painting name are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 644, + "osl": 95, + "total_tokens": 739, + "latency_ms": 3448.08, + "tokens_per_second": 27.55 + }, + "context": { + "kept_docs_count": 2, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "5638c1c1-bed0-4348-8a35-4b843f86f41c", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T02:42:25.347642Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: An Australian artist, born the same year as artist Janet Cumbrae Stewart and fellow member of the Melbourne Society of Women Painters and Sculptors, had her painting featured on the cover of Women's World Magazine in 1923. What is the name of the painting?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Australian painter Janet Cumbrae Stewart Self-portrait, 1911 Born Janet Agnes Stewart ( 1883-12-23 ) 23 December 1883 Melbourne , Victoria , Australia Died 8 September 1960 (1960-09-08) (aged 76) Melbourne Known for Painting Partner Miss Argemore ffarington \"Bill\" Bellairs Janet Agnes Cumbrae Stewart (23 December 1883 – 8 September 1960) was an Australian painter. She spent the 1920s and 1930s painting in Britain, France and Italy. Biography Cumbrae Stewart was born on 23 December 1883 in Brighton, Victoria , Australia. She was born Janet Agnes Stewart , the youngest of ten children born to Francis Edward Stewart (1833–1904) and Agnes Park (1843–1927). Janet's eldest brother, Francis William Sutton Stewart, became convinced of a family connection to the Stua\n\n[DOC 2] Australian visual art organisation Melbourne Society of Women Painters and Sculptors Formation 1902 Headquarters Melbourne, Australia Website mswps . com . au The Melbourne Society of Women Painters and Sculptors , established in Melbourne , Victoria in 1902, is the oldest surviving women's art group in Australia . History The Melbourne Society of Women Painters and Sculptors (MSWPS) began in 1902 as a monthly gathering of eight former students of Frederick McCubbin from the National Gallery School which members called the Students' Art Club . It is known that among these founders were Daisy Stone, Tina Gowdie, Annie Gates, Kate Allan, Ella Thorn, Henrietta Maria Gulliver and a Miss Stock (otherwise unidentified, who died in 1906). In 1905 they added the ind\n\n\nSEARCH HISTORY: - Query: 'Janet Cumbrae Stewart birth year' → 1 docs\n- Query: 'Melbourne Society of Women Painters and Sculptors members 1923' → 1 docs\n- Query: 'Women's World Magazine 1923 cover painting' → 3 docs\n- Query: 'Melbourne Society of Women Painters and Sculptors members born 1883' → 0 docs\n- Query: 'Women's World Magazine 1923 cover painting' → 0 docs\n- Query: 'Australian woman painter 1883 magazine cover' → 1 docs\n- Query: '\"Women's World Magazine\" 1923 cover' → 3 docs\n- Query: 'Australian woman painter 1883 Melbourne Society' → 0 docs\n- Query: 'Melbourne Society of Women Painters and Sculptors members 1883' → 0 docs\n\nFEEDBACK: First query will give the birth year needed to identify the fellow artist. Second query lists society members around that time to find the matching artist. Third query should reveal which painting appeared on the 1923 magazine cover, giving the answer.\nWe need to identify a MSWPS member born in 1883 (same year as Janet Cumbrae Stewart) whose artwork appeared on the 1923 Women's World Magazine cover, then retrieve the painting's title.\nWe need to locate the artist (born 1883, MSWPS member) whose work appeared on the 1923 cover of Women's World Magazine, then identify the painting title. The first query targets the magazine cover specifics; the second seeks artists matching the birth year and society affiliation; the third attempts to list society members born in 1883 to narrow candidates.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Melbourne Society of Women Painters and Sculptors members\",\n \"Australian woman painter 1883\",\n \"Women's World Magazine 1923 painting\"\n ],\n \"feedback\": \"First query will retrieve a list of MSWPS members with biographical details (including birth years). Second query narrows down Australian women painters born in 1883 to identify the fellow member. Third query aims to locate the specific painting featured on the 1923 cover of Women's World Magazine, revealing its title.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1146, + "osl": 112, + "total_tokens": 1258, + "latency_ms": 5791.91, + "tokens_per_second": 19.34 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "1dc6f02d-36fa-4fab-8e6f-126b7eac171e", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T02:42:28.761105Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: An Australian artist, born the same year as artist Janet Cumbrae Stewart and fellow member of the Melbourne Society of Women Painters and Sculptors, had her painting featured on the cover of Women's World Magazine in 1923. What is the name of the painting?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] British-born Australian artist Dora Lynnell Wilson Dora Lynnell Wilson (1883-1946) British-born Australian artist c.1926 Born Dora Lynnell Wilson ( 1883-08-31 ) 31 August 1883 Newcastle upon Tyne , England Died 21 November 1946 (1946-11-21) (aged 63) Melbourne , Victoria , Australia Occupation Artist Dora Lynnell Wilson (31 August 1883 – 21 November 1946) was a British-born Australian artist, best known in her adopted country of Australia for her etchings and street scenes. Early life Dora Lynnell Wilson was born on 31 August 1883 in Newcastle upon Tyne , England. Her parents were James Wilson, agent, and Annie Maria, née Green. The family emigrated to the state of Victoria in Australia in 1884, when Dora was a year old. Education Wilson was educated at Some\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] Australian painter Janet Cumbrae Stewart Self-portrait, 1911 Born Janet Agnes Stewart ( 1883-12-23 ) 23 December 1883 Melbourne , Victoria , Australia Died 8 September 1960 (1960-09-08) (aged 76) Melbourne Known for Painting Partner Miss Argemore ffarington \"Bill\" Bellairs Janet Agnes Cumbrae Stewar...\n[KEPT 2] Australian visual art organisation Melbourne Society of Women Painters and Sculptors Formation 1902 Headquarters Melbourne, Australia Website mswps . com . au The Melbourne Society of Women Painters and Sculptors , established in Melbourne , Victoria in 1902, is the oldest surviving women's art grou...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 644, + "osl": 395, + "total_tokens": 1039, + "latency_ms": 2888.12, + "tokens_per_second": 136.77 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "d85d9e81-aefd-436a-b07d-d9067b926db9", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T02:42:37.774590Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: An Australian artist, born the same year as artist Janet Cumbrae Stewart and fellow member of the Melbourne Society of Women Painters and Sculptors, had her painting featured on the cover of Women's World Magazine in 1923. What is the name of the painting?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Australian painter Janet Cumbrae Stewart Self-portrait, 1911 Born Janet Agnes Stewart ( 1883-12-23 ) 23 December 1883 Melbourne , Victoria , Australia Died 8 September 1960 (1960-09-08) (aged 76) Melbourne Known for Painting Partner Miss Argemore ffarington \"Bill\" Bellairs Janet Agnes Cumbrae Stewart (23 December 1883 – 8 September 1960) was an Australian painter. She spent the 1920s and 1930s painting in Britain, France and Italy. Biography Cumbrae Stewart was born on 23 December 1883 in Brighton, Victoria , Australia. She was born Janet Agnes Stewart , the youngest of ten children born to Francis Edward Stewart (1833–1904) and Agnes Park (1843–1927). Janet's eldest brother, Francis William Sutton Stewart, became convinced of a family connection to the Stua\n\n[DOC 2] Australian visual art organisation Melbourne Society of Women Painters and Sculptors Formation 1902 Headquarters Melbourne, Australia Website mswps . com . au The Melbourne Society of Women Painters and Sculptors , established in Melbourne , Victoria in 1902, is the oldest surviving women's art group in Australia . History The Melbourne Society of Women Painters and Sculptors (MSWPS) began in 1902 as a monthly gathering of eight former students of Frederick McCubbin from the National Gallery School which members called the Students' Art Club . It is known that among these founders were Daisy Stone, Tina Gowdie, Annie Gates, Kate Allan, Ella Thorn, Henrietta Maria Gulliver and a Miss Stock (otherwise unidentified, who died in 1906). In 1905 they added the ind\n\n[DOC 3] British-born Australian artist Dora Lynnell Wilson Dora Lynnell Wilson (1883-1946) British-born Australian artist c.1926 Born Dora Lynnell Wilson ( 1883-08-31 ) 31 August 1883 Newcastle upon Tyne , England Died 21 November 1946 (1946-11-21) (aged 63) Melbourne , Victoria , Australia Occupation Artist Dora Lynnell Wilson (31 August 1883 – 21 November 1946) was a British-born Australian artist, best known in her adopted country of Australia for her etchings and street scenes. Early life Dora Lynnell Wilson was born on 31 August 1883 in Newcastle upon Tyne , England. Her parents were James Wilson, agent, and Annie Maria, née Green. The family emigrated to the state of Victoria in Australia in 1884, when Dora was a year old. Education Wilson was educated at Some\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents identify an Australian artist born in 1883 (Dora Lynnell Wilson) who could be a fellow member of the Melbourne Society of Women Painters and Sculptors, but they do not provide any information about a painting of hers being featured on the cover of Women's World Magazine in 1923, nor the name of such a painting.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 877, + "osl": 409, + "total_tokens": 1286, + "latency_ms": 9012.76, + "tokens_per_second": 45.38 + }, + "context": { + "kept_docs_count": 3, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "fe74dc76-4014-4748-b57b-392e90750238", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T02:42:39.198852Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: An Australian artist, born the same year as artist Janet Cumbrae Stewart and fellow member of the Melbourne Society of Women Painters and Sculptors, had her painting featured on the cover of Women's World Magazine in 1923. What is the name of the painting?\n\nDOCUMENTS:\n\n[DOC 1] Australian painter Janet Cumbrae Stewart Self-portrait, 1911 Born Janet Agnes Stewart ( 1883-12-23 ) 23 December 1883 Melbourne , Victoria , Australia Died 8 September 1960 (1960-09-08) (aged 76) Melbourne Known for Painting Partner Miss Argemore ffarington \"Bill\" Bellairs Janet Agnes Cumbrae Stewart (23 December 1883 – 8 September 1960) was an Australian painter. She spent the 1920s and 1930s painting in Britain, France and Italy. Biography Cumbrae Stewart was born on 23 December 1883 in Brighton, Victoria , Australia. She was born Janet Agnes Stewart , the youngest of ten children born to Francis Edward Stewart (1833–1904) and Agnes Park (1843–1927). Janet's eldest brother, Francis William Sutton Stewart, became convinced of a family connection to the Stua\n\n[DOC 2] Australian visual art organisation Melbourne Society of Women Painters and Sculptors Formation 1902 Headquarters Melbourne, Australia Website mswps . com . au The Melbourne Society of Women Painters and Sculptors , established in Melbourne , Victoria in 1902, is the oldest surviving women's art group in Australia . History The Melbourne Society of Women Painters and Sculptors (MSWPS) began in 1902 as a monthly gathering of eight former students of Frederick McCubbin from the National Gallery School which members called the Students' Art Club . It is known that among these founders were Daisy Stone, Tina Gowdie, Annie Gates, Kate Allan, Ella Thorn, Henrietta Maria Gulliver and a Miss Stock (otherwise unidentified, who died in 1906). In 1905 they added the ind\n\n[DOC 3] British-born Australian artist Dora Lynnell Wilson Dora Lynnell Wilson (1883-1946) British-born Australian artist c.1926 Born Dora Lynnell Wilson ( 1883-08-31 ) 31 August 1883 Newcastle upon Tyne , England Died 21 November 1946 (1946-11-21) (aged 63) Melbourne , Victoria , Australia Occupation Artist Dora Lynnell Wilson (31 August 1883 – 21 November 1946) was a British-born Australian artist, best known in her adopted country of Australia for her etchings and street scenes. Early life Dora Lynnell Wilson was born on 31 August 1883 in Newcastle upon Tyne , England. Her parents were James Wilson, agent, and Annie Maria, née Green. The family emigrated to the state of Victoria in Australia in 1884, when Dora was a year old. Education Wilson was educated at Some\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 701, + "osl": 1, + "total_tokens": 702, + "latency_ms": 1423.63, + "tokens_per_second": 0.7 + }, + "context": { + "num_documents_used": 3, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:42:39.199643Z", + "summary": { + "total_llm_calls": 13, + "total_hops": 5, + "total_input_tokens": 10593, + "total_output_tokens": 3689, + "total_tokens": 14282, + "total_latency_ms": 57134.15, + "average_tokens_per_second": 97.89, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Janet_Cumbrae_Stewart", + "https://en.wikipedia.org/wiki/Melbourne_Society_of_Women_Painters_and_Sculptors", + "https://en.wikipedia.org/wiki/Dora_Wilson" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "Reve d'Or" + } + }, + { + "query_id": "11", + "original_query": "As of July 1, 2024, what is the parent company of the current record label of the singer of Edge of Seventeen?", + "timestamp_start": "2026-05-16T02:42:39.209791Z", + "llm_calls": [ + { + "call_id": "97c3144a-461e-48f4-a538-50ba1eea131a", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:42:43.884731Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of July 1, 2024, what is the parent company of the current record label of the singer of Edge of Seventeen?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Edge of Seventeen singer\",\n \"Stevie Nicks record label\",\n \"Warner Records parent company\"\n ],\n \"feedback\": \"First query identifies the singer (Stevie Nicks). Second query determines her current record label as of 2024. Third query finds the parent company of that label.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 462, + "osl": 559, + "total_tokens": 1021, + "latency_ms": 4674.78, + "tokens_per_second": 119.58 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "3d1d59ae-ecb2-47f8-a857-7fbb3f2d3c07", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:43:20.364058Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of July 1, 2024, what is the parent company of the current record label of the singer of Edge of Seventeen?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] d No. 26 on Billboard ' s Mainstream Rock chart. The original album version of the song had previously made the top five of Billboard ' s Mainstream Rock chart in 1981, peaking at No. 4. \"Edge of Seventeen\" also peaked at No. 11 on the RPM Top 100 Singles chart in Canada. The song entered the UK chart in 2021 following its use in a John Lewis commercial. Personnel Musicians Stevie Nicks – lead vocals, writer Waddy Wachtel – guitar Bob Glaub – bass guitar Russ Kunkel – drums Bobbye Hall – percussion Benmont Tench – organ Roy Bittan – piano Lori Perry – backing vocals Sharon Celani – backing vocals Production Jimmy Iovine – producer Charts Weekly charts Chart (1981–1983) Peak position Canada Top Singles ( RPM ) 11 US Billboard Hot 100 11 US Mainstream Rock ( B\n\n[NEW 2] featured it, in which she expressed her admiration for both the song and the group. American actress and singer Lindsay Lohan covered \"Edge of Seventeen\" on her second studio album A Little More Personal (Raw) (2005). Deep Dish fulfilled their \"Dreams\" of working with Nicks in 2005 when Nicks offered to re-record vocals on a remix of her number-one penned song, \" Dreams \". The Deep Dish version went on to reach number two on the Billboard Hot Dance Airplay chart, as well as providing Nicks with her third UK top-40 hit. Nicks provided additional vocals on Vanessa Carlton's 2007 album, Heroes and Thieves . On January 31, 2010, Nicks performed with Taylor Swift at the 52nd Annual Grammy Awards . Swift, who describes Nicks as one of her childhood heroes, introdu\n\n[NEW 3] Defunct American record label cofounded by Stevie Nicks Record label Modern Records Founded 1980 ( 1980 ) Founder Stevie Nicks Danny Goldberg Paul Fishkin Defunct 1999 ( 1999 ) Status Inactive Distributors Atlantic WEA EMI Genre Rock pop Location Los Angeles, California Official website Modern Records album discography from BSN Pubs. Modern Records was a record label founded in 1980 by Stevie Nicks , Danny Goldberg, and Paul Fishkin. Its logo clearly stated the founding year to avoid confusion with the earlier Modern Records . The label had a distribution deal with Atlantic Records in the United States (also had international distribution with WEA and EMI) and Nicks was the biggest artist on the label, with other artists such as Joey Wilson, Venice , Sandy S\n\n[NEW 4] American record label Record label Reprise Records Parent company Warner Music Group Founded 1960 (original) 1987 (relaunch) Founder Frank Sinatra Defunct 1976 (original) Status Active Distributors Warner Records (United States) Warner Music Group (international) Rhino Entertainment Company (re-issues) Genre Various Country of origin United States Official website warnerrecords . com Reprise Records is an American record label founded in 1960 by Frank Sinatra . It is owned by Warner Music Group , and operates through Warner Records , one of its flagship labels. Artists currently signed to Reprise Records include Green Day , Enya , Michael Bublé , Eric Clapton , Stevie Nicks , Neil Young , Deftones , Lindsey Buckingham , Josh Groban , Disturbed , Idina Menzel\n\n[NEW 5] American record label Record label Warner Records Inc. Parent company Warner Music Group (WMG) Founded March 19, 1958 ; 68 years ago ( 1958-03-19 ) Founder Warner Bros. Distributors Self-distributed (United States) Warner Music Group (International) Rhino Entertainment Company (Reissues) Genre Various Country of origin United States Location Los Angeles , California , U. S. Official website warnerrecords . com Warner Records Inc. (also known as Warner Bros. Records Inc. until 2019) is an American record label . A subsidiary of Warner Music Group , it is headquartered in Los Angeles , California. It was founded on March 19, 1958, as the recorded music division of the American film studio Warner Bros. Pictures . History Founding At the end of the silent movie\n\n[NEW 6] American record label Record label Warner Records Inc. Parent company Warner Music Group (WMG) Founded March 19, 1958 ; 68 years ago ( 1958-03-19 ) Founder Warner Bros. Distributors Self-distributed (United States) Warner Music Group (International) Rhino Entertainment Company (Reissues) Genre Various Country of origin United States Location Los Angeles , California , U. S. Official website warnerrecords . com Warner Records Inc. (also known as Warner Bros. Records Inc. until 2019) is an American record label . A subsidiary of Warner Music Group , it is headquartered in Los Angeles , California. It was founded on March 19, 1958, as the recorded music division of the American film studio Warner Bros. Pictures . History Founding At the end of the silent movie\n\n[NEW 7] American multinational entertainment and record label conglomerate Warner Music Group Corp. Logo used since November 2021 Headquarters in 1633 Broadway, New York City Trade name Warner Music Group Formerly Warner Bros. Records (1958–1967, 1970–1971) Warner Bros.-Seven Arts (1967–1970) Kinney Record Group International (1970–1972) Warner-Elektra-Atlantic (1972–1991) Warner Music (1991–2001) Company type Public Traded as Nasdaq : WMG ( Class A ) S&P 400 component NYSE : WMG (2004–2011) ISIN US9345502036 Industry Music Entertainment Founded April 6, 1958 ; 67 years ago ( 1958-04-06 ) Founder Warner Bros. Headquarters 1633 Broadway , New York City, New York , U. S. Area served Worldwide (except for Russia & Belarus) Key people Michael Lynton ( chairman ) Len Bla\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 7 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 1, 1, 1, 1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1532, + "osl": 736, + "total_tokens": 2268, + "latency_ms": 35975.78, + "tokens_per_second": 20.46 + }, + "context": { + "num_documents_evaluated": 7, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "97b444ee-dee2-4ff6-80e9-695b40df3611", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:43:21.502776Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of July 1, 2024, what is the parent company of the current record label of the singer of Edge of Seventeen?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American record label Record label Reprise Records Parent company Warner Music Group Founded 1960 (original) 1987 (relaunch) Founder Frank Sinatra Defunct 1976 (original) Status Active Distributors Warner Records (United States) Warner Music Group (international) Rhino Entertainment Company (re-issues) Genre Various Country of origin United States Official website warnerrecords . com Reprise Records is an American record label founded in 1960 by Frank Sinatra . It is owned by Warner Music Group , and operates through Warner Records , one of its flagship labels. Artists currently signed to Reprise Records include Green Day , Enya , Michael Bublé , Eric Clapton , Stevie Nicks , Neil Young , Deftones , Lindsey Buckingham , Josh Groban , Disturbed , Idina Menzel\n\n[DOC 2] American record label Record label Warner Records Inc. Parent company Warner Music Group (WMG) Founded March 19, 1958 ; 68 years ago ( 1958-03-19 ) Founder Warner Bros. Distributors Self-distributed (United States) Warner Music Group (International) Rhino Entertainment Company (Reissues) Genre Various Country of origin United States Location Los Angeles , California , U. S. Official website warnerrecords . com Warner Records Inc. (also known as Warner Bros. Records Inc. until 2019) is an American record label . A subsidiary of Warner Music Group , it is headquartered in Los Angeles , California. It was founded on March 19, 1958, as the recorded music division of the American film studio Warner Bros. Pictures . History Founding At the end of the silent movie\n\n[DOC 3] American record label Record label Warner Records Inc. Parent company Warner Music Group (WMG) Founded March 19, 1958 ; 68 years ago ( 1958-03-19 ) Founder Warner Bros. Distributors Self-distributed (United States) Warner Music Group (International) Rhino Entertainment Company (Reissues) Genre Various Country of origin United States Location Los Angeles , California , U. S. Official website warnerrecords . com Warner Records Inc. (also known as Warner Bros. Records Inc. until 2019) is an American record label . A subsidiary of Warner Music Group , it is headquartered in Los Angeles , California. It was founded on March 19, 1958, as the recorded music division of the American film studio Warner Bros. Pictures . History Founding At the end of the silent movie\n\n[DOC 4] American multinational entertainment and record label conglomerate Warner Music Group Corp. Logo used since November 2021 Headquarters in 1633 Broadway, New York City Trade name Warner Music Group Formerly Warner Bros. Records (1958–1967, 1970–1971) Warner Bros.-Seven Arts (1967–1970) Kinney Record Group International (1970–1972) Warner-Elektra-Atlantic (1972–1991) Warner Music (1991–2001) Company type Public Traded as Nasdaq : WMG ( Class A ) S&P 400 component NYSE : WMG (2004–2011) ISIN US9345502036 Industry Music Entertainment Founded April 6, 1958 ; 67 years ago ( 1958-04-06 ) Founder Warner Bros. Headquarters 1633 Broadway , New York City, New York , U. S. Area served Worldwide (except for Russia & Belarus) Key people Michael Lynton ( chairman ) Len Bla\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": true,\n \"reasoning\": \"Document 1 identifies Stevie Nicks as a current artist signed to Reprise Records, establishing Reprise as her current record label. The same document states that Reprise Records is owned by Warner Music Group, providing the parent company. These facts together answer the question.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1039, + "osl": 238, + "total_tokens": 1277, + "latency_ms": 1137.79, + "tokens_per_second": 209.18 + }, + "context": { + "kept_docs_count": 4, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "420f43bc-2f90-4043-8969-942848db220c", + "component": "answer_generator", + "hop_count": 2, + "timestamp": "2026-05-16T02:43:22.942491Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: As of July 1, 2024, what is the parent company of the current record label of the singer of Edge of Seventeen?\n\nDOCUMENTS:\n\n[DOC 1] American record label Record label Reprise Records Parent company Warner Music Group Founded 1960 (original) 1987 (relaunch) Founder Frank Sinatra Defunct 1976 (original) Status Active Distributors Warner Records (United States) Warner Music Group (international) Rhino Entertainment Company (re-issues) Genre Various Country of origin United States Official website warnerrecords . com Reprise Records is an American record label founded in 1960 by Frank Sinatra . It is owned by Warner Music Group , and operates through Warner Records , one of its flagship labels. Artists currently signed to Reprise Records include Green Day , Enya , Michael Bublé , Eric Clapton , Stevie Nicks , Neil Young , Deftones , Lindsey Buckingham , Josh Groban , Disturbed , Idina Menzel\n\n[DOC 2] American record label Record label Warner Records Inc. Parent company Warner Music Group (WMG) Founded March 19, 1958 ; 68 years ago ( 1958-03-19 ) Founder Warner Bros. Distributors Self-distributed (United States) Warner Music Group (International) Rhino Entertainment Company (Reissues) Genre Various Country of origin United States Location Los Angeles , California , U. S. Official website warnerrecords . com Warner Records Inc. (also known as Warner Bros. Records Inc. until 2019) is an American record label . A subsidiary of Warner Music Group , it is headquartered in Los Angeles , California. It was founded on March 19, 1958, as the recorded music division of the American film studio Warner Bros. Pictures . History Founding At the end of the silent movie\n\n[DOC 3] American record label Record label Warner Records Inc. Parent company Warner Music Group (WMG) Founded March 19, 1958 ; 68 years ago ( 1958-03-19 ) Founder Warner Bros. Distributors Self-distributed (United States) Warner Music Group (International) Rhino Entertainment Company (Reissues) Genre Various Country of origin United States Location Los Angeles , California , U. S. Official website warnerrecords . com Warner Records Inc. (also known as Warner Bros. Records Inc. until 2019) is an American record label . A subsidiary of Warner Music Group , it is headquartered in Los Angeles , California. It was founded on March 19, 1958, as the recorded music division of the American film studio Warner Bros. Pictures . History Founding At the end of the silent movie\n\n[DOC 4] American multinational entertainment and record label conglomerate Warner Music Group Corp. Logo used since November 2021 Headquarters in 1633 Broadway, New York City Trade name Warner Music Group Formerly Warner Bros. Records (1958–1967, 1970–1971) Warner Bros.-Seven Arts (1967–1970) Kinney Record Group International (1970–1972) Warner-Elektra-Atlantic (1972–1991) Warner Music (1991–2001) Company type Public Traded as Nasdaq : WMG ( Class A ) S&P 400 component NYSE : WMG (2004–2011) ISIN US9345502036 Industry Music Entertainment Founded April 6, 1958 ; 67 years ago ( 1958-04-06 ) Founder Warner Bros. Headquarters 1633 Broadway , New York City, New York , U. S. Area served Worldwide (except for Russia & Belarus) Key people Michael Lynton ( chairman ) Len Bla\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Warner Music Group.", + "finish_reason": "stop" + }, + "metrics": { + "isl": 893, + "osl": 137, + "total_tokens": 1030, + "latency_ms": 1439.09, + "tokens_per_second": 95.2 + }, + "context": { + "num_documents_used": 4, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:43:22.943179Z", + "summary": { + "total_llm_calls": 4, + "total_hops": 2, + "total_input_tokens": 3926, + "total_output_tokens": 1670, + "total_tokens": 5596, + "total_latency_ms": 43227.44, + "average_tokens_per_second": 111.11, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Edge_of_Seventeen", + "https://en.wikipedia.org/wiki/Stevie_Nicks", + "https://en.wikipedia.org/wiki/Reprise_Records", + "https://en.wikipedia.org/wiki/Atlantic_Records", + "https://en.wikipedia.org/wiki/Modern_Records_(1980)", + "https://en.wikipedia.org/wiki/Warner_Music_Group", + "https://en.wikipedia.org/wiki/Warner_Records" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Warner Music Group.", + "ground_truth_answer": "Warner Music Group" + } + }, + { + "query_id": "12", + "original_query": "The Basibasy mine is located in Madagascar. This mine is abundant in a specific chemical element that was discovered for the first time in 1791. The person who discovered this element was born on what is now known as a major US holiday - what holiday is this?", + "timestamp_start": "2026-05-16T02:43:22.953555Z", + "llm_calls": [ + { + "call_id": "8ccd6289-f50a-42d8-a497-617ff4b0862f", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:43:36.366221Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: The Basibasy mine is located in Madagascar. This mine is abundant in a specific chemical element that was discovered for the first time in 1791. The person who discovered this element was born on what is now known as a major US holiday - what holiday is this?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"1791 discovered element\",\n \"Basibasy mine Madagascar element\",\n \"William Gregor birthday\"\n ],\n \"feedback\": \"First query will identify which chemical element was first isolated in 1791. Second query links the Basibasy mine to the specific element it contains. Third query retrieves the discoverer's birth date to determine the US holiday.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 492, + "osl": 989, + "total_tokens": 1481, + "latency_ms": 13412.53, + "tokens_per_second": 73.74 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "bbac8dad-18c1-491f-bfb0-002204fe2fdc", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:44:15.691773Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: The Basibasy mine is located in Madagascar. This mine is abundant in a specific chemical element that was discovered for the first time in 1791. The person who discovered this element was born on what is now known as a major US holiday - what holiday is this?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] ember 1789 he announced his discovery to the Royal Prussian Academy of sciences in Berlin. He also discovered zirconium in 1789, separating it in the form of its \"earth\" zirconia, oxide ZrO 2 . Klaproth analyzed a brightly-colored form of the mineral called \"hyacinth\" from Ceylon. He gave the new element the name zirconium based on its Persian name \"zargun\", gold-colored. Klaproth characterised uranium and zirconium as distinct elements , though he was unable to isolate them. Klaproth independently discovered cerium (1803), a rare earth element , around the same time as Jöns Jacob Berzelius and Wilhelm Hisinger , in the winter of 1803. William Gregor of Cornwall was the first to identify the element titanium in 1791, correctly concluding that he had found a\n\n[NEW 2] Gregor (1791) First isolation Jöns Jakob Berzelius (1825) Named by Martin Heinrich Klaproth (1795) Isotopes of titanium v e Main isotopes Decay Isotope abun­dance half-life ( t 1/2 ) mode pro­duct 44 Ti synth 59.1 y ε 44 Sc 45 Ti synth 3.08 h β + 45 Sc 46 Ti 8.25% stable 47 Ti 7.44% stable 48 Ti 73.7% stable 49 Ti 5.41% stable 50 Ti 5.18% stable Category: Titanium view talk edit | references Titanium is a chemical element ; it has symbol Ti and atomic number 22. Found in nature only as an oxide , it can be reduced to produce a lustrous transition metal with a silver color , low density , and high strength that is resistant to corrosion in sea water , aqua regia , and chlorine . Titanium was discovered in Cornwall , Great Britain , by William Gregor in 1791 a\n\n[NEW 3] Mine in Basibasy, Atsimo-Andrefana, Madagascar Basibasy mine Basibasy mine Location Location Basibasy Atsimo-Andrefana Country Madagascar Coordinates 22°9′S 43°36′E  /  22.150°S 43.600°E  / -22.150; 43.600 Production Products Titanium The Basibasy mine is one of the largest titanium mines in Madagascar . The mine is located in Basibasy , Atsimo-Andrefana . The mine has reserves amounting to 446 million tonnes of ore grading 5.5% titanium . References ^ a b c This article about a specific mine is a stub . You can help Wikipedia by adding missing information . v t e This Atsimo-Andrefana location article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[NEW 4] Chemical element with atomic number 79 (Au) Gold, 79 Au Gold Appearance Metallic yellow Standard atomic weight A r °(Au) 196.966 570 ± 0.000 004 196.97 ± 0.01 ( abridged ) Gold in the periodic table Hydrogen Helium Lithium Beryllium Boron Carbon Nitrogen Oxygen Fluorine Neon Sodium Magnesium Aluminium Silicon Phosphorus Sulfur Chlorine Argon Potassium Calcium Scandium Titanium Vanadium Chromium Manganese Iron Cobalt Nickel Copper Zinc Gallium Germanium Arsenic Selenium Bromine Krypton Rubidium Strontium Yttrium Zirconium Niobium Molybdenum Technetium Ruthenium Rhodium Palladium Silver Cadmium Indium Tin Antimony Tellurium Iodine Xenon Caesium Barium Lanthanum Cerium Praseodymium Neodymium Promethium Samarium Europium Gadolinium Terbium Dysprosium Holmium Erb\n\n[NEW 5] olivia), Antamina (Peru), Rudna (Poland), and Penasquito (Mexico). Top near-term mine development projects through 2015 are Pascua Lama (Chile), Navidad (Argentina), Jaunicipio (Mexico), Malku Khota (Bolivia), and Hackett River (Canada). In Central Asia , Tajikistan is known to have some of the largest silver deposits in the world. Silver is usually found in nature combined with other metals, or in minerals that contain silver compounds, generally in the form of sulfides such as galena (lead sulfide) or cerussite (lead carbonate). So the primary production of silver requires the smelting and then cupellation of argentiferous lead ores, a historically important process. Lead melts at 327 °C, lead oxide at 888 °C and silver melts at 960 °C. To separate the sil\n\n[NEW 6] tadtium Roentgenium Copernicium Nihonium Flerovium Moscovium Livermorium Tennessine Oganesson 32 columns Hydrogen Helium Lithium Beryllium Boron Carbon Nitrogen Oxygen Fluorine Neon Sodium Magnesium Aluminium Silicon Phosphorus Sulfur Chlorine Argon Potassium Calcium Scandium Titanium Vanadium Chromium Manganese Iron Cobalt Nickel Copper Zinc Gallium Germanium Arsenic Selenium Bromine Krypton Rubidium Strontium Yttrium Zirconium Niobium Molybdenum Technetium Ruthenium Rhodium Palladium Silver Cadmium Indium Tin Antimony Tellurium Iodine Xenon Caesium Barium Lutetium Hafnium Tantalum Tungsten Rhenium Osmium Iridium Platinum Gold Mercury (element) Thallium Lead Bismuth Polonium Astatine Radon Francium Radium Lawrencium Rutherfordium Dubnium Seaborgium Bohrium\n\n[NEW 7] + 1 ⁄ 2 Fast 2:37.75 1876 Vagrant Robert Swim James Williams William Astor Jr. 1 + 1 ⁄ 2 Fast 2:38.25 1877 Baden-Baden Billy Walker Ed Brown Daniel Swigert 1 + 1 ⁄ 2 Fast 2:38.0 1878 Day Star Jimmy Carter Lee Paul Thomas J. Nichols 1 + 1 ⁄ 2 Fast 2:37.25 1879 Lord Murphy Charlie Shauer George Rice George W. Darden & Co. 1 + 1 ⁄ 2 Fast 2:37.00 1880 Fonso George Lewis Tice Hutsell J. S. Shawhan 1 + 1 ⁄ 2 Fast 2:37.50 1881 Hindoo †‡ Jim McLaughlin James Rowe Sr. Dwyer Brothers 1 + 1 ⁄ 2 Fast 2:40.0 1882 Apollo Babe Hurd Green B. Morris Green B. Morris, James D. Patton 1 + 1 ⁄ 2 Fast 2:40.25 1883 Leonatus William Donohue Raleigh Colston Sr. Jack P. Chinn, George Morgan 1 + 1 ⁄ 2 Heavy 2:43.0 1884 Buchanan Isaac Murphy William Bird William Cottrill, Sam S. Brown\n\n[NEW 8] & Wolff 2019 , p. 261. ^ a b Huehnergard 2004 , p. 139. ^ Gragg 2019 , p. 26. ^ a b c Meyer & Wolff 2019 , p. 262. ^ a b Lipiński 2001 , p. 24. ^ Hayward 2000 , pp. 78–80. ^ Fleming 2006 . ^ Güldemann 2018 , p. 342. ^ a b Huehnergard 2004 , p. 140. ^ Güldemann 2018 , p. 327. ^ a b c d e f Meyer & Wolff 2019 , p. 251. ^ Güldemann 2018 , p. 282. ^ Meyer & Wolff 2019 , p. 258. ^ Peust 2012 , p. 231. ^ Blench 2008 . ^ a b c Frajzyngier 2018 . ^ Peust 2012 , p. 225-227. ^ a b c d e f Gragg 2019 , p. 43. ^ Blench 2006 , p. 145. ^ a b Sanker 2023 , p. 29. ^ Güldemann 2018 , pp. 312–313. ^ a b Bacovcin & Wilson 2018 , p. 422. ^ a b Güldemann 2018 , p. 310. ^ a b Peust 2012 , p. 227. ^ a b Militarev 2005 , pp. 398–399. ^ a b Blažek 2013 , p. 1. ^ Bacovcin & Wilson 20\n\n[NEW 9] hyte & Whyte 1991 , p. 100. ^ ^ Macaulay 1975 , p. 174. ^ Aldrich 2019 , p. 25. ^ Rickman 1848 , p. 47. ^ a b ^ ^ Lindfield 2016 , p. 78. ^ ^ ^ Bartlett 2001 , p. 14. ^ Lindfield 2016 , p. 224. ^ Anstruther 1963 , preface. ^ Graur 1970 , p. 233. ^ Chadenet 2001 , pp. 116–117, 138. ^ Beard 1985 , p. 72. ^ Pevsner 1951 , foreword. ^ ^ ^ Bartlett 2001 , p. 15. ^ ^ ^ ^ Lowenthal 2015 , p. 416. ^ ^ ^ a b Midant 2002 , p. 54. ^ Midant 2002 , p. 19. ^ a b c Midant 2002 , p. 96. ^ ^ ^ Midant 2002 , p. 108. ^ Toker 1991 , pp. xviii–xix. ^ Menin 1775 , p. 5. ^ ^ Brichta 2014 , p. ?. ^ Germann 1972 , p. 152. ^ ^ ^ ^ ^ ^ ^ ^ ^ Tuleshkov 2007 , p. ?. ^ Stamp 1995 , pp. 108–110. ^ Jackson 2011 , p. 152. ^ a b c Hitchcock 1968 , p. 94. ^ Hull 2006 , p. 154. ^ Glendenning,\n\n[NEW 10] nterer # Saunterer 1882 Apollo Vanguard Forester 1883 Leonatus Jacobus George Kinney 1884 Buchanan Knight of Ellerslie Panique 1885 Joe Cotton Tecumseh Tyrant 1886 Ben Ali The Bard Inspector B 1887 Montrose Dunboyne Hanover 1888 Macbeth II Refund Sir Dixon 1889 Spokane Buddhist Eric 1890 Riley Montague Burlington 1891 Kingman RNR Foxford 1892 Azra RNR Patron 1893 Lookout RNR Commanche 1894 Chant Assignee Henry of Navarre 1895 Halma # Belmar # Belmar 1896 Ben Brush Margrave Hastings 1897 Typhoon II Paul Kauvar Scottish Chieftain 1898 Plaudit Sly Fox Bowling Brook 1899 Manuel Half Time Jean Bereaud 1900 Lieut. Gibson Hindus Ildrim 1901 His Eminence The Parader Commando 1902 Alan-a-Dale Old England Masterman 1903 Judge Himes Flocarline [Fy] Africander 1904 Elwo\n\n[NEW 11] . 3–5. ^ Gray 2005 , pp. 147–148. ^ a b c ^ a b Hamalian 1974 , p. 3. ^ ^ Corngold 1972 , pp. xii, 11. ^ a b Kafka-Franz, Father 2012 . ^ Brod 1960 , p. 9. ^ a b Brod 1960 , pp. 15–16. ^ Brod 1960 , pp. 19–20. ^ Brod 1960 , pp. 15, 17, 22–23. ^ Stach 2005 , p. 22 . ^ Stach 2005 , pp. 390–391 , 462–463 . ^ Stach 2005 , p. 13. ^ Brod 1960 , pp. 26–27. ^ a b Hawes 2008 , p. 29. ^ a b c Sayer 1996 , pp. 164–210. ^ Kempf 2005 , pp. 159–160. ^ Corngold 2004 , p. xii. ^ Karl 1991 , p. 148. ^ Diamant 2003 , pp. 36–38. ^ Brod 1960 , pp. 40–41. ^ a b Gray 2005 , p. 179. ^ Stach 2005 , pp. 43–70. ^ Spector 2000 , p. 17. ^ Keren 1993 , p. 3. ^ Brod 1960 , p. 40. ^ a b Brod 1960 , p. 14. ^ Brod 1966 , pp. 53–54. ^ ^ Stach 2005 , p. 362. ^ Gray 2005 , pp. 74, 273. ^ Brod\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 11 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1,1,1,0,0,1,0,0,0,0,0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 3188, + "osl": 3697, + "total_tokens": 6885, + "latency_ms": 38676.96, + "tokens_per_second": 95.59 + }, + "context": { + "num_documents_evaluated": 11, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "3cbc022a-a657-443e-9e52-006e4a54264a", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:44:20.500191Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: The Basibasy mine is located in Madagascar. This mine is abundant in a specific chemical element that was discovered for the first time in 1791. The person who discovered this element was born on what is now known as a major US holiday - what holiday is this?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] ember 1789 he announced his discovery to the Royal Prussian Academy of sciences in Berlin. He also discovered zirconium in 1789, separating it in the form of its \"earth\" zirconia, oxide ZrO 2 . Klaproth analyzed a brightly-colored form of the mineral called \"hyacinth\" from Ceylon. He gave the new element the name zirconium based on its Persian name \"zargun\", gold-colored. Klaproth characterised uranium and zirconium as distinct elements , though he was unable to isolate them. Klaproth independently discovered cerium (1803), a rare earth element , around the same time as Jöns Jacob Berzelius and Wilhelm Hisinger , in the winter of 1803. William Gregor of Cornwall was the first to identify the element titanium in 1791, correctly concluding that he had found a\n\n[DOC 2] Gregor (1791) First isolation Jöns Jakob Berzelius (1825) Named by Martin Heinrich Klaproth (1795) Isotopes of titanium v e Main isotopes Decay Isotope abun­dance half-life ( t 1/2 ) mode pro­duct 44 Ti synth 59.1 y ε 44 Sc 45 Ti synth 3.08 h β + 45 Sc 46 Ti 8.25% stable 47 Ti 7.44% stable 48 Ti 73.7% stable 49 Ti 5.41% stable 50 Ti 5.18% stable Category: Titanium view talk edit | references Titanium is a chemical element ; it has symbol Ti and atomic number 22. Found in nature only as an oxide , it can be reduced to produce a lustrous transition metal with a silver color , low density , and high strength that is resistant to corrosion in sea water , aqua regia , and chlorine . Titanium was discovered in Cornwall , Great Britain , by William Gregor in 1791 a\n\n[DOC 3] Mine in Basibasy, Atsimo-Andrefana, Madagascar Basibasy mine Basibasy mine Location Location Basibasy Atsimo-Andrefana Country Madagascar Coordinates 22°9′S 43°36′E  /  22.150°S 43.600°E  / -22.150; 43.600 Production Products Titanium The Basibasy mine is one of the largest titanium mines in Madagascar . The mine is located in Basibasy , Atsimo-Andrefana . The mine has reserves amounting to 446 million tonnes of ore grading 5.5% titanium . References ^ a b c This article about a specific mine is a stub . You can help Wikipedia by adding missing information . v t e This Atsimo-Andrefana location article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 4] tadtium Roentgenium Copernicium Nihonium Flerovium Moscovium Livermorium Tennessine Oganesson 32 columns Hydrogen Helium Lithium Beryllium Boron Carbon Nitrogen Oxygen Fluorine Neon Sodium Magnesium Aluminium Silicon Phosphorus Sulfur Chlorine Argon Potassium Calcium Scandium Titanium Vanadium Chromium Manganese Iron Cobalt Nickel Copper Zinc Gallium Germanium Arsenic Selenium Bromine Krypton Rubidium Strontium Yttrium Zirconium Niobium Molybdenum Technetium Ruthenium Rhodium Palladium Silver Cadmium Indium Tin Antimony Tellurium Iodine Xenon Caesium Barium Lutetium Hafnium Tantalum Tungsten Rhenium Osmium Iridium Platinum Gold Mercury (element) Thallium Lead Bismuth Polonium Astatine Radon Francium Radium Lawrencium Rutherfordium Dubnium Seaborgium Bohrium\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Documents provide the element (titanium) abundant in the Basibasy mine and its discovery in 1791 by William Gregor. However, none of the kept documents contain William Gregor's birthdate, which is needed to determine the major US holiday on which he was born.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1100, + "osl": 73, + "total_tokens": 1173, + "latency_ms": 4807.45, + "tokens_per_second": 15.18 + }, + "context": { + "kept_docs_count": 4, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "42440c27-256f-4e46-942d-e6638fdb50d2", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:44:28.143763Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: The Basibasy mine is located in Madagascar. This mine is abundant in a specific chemical element that was discovered for the first time in 1791. The person who discovered this element was born on what is now known as a major US holiday - what holiday is this?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] ember 1789 he announced his discovery to the Royal Prussian Academy of sciences in Berlin. He also discovered zirconium in 1789, separating it in the form of its \"earth\" zirconia, oxide ZrO 2 . Klaproth analyzed a brightly-colored form of the mineral called \"hyacinth\" from Ceylon. He gave the new element the name zirconium based on its Persian name \"zargun\", gold-colored. Klaproth characterised uranium and zirconium as distinct elements , though he was unable to isolate them. Klaproth independently discovered cerium (1803), a rare earth element , around the same time as Jöns Jacob Berzelius and Wilhelm Hisinger , in the winter of 1803. William Gregor of Cornwall was the first to identify the element titanium in 1791, correctly concluding that he had found a\n\n[DOC 2] Gregor (1791) First isolation Jöns Jakob Berzelius (1825) Named by Martin Heinrich Klaproth (1795) Isotopes of titanium v e Main isotopes Decay Isotope abun­dance half-life ( t 1/2 ) mode pro­duct 44 Ti synth 59.1 y ε 44 Sc 45 Ti synth 3.08 h β + 45 Sc 46 Ti 8.25% stable 47 Ti 7.44% stable 48 Ti 73.7% stable 49 Ti 5.41% stable 50 Ti 5.18% stable Category: Titanium view talk edit | references Titanium is a chemical element ; it has symbol Ti and atomic number 22. Found in nature only as an oxide , it can be reduced to produce a lustrous transition metal with a silver color , low density , and high strength that is resistant to corrosion in sea water , aqua regia , and chlorine . Titanium was discovered in Cornwall , Great Britain , by William Gregor in 1791 a\n\n[DOC 3] Mine in Basibasy, Atsimo-Andrefana, Madagascar Basibasy mine Basibasy mine Location Location Basibasy Atsimo-Andrefana Country Madagascar Coordinates 22°9′S 43°36′E  /  22.150°S 43.600°E  / -22.150; 43.600 Production Products Titanium The Basibasy mine is one of the largest titanium mines in Madagascar . The mine is located in Basibasy , Atsimo-Andrefana . The mine has reserves amounting to 446 million tonnes of ore grading 5.5% titanium . References ^ a b c This article about a specific mine is a stub . You can help Wikipedia by adding missing information . v t e This Atsimo-Andrefana location article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 4] tadtium Roentgenium Copernicium Nihonium Flerovium Moscovium Livermorium Tennessine Oganesson 32 columns Hydrogen Helium Lithium Beryllium Boron Carbon Nitrogen Oxygen Fluorine Neon Sodium Magnesium Aluminium Silicon Phosphorus Sulfur Chlorine Argon Potassium Calcium Scandium Titanium Vanadium Chromium Manganese Iron Cobalt Nickel Copper Zinc Gallium Germanium Arsenic Selenium Bromine Krypton Rubidium Strontium Yttrium Zirconium Niobium Molybdenum Technetium Ruthenium Rhodium Palladium Silver Cadmium Indium Tin Antimony Tellurium Iodine Xenon Caesium Barium Lutetium Hafnium Tantalum Tungsten Rhenium Osmium Iridium Platinum Gold Mercury (element) Thallium Lead Bismuth Polonium Astatine Radon Francium Radium Lawrencium Rutherfordium Dubnium Seaborgium Bohrium\n\n\nSEARCH HISTORY: - Query: '1791 discovered element' → 2 docs\n- Query: 'Basibasy mine Madagascar element' → 4 docs\n- Query: 'William Gregor birthday' → 5 docs\n\nFEEDBACK: First query will identify which chemical element was first isolated in 1791. Second query links the Basibasy mine to the specific element it contains. Third query retrieves the discoverer's birth date to determine the US holiday.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Titanium discovery 1791\",\n \"William Gregor birth date\",\n \"US federal holidays dates\"\n ],\n \"feedback\": \"The first two queries retrieve the element (titanium) and the discoverer's birth date. The third query obtains a list of official U.S. holidays with their calendar dates, allowing us to match the discoverer's birthday to the corresponding holiday.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1362, + "osl": 1757, + "total_tokens": 3119, + "latency_ms": 7643.03, + "tokens_per_second": 229.88 + }, + "context": { + "kept_docs_count": 4, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "04b02a1f-6008-485d-bfa3-50d40bc6ed50", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T02:45:29.989909Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: The Basibasy mine is located in Madagascar. This mine is abundant in a specific chemical element that was discovered for the first time in 1791. The person who discovered this element was born on what is now known as a major US holiday - what holiday is this?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] ember 1789 he announced his discovery to the Royal Prussian Academy of sciences in Berlin. He also discovered zirconium in 1789, separating it in the form of its \"earth\" zirconia, oxide ZrO 2 . Klaproth analyzed a brightly-colored form of the mineral called \"hyacinth\" from Ceylon. He gave the new element the name zirconium based on its Persian name \"zargun\", gold-colored. Klaproth characterised uranium and zirconium as distinct elements , though he was unable to isolate them. Klaproth independently discovered cerium (1803), a rare earth element , around the same time as Jöns Jacob Berzelius and Wilhelm Hisinger , in the winter of 1803. William Gregor of Cornwall was the first to identify the element titanium in 1791, correctly concluding that he had found a\n\n[DOC 2] Gregor (1791) First isolation Jöns Jakob Berzelius (1825) Named by Martin Heinrich Klaproth (1795) Isotopes of titanium v e Main isotopes Decay Isotope abun­dance half-life ( t 1/2 ) mode pro­duct 44 Ti synth 59.1 y ε 44 Sc 45 Ti synth 3.08 h β + 45 Sc 46 Ti 8.25% stable 47 Ti 7.44% stable 48 Ti 73.7% stable 49 Ti 5.41% stable 50 Ti 5.18% stable Category: Titanium view talk edit | references Titanium is a chemical element ; it has symbol Ti and atomic number 22. Found in nature only as an oxide , it can be reduced to produce a lustrous transition metal with a silver color , low density , and high strength that is resistant to corrosion in sea water , aqua regia , and chlorine . Titanium was discovered in Cornwall , Great Britain , by William Gregor in 1791 a\n\n[DOC 3] Mine in Basibasy, Atsimo-Andrefana, Madagascar Basibasy mine Basibasy mine Location Location Basibasy Atsimo-Andrefana Country Madagascar Coordinates 22°9′S 43°36′E  /  22.150°S 43.600°E  / -22.150; 43.600 Production Products Titanium The Basibasy mine is one of the largest titanium mines in Madagascar . The mine is located in Basibasy , Atsimo-Andrefana . The mine has reserves amounting to 446 million tonnes of ore grading 5.5% titanium . References ^ a b c This article about a specific mine is a stub . You can help Wikipedia by adding missing information . v t e This Atsimo-Andrefana location article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 4] tadtium Roentgenium Copernicium Nihonium Flerovium Moscovium Livermorium Tennessine Oganesson 32 columns Hydrogen Helium Lithium Beryllium Boron Carbon Nitrogen Oxygen Fluorine Neon Sodium Magnesium Aluminium Silicon Phosphorus Sulfur Chlorine Argon Potassium Calcium Scandium Titanium Vanadium Chromium Manganese Iron Cobalt Nickel Copper Zinc Gallium Germanium Arsenic Selenium Bromine Krypton Rubidium Strontium Yttrium Zirconium Niobium Molybdenum Technetium Ruthenium Rhodium Palladium Silver Cadmium Indium Tin Antimony Tellurium Iodine Xenon Caesium Barium Lutetium Hafnium Tantalum Tungsten Rhenium Osmium Iridium Platinum Gold Mercury (element) Thallium Lead Bismuth Polonium Astatine Radon Francium Radium Lawrencium Rutherfordium Dubnium Seaborgium Bohrium\n\n[DOC 5] the lead mineral mimetite and the nickel mineral niccolite , and others. But he is best known for one of his earliest discoveries: in 1791, while analysing the minerals in a black sand he had discovered in the Manaccan valley, he isolated the calx of an unknown metal which he named manaccanite . Later in 1791, Martin Heinrich Klaproth discovered what is now known as the transition metal , titanium in the mineral rutile . Believing this to be a new discovery, Klaproth named it titanium after the Titans of Greek Mythology , but eventually it was clarified that Gregor made the discovery first. Gregor was credited with the discovery, but the element kept the name chosen by Klaproth. Gregor later found titanium in corundum from Tibet , and in a tourmaline from a\n\n[DOC 6] Scottish-American naturalist (1838–1914) John Muir Portrait by Carleton E. Watkins c. 1875 Born ( 1838-04-21 ) April 21, 1838 Dunbar , Scotland Died December 24, 1914 (1914-12-24) (aged 76) Los Angeles, California, U. S. Alma mater University of Wisconsin–Madison Occupations Farmer inventor naturalist philosopher writer botanist zoologist geologist environmentalist Spouse Louisa Strentzel ( m. 1880; died 1905) Children 2 Signature John Muir ( / m jʊər / MURE ; April 21, 1838 – December 24, 1914), also known as \"John of the Mountains\" and \"Father of the National Parks \", was a Scottish-born American naturalist , author, environmental philosopher , botanist , zoologist , glaciologist , and early advocate for the preservation of wilderness in the United States.\n\n[DOC 7] ian-American actor (born 1882) 1957 – Irving Langmuir , American chemist and physicist, Nobel Prize laureate (born 1881) 1958 – Jacob M. Lomakin , Soviet Consul General in New York City, journalist and economist (born 1904) 1959 – William Halsey, Jr. , American admiral (born 1882) 1959 – Wanda Landowska , Polish-French harpsichord player (born 1879) 1961 – Abdul Haq , Pakistani linguist and scholar (born 1870) 1963 – Joan Eardley , British artist (born 1921) 1971 – Spyros Skouras , Greek-American businessman (born 1893) 1972 – Pierre Brasseur , French actor and screenwriter (born 1905) 1973 – Selman Waksman , Ukrainian-American biochemist and microbiologist, Nobel Prize laureate (born 1888) 1977 – Elvis Presley , American singer and actor (born 1935) 1978 –\n\n[DOC 8] 40–1913) 1872 1877 5 years Democratic TBA 24 Jacob Carl DeGress (TBA–TBA) 1877 1879 2 years Unknown TBA 25 William A. Saylor (TBA–TBA) 1st time 1879 1881 2 years Unknown TBA 26 L. M. Crooker (TBA–TBA) 1881 1883 2 years Unknown TBA 27 William A. Saylor (TBA–TBA) 2nd time 1883 1884 1 year Unknown TBA 28 John W. Robertson (TBA–TBA) 1884 1887 3 years Unknown TBA 29 Joseph Nalle (TBA–TBA) 1887 1890 3 years Unknown TBA 30 John McDonald (TBA–TBA) 1890 1895 5 years Unknown TBA 31 Louis Hancock (TBA–TBA) 1896 1897 1 year Unknown TBA 32 John Dodd McCall (TBA–TBA) 1897 1901 4 years Unknown TBA 33 Robert E. White (TBA–TBA) 1901 1905 4 years Unknown TBA 34 William D. Shelley (TBA–TBA) 1905 1907 2 years Unknown TBA 35 Frank M. Maddox (TBA–TBA) 1907 1909 2 years Unknown TB\n\n[DOC 9] st and composer (b. 1892 ) April 4 – Stefan Wolpe , German-born American composer (b. 1902 ) April 4 – Gil Hodges , American baseball player (b. 1924 ) April 5 – Isabel Jewell , American actress (b. 1907 ) April 6 Brian Donlevy , American actor (b. 1901 ) Heinrich Lübke , 2nd President of the Federal Republic of Germany (b. 1894 ) April 7 Abeid Karume , 1st President of Zanzibar (b. 1905 ) August Zaleski , 6th President of Poland (b. 1883 ) April 9 – James F. Byrnes , United States Secretary of State and Justice of the Supreme Court (b. 1882 ) April 10 – Henry de La Falaise , French film director, Croix de guerre recipient (b. 1898 ) April 16 – Yasunari Kawabata , Japanese novelist (b. 1899 ) April 20 – Andrea Andreen , Swedish physician (b. 1888 ) April 24\n\n[DOC 10] ing states and their standard time zones\n\n[DOC 11] (with most diaspora Protestants celebrating both days). [ citation needed ] Most Western Christian churches , most Eastern Catholic churches and civil calendars; also the Assyrian Church of the East . Gregorian calendar December 25 December 25 The Assyrian Church of the East adopted the Gregorian calendar in 1964. Economy Christmas is typically a peak selling season for retailers in many nations around the world; sales increase dramatically during this time as people purchase gifts, decorations, and supplies to celebrate. In the United States, the \"Christmas shopping season\" starts as early as October. In Canada, merchants begin advertising campaigns before Halloween (October 31) and step up their marketing following Remembrance Day on November 11. In the U\n\n[DOC 12] ng blue moon dates (timeanddate. com). Blue moon calculator (obliquity. com)\n\n[DOC 13] House November 26, 1783 August 19, 1784 8 months and 24 days Trenton, New Jersey French Arms Tavern November 1, 1784 December 24, 1784 1 month and 23 days New York, New York Federal Hall January 11, 1785 October 6, 1788 3 years, 11 months and 5 days Fraunces Tavern , Walter Livingston House October 6, 1788 March 3, 1789 4 months and 25 days United States Congress New York, New York Federal Hall March 4, 1789 December 5, 1790 1 year, 9 months and 1 day Philadelphia, Pennsylvania Congress Hall December 6, 1790 May 14, 1800 9 years, 5 months and 8 days Washington, D. C. United States Capitol November 17, 1800 August 24, 1814 13 years, 9 months and 7 days Blodgett's Hotel September 19, 1814 December 7, 1815 1 year, 2 months and 18 days Old Brick Capitol December\n\n[DOC 14] Establishments and disestablishments categories Establishments Disestablishments Works category Works Introductions v t e 1992 in various calendars Gregorian calendar 1992 MCMXCII Ab urbe condita 2745 Armenian calendar 1441 ԹՎ ՌՆԽԱ Assyrian calendar 6742 Baháʼí calendar 148–149 Balinese saka calendar 1913–1914 Bengali calendar 1398–1399 Berber calendar 2942 British Regnal year 40 Eliz. 2 – 41 Eliz. 2 Buddhist calendar 2536 Burmese calendar 1354 Byzantine calendar 7500–7501 Chinese calendar 辛未 年 (Metal Goat ) 4689 or 4482 — to — 壬申年 (Water Monkey ) 4690 or 4483 Coptic calendar 1708–1709 Discordian calendar 3158 Ethiopian calendar 1984–1985 Hebrew calendar 5752–5753 Hindu calendars - Vikram Samvat 2048–2049 - Shaka Samvat 1913–1914 - Kali Yuga 5092–5093 Holoce\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents identify the Basibasy mine's product as titanium and state that titanium was first discovered in 1791 by William Gregor. However, none of the provided documents contain William Gregor's birthdate, which is needed to determine the corresponding major US holiday. Therefore, the necessary fact is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 3016, + "osl": 1300, + "total_tokens": 4316, + "latency_ms": 5464.47, + "tokens_per_second": 237.9 + }, + "context": { + "kept_docs_count": 14, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "c3b5021a-45eb-4e9d-af31-0d91048c7164", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T02:45:55.009137Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: The Basibasy mine is located in Madagascar. This mine is abundant in a specific chemical element that was discovered for the first time in 1791. The person who discovered this element was born on what is now known as a major US holiday - what holiday is this?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Mine in Basibasy, Atsimo-Andrefana, Madagascar Basibasy mine Basibasy mine Location Location Basibasy Atsimo-Andrefana Country Madagascar Coordinates 22°9′S 43°36′E  /  22.150°S 43.600°E  / -22.150; 43.600 Production Products Titanium The Basibasy mine is one of the largest titanium mines in Madagascar . The mine is located in Basibasy , Atsimo-Andrefana . The mine has reserves amounting to 446 million tonnes of ore grading 5.5% titanium . References ^ a b c This article about a specific mine is a stub . You can help Wikipedia by adding missing information . v t e This Atsimo-Andrefana location article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 2] tadtium Roentgenium Copernicium Nihonium Flerovium Moscovium Livermorium Tennessine Oganesson 32 columns Hydrogen Helium Lithium Beryllium Boron Carbon Nitrogen Oxygen Fluorine Neon Sodium Magnesium Aluminium Silicon Phosphorus Sulfur Chlorine Argon Potassium Calcium Scandium Titanium Vanadium Chromium Manganese Iron Cobalt Nickel Copper Zinc Gallium Germanium Arsenic Selenium Bromine Krypton Rubidium Strontium Yttrium Zirconium Niobium Molybdenum Technetium Ruthenium Rhodium Palladium Silver Cadmium Indium Tin Antimony Tellurium Iodine Xenon Caesium Barium Lutetium Hafnium Tantalum Tungsten Rhenium Osmium Iridium Platinum Gold Mercury (element) Thallium Lead Bismuth Polonium Astatine Radon Francium Radium Lawrencium Rutherfordium Dubnium Seaborgium Bohrium\n\n[DOC 3] the lead mineral mimetite and the nickel mineral niccolite , and others. But he is best known for one of his earliest discoveries: in 1791, while analysing the minerals in a black sand he had discovered in the Manaccan valley, he isolated the calx of an unknown metal which he named manaccanite . Later in 1791, Martin Heinrich Klaproth discovered what is now known as the transition metal , titanium in the mineral rutile . Believing this to be a new discovery, Klaproth named it titanium after the Titans of Greek Mythology , but eventually it was clarified that Gregor made the discovery first. Gregor was credited with the discovery, but the element kept the name chosen by Klaproth. Gregor later found titanium in corundum from Tibet , and in a tourmaline from a\n\n[DOC 4] Scottish-American naturalist (1838–1914) John Muir Portrait by Carleton E. Watkins c. 1875 Born ( 1838-04-21 ) April 21, 1838 Dunbar , Scotland Died December 24, 1914 (1914-12-24) (aged 76) Los Angeles, California, U. S. Alma mater University of Wisconsin–Madison Occupations Farmer inventor naturalist philosopher writer botanist zoologist geologist environmentalist Spouse Louisa Strentzel ( m. 1880; died 1905) Children 2 Signature John Muir ( / m jʊər / MURE ; April 21, 1838 – December 24, 1914), also known as \"John of the Mountains\" and \"Father of the National Parks \", was a Scottish-born American naturalist , author, environmental philosopher , botanist , zoologist , glaciologist , and early advocate for the preservation of wilderness in the United States.\n\n[DOC 5] ian-American actor (born 1882) 1957 – Irving Langmuir , American chemist and physicist, Nobel Prize laureate (born 1881) 1958 – Jacob M. Lomakin , Soviet Consul General in New York City, journalist and economist (born 1904) 1959 – William Halsey, Jr. , American admiral (born 1882) 1959 – Wanda Landowska , Polish-French harpsichord player (born 1879) 1961 – Abdul Haq , Pakistani linguist and scholar (born 1870) 1963 – Joan Eardley , British artist (born 1921) 1971 – Spyros Skouras , Greek-American businessman (born 1893) 1972 – Pierre Brasseur , French actor and screenwriter (born 1905) 1973 – Selman Waksman , Ukrainian-American biochemist and microbiologist, Nobel Prize laureate (born 1888) 1977 – Elvis Presley , American singer and actor (born 1935) 1978 –\n\n[DOC 6] 40–1913) 1872 1877 5 years Democratic TBA 24 Jacob Carl DeGress (TBA–TBA) 1877 1879 2 years Unknown TBA 25 William A. Saylor (TBA–TBA) 1st time 1879 1881 2 years Unknown TBA 26 L. M. Crooker (TBA–TBA) 1881 1883 2 years Unknown TBA 27 William A. Saylor (TBA–TBA) 2nd time 1883 1884 1 year Unknown TBA 28 John W. Robertson (TBA–TBA) 1884 1887 3 years Unknown TBA 29 Joseph Nalle (TBA–TBA) 1887 1890 3 years Unknown TBA 30 John McDonald (TBA–TBA) 1890 1895 5 years Unknown TBA 31 Louis Hancock (TBA–TBA) 1896 1897 1 year Unknown TBA 32 John Dodd McCall (TBA–TBA) 1897 1901 4 years Unknown TBA 33 Robert E. White (TBA–TBA) 1901 1905 4 years Unknown TBA 34 William D. Shelley (TBA–TBA) 1905 1907 2 years Unknown TBA 35 Frank M. Maddox (TBA–TBA) 1907 1909 2 years Unknown TB\n\n[DOC 7] st and composer (b. 1892 ) April 4 – Stefan Wolpe , German-born American composer (b. 1902 ) April 4 – Gil Hodges , American baseball player (b. 1924 ) April 5 – Isabel Jewell , American actress (b. 1907 ) April 6 Brian Donlevy , American actor (b. 1901 ) Heinrich Lübke , 2nd President of the Federal Republic of Germany (b. 1894 ) April 7 Abeid Karume , 1st President of Zanzibar (b. 1905 ) August Zaleski , 6th President of Poland (b. 1883 ) April 9 – James F. Byrnes , United States Secretary of State and Justice of the Supreme Court (b. 1882 ) April 10 – Henry de La Falaise , French film director, Croix de guerre recipient (b. 1898 ) April 16 – Yasunari Kawabata , Japanese novelist (b. 1899 ) April 20 – Andrea Andreen , Swedish physician (b. 1888 ) April 24\n\n[DOC 8] ing states and their standard time zones\n\n[DOC 9] (with most diaspora Protestants celebrating both days). [ citation needed ] Most Western Christian churches , most Eastern Catholic churches and civil calendars; also the Assyrian Church of the East . Gregorian calendar December 25 December 25 The Assyrian Church of the East adopted the Gregorian calendar in 1964. Economy Christmas is typically a peak selling season for retailers in many nations around the world; sales increase dramatically during this time as people purchase gifts, decorations, and supplies to celebrate. In the United States, the \"Christmas shopping season\" starts as early as October. In Canada, merchants begin advertising campaigns before Halloween (October 31) and step up their marketing following Remembrance Day on November 11. In the U\n\n[DOC 10] ng blue moon dates (timeanddate. com). Blue moon calculator (obliquity. com)\n\n[DOC 11] House November 26, 1783 August 19, 1784 8 months and 24 days Trenton, New Jersey French Arms Tavern November 1, 1784 December 24, 1784 1 month and 23 days New York, New York Federal Hall January 11, 1785 October 6, 1788 3 years, 11 months and 5 days Fraunces Tavern , Walter Livingston House October 6, 1788 March 3, 1789 4 months and 25 days United States Congress New York, New York Federal Hall March 4, 1789 December 5, 1790 1 year, 9 months and 1 day Philadelphia, Pennsylvania Congress Hall December 6, 1790 May 14, 1800 9 years, 5 months and 8 days Washington, D. C. United States Capitol November 17, 1800 August 24, 1814 13 years, 9 months and 7 days Blodgett's Hotel September 19, 1814 December 7, 1815 1 year, 2 months and 18 days Old Brick Capitol December\n\n[DOC 12] Establishments and disestablishments categories Establishments Disestablishments Works category Works Introductions v t e 1992 in various calendars Gregorian calendar 1992 MCMXCII Ab urbe condita 2745 Armenian calendar 1441 ԹՎ ՌՆԽԱ Assyrian calendar 6742 Baháʼí calendar 148–149 Balinese saka calendar 1913–1914 Bengali calendar 1398–1399 Berber calendar 2942 British Regnal year 40 Eliz. 2 – 41 Eliz. 2 Buddhist calendar 2536 Burmese calendar 1354 Byzantine calendar 7500–7501 Chinese calendar 辛未 年 (Metal Goat ) 4689 or 4482 — to — 壬申年 (Water Monkey ) 4690 or 4483 Coptic calendar 1708–1709 Discordian calendar 3158 Ethiopian calendar 1984–1985 Hebrew calendar 5752–5753 Hindu calendars - Vikram Samvat 2048–2049 - Shaka Samvat 1913–1914 - Kali Yuga 5092–5093 Holoce\n\n\nSEARCH HISTORY: - Query: '1791 discovered element' → 2 docs\n- Query: 'Basibasy mine Madagascar element' → 4 docs\n- Query: 'William Gregor birthday' → 5 docs\n- Query: 'Titanium discovery 1791' → 1 docs\n- Query: 'William Gregor birth date' → 4 docs\n- Query: 'US federal holidays dates' → 5 docs\n\nFEEDBACK: First query will identify which chemical element was first isolated in 1791. Second query links the Basibasy mine to the specific element it contains. Third query retrieves the discoverer's birth date to determine the US holiday.\nThe first two queries retrieve the element (titanium) and the discoverer's birth date. The third query obtains a list of official U.S. holidays with their calendar dates, allowing us to match the discoverer's birthday to the corresponding holiday.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"William Gregor birthdate\",\n \"Martin Heinrich Klaproth birthdate\",\n \"US holiday July 4\"\n ],\n \"feedback\": \"We need the exact birth date of the person who first isolated titanium in 1791 (either William Gregor or Martin Heinrich Klaproth) and then match that date to a major U.S. holiday. The first two queries retrieve the discoverer’s birth date; the third query confirms that July 4 is Independence Day, the major U.S. holiday that may correspond to the discoverer’s birthday.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2936, + "osl": 1857, + "total_tokens": 4793, + "latency_ms": 25018.62, + "tokens_per_second": 74.22 + }, + "context": { + "kept_docs_count": 12, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "0fd153fc-667b-4815-9477-5efd4a741ce6", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T02:46:20.177257Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: The Basibasy mine is located in Madagascar. This mine is abundant in a specific chemical element that was discovered for the first time in 1791. The person who discovered this element was born on what is now known as a major US holiday - what holiday is this?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Day: 11 June 1955\n\n[NEW 2] citation needed ] September 30, 2002 [ citation needed ] So Hot Summer May 27, 2005 September 4, 2006 Hauntoberfest October 1, 2005 October 31, 2006 Hail to the Holidays December 1, 2005 December 25, 2006 Disney Channel Summer May 25, 2007 September 5, 2016 Disney Channel Halloween October 1, 2007 October 31, 2007 Disney Channel Christmas December 1, 2007 December 25, 2007 Totally Rocking Summer May 24, 2008 September 1, 2008 Wiztober May 1, 2008 October 31, 2009 Santacember December 1, 2008 December 25, 2009 It's On May 29, 2010 September 6, 2010 October Takeover October 4, 2010 October 31, 2010 December Center Event December 1, 2010 December 25, 2010 Monstober October 1, 2011 October 31, 2024 TOONin' Saturdays June 18, 2011 September 29, 2012 Sizzlin Summe\n\n[NEW 3] events throughout July and August (ranging from a Bon Odori celebration to the Seafair Cup hydroplane races), the Bite of Seattle , one of the largest Gay Pride festivals in the United States, and the art and music festival Bumbershoot , which programs music as well as other art and entertainment over the Labor Day weekend. All are typically attended by 100,000 people annually, as are the Seattle Hempfest and two separate Independence Day celebrations. Other significant events include numerous Native American pow-wows , a Greek Festival hosted by St. Demetrios Greek Orthodox Church in Montlake , and numerous ethnic festivals (many associated with Festál at Seattle Center ). There are other annual events, ranging from the Seattle Antiquarian Book Fair & Book\n\n[NEW 4] und Midnight Bob Hoskins George Mona Lisa William Hurt James Leeds Children of a Lesser God James Woods Richard Boyle Salvador 1987 (60th) Michael Douglas ‡ Gordon Gekko Wall Street William Hurt Tom Grunick Broadcast News Marcello Mastroianni Romano Patroni Dark Eyes Jack Nicholson Francis Phelan Ironweed Robin Williams Adrian Cronauer Good Morning, Vietnam 1988 (61st) Dustin Hoffman ‡ Raymond Babbitt Rain Man Gene Hackman Rupert Anderson Mississippi Burning Tom Hanks Josh Baskin Big Edward James Olmos Jaime Escalante Stand and Deliver Max von Sydow Lassefar \"Lasse\" Karlsson Pelle the Conqueror 1989 (62nd) Daniel Day-Lewis ‡ Christy Brown My Left Foot Kenneth Branagh King Henry V Henry V Tom Cruise Ron Kovic Born on the Fourth of July Morgan Freeman Hoke Col\n\n\nKEPT DOCUMENTS (context only):\n[14 documents already kept as relevant]\n[KEPT 1] ember 1789 he announced his discovery to the Royal Prussian Academy of sciences in Berlin. He also discovered zirconium in 1789, separating it in the form of its \"earth\" zirconia, oxide ZrO 2 . Klaproth analyzed a brightly-colored form of the mineral called \"hyacinth\" from Ceylon. He gave the new el...\n[KEPT 2] Gregor (1791) First isolation Jöns Jakob Berzelius (1825) Named by Martin Heinrich Klaproth (1795) Isotopes of titanium v e Main isotopes Decay Isotope abun­dance half-life ( t 1/2 ) mode pro­duct 44 Ti synth 59.1 y ε 44 Sc 45 Ti synth 3.08 h β + 45 Sc 46 Ti 8.25% stable 47 Ti 7.44% stable 48 Ti 73....\n[KEPT 3] Mine in Basibasy, Atsimo-Andrefana, Madagascar Basibasy mine Basibasy mine Location Location Basibasy Atsimo-Andrefana Country Madagascar Coordinates 22°9′S 43°36′E  /  22.150°S 43.600°E  / -22.150; 43.600 Production Products Titanium The Basibasy mine is one of the largest titanium mines in Mada...\n[KEPT 4] tadtium Roentgenium Copernicium Nihonium Flerovium Moscovium Livermorium Tennessine Oganesson 32 columns Hydrogen Helium Lithium Beryllium Boron Carbon Nitrogen Oxygen Fluorine Neon Sodium Magnesium Aluminium Silicon Phosphorus Sulfur Chlorine Argon Potassium Calcium Scandium Titanium Vanadium Chrom...\n[KEPT 5] the lead mineral mimetite and the nickel mineral niccolite , and others. But he is best known for one of his earliest discoveries: in 1791, while analysing the minerals in a black sand he had discovered in the Manaccan valley, he isolated the calx of an unknown metal which he named manaccanite . Lat...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 4 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1372, + "osl": 2188, + "total_tokens": 3560, + "latency_ms": 24579.8, + "tokens_per_second": 89.02 + }, + "context": { + "num_documents_evaluated": 4, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 14 + } + }, + { + "call_id": "fa7b0dd8-3e65-441c-be28-df4c761b48cc", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T02:46:23.749717Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: The Basibasy mine is located in Madagascar. This mine is abundant in a specific chemical element that was discovered for the first time in 1791. The person who discovered this element was born on what is now known as a major US holiday - what holiday is this?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] ember 1789 he announced his discovery to the Royal Prussian Academy of sciences in Berlin. He also discovered zirconium in 1789, separating it in the form of its \"earth\" zirconia, oxide ZrO 2 . Klaproth analyzed a brightly-colored form of the mineral called \"hyacinth\" from Ceylon. He gave the new element the name zirconium based on its Persian name \"zargun\", gold-colored. Klaproth characterised uranium and zirconium as distinct elements , though he was unable to isolate them. Klaproth independently discovered cerium (1803), a rare earth element , around the same time as Jöns Jacob Berzelius and Wilhelm Hisinger , in the winter of 1803. William Gregor of Cornwall was the first to identify the element titanium in 1791, correctly concluding that he had found a\n\n[DOC 2] Gregor (1791) First isolation Jöns Jakob Berzelius (1825) Named by Martin Heinrich Klaproth (1795) Isotopes of titanium v e Main isotopes Decay Isotope abun­dance half-life ( t 1/2 ) mode pro­duct 44 Ti synth 59.1 y ε 44 Sc 45 Ti synth 3.08 h β + 45 Sc 46 Ti 8.25% stable 47 Ti 7.44% stable 48 Ti 73.7% stable 49 Ti 5.41% stable 50 Ti 5.18% stable Category: Titanium view talk edit | references Titanium is a chemical element ; it has symbol Ti and atomic number 22. Found in nature only as an oxide , it can be reduced to produce a lustrous transition metal with a silver color , low density , and high strength that is resistant to corrosion in sea water , aqua regia , and chlorine . Titanium was discovered in Cornwall , Great Britain , by William Gregor in 1791 a\n\n[DOC 3] Mine in Basibasy, Atsimo-Andrefana, Madagascar Basibasy mine Basibasy mine Location Location Basibasy Atsimo-Andrefana Country Madagascar Coordinates 22°9′S 43°36′E  /  22.150°S 43.600°E  / -22.150; 43.600 Production Products Titanium The Basibasy mine is one of the largest titanium mines in Madagascar . The mine is located in Basibasy , Atsimo-Andrefana . The mine has reserves amounting to 446 million tonnes of ore grading 5.5% titanium . References ^ a b c This article about a specific mine is a stub . You can help Wikipedia by adding missing information . v t e This Atsimo-Andrefana location article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 4] tadtium Roentgenium Copernicium Nihonium Flerovium Moscovium Livermorium Tennessine Oganesson 32 columns Hydrogen Helium Lithium Beryllium Boron Carbon Nitrogen Oxygen Fluorine Neon Sodium Magnesium Aluminium Silicon Phosphorus Sulfur Chlorine Argon Potassium Calcium Scandium Titanium Vanadium Chromium Manganese Iron Cobalt Nickel Copper Zinc Gallium Germanium Arsenic Selenium Bromine Krypton Rubidium Strontium Yttrium Zirconium Niobium Molybdenum Technetium Ruthenium Rhodium Palladium Silver Cadmium Indium Tin Antimony Tellurium Iodine Xenon Caesium Barium Lutetium Hafnium Tantalum Tungsten Rhenium Osmium Iridium Platinum Gold Mercury (element) Thallium Lead Bismuth Polonium Astatine Radon Francium Radium Lawrencium Rutherfordium Dubnium Seaborgium Bohrium\n\n[DOC 5] the lead mineral mimetite and the nickel mineral niccolite , and others. But he is best known for one of his earliest discoveries: in 1791, while analysing the minerals in a black sand he had discovered in the Manaccan valley, he isolated the calx of an unknown metal which he named manaccanite . Later in 1791, Martin Heinrich Klaproth discovered what is now known as the transition metal , titanium in the mineral rutile . Believing this to be a new discovery, Klaproth named it titanium after the Titans of Greek Mythology , but eventually it was clarified that Gregor made the discovery first. Gregor was credited with the discovery, but the element kept the name chosen by Klaproth. Gregor later found titanium in corundum from Tibet , and in a tourmaline from a\n\n[DOC 6] Scottish-American naturalist (1838–1914) John Muir Portrait by Carleton E. Watkins c. 1875 Born ( 1838-04-21 ) April 21, 1838 Dunbar , Scotland Died December 24, 1914 (1914-12-24) (aged 76) Los Angeles, California, U. S. Alma mater University of Wisconsin–Madison Occupations Farmer inventor naturalist philosopher writer botanist zoologist geologist environmentalist Spouse Louisa Strentzel ( m. 1880; died 1905) Children 2 Signature John Muir ( / m jʊər / MURE ; April 21, 1838 – December 24, 1914), also known as \"John of the Mountains\" and \"Father of the National Parks \", was a Scottish-born American naturalist , author, environmental philosopher , botanist , zoologist , glaciologist , and early advocate for the preservation of wilderness in the United States.\n\n[DOC 7] ian-American actor (born 1882) 1957 – Irving Langmuir , American chemist and physicist, Nobel Prize laureate (born 1881) 1958 – Jacob M. Lomakin , Soviet Consul General in New York City, journalist and economist (born 1904) 1959 – William Halsey, Jr. , American admiral (born 1882) 1959 – Wanda Landowska , Polish-French harpsichord player (born 1879) 1961 – Abdul Haq , Pakistani linguist and scholar (born 1870) 1963 – Joan Eardley , British artist (born 1921) 1971 – Spyros Skouras , Greek-American businessman (born 1893) 1972 – Pierre Brasseur , French actor and screenwriter (born 1905) 1973 – Selman Waksman , Ukrainian-American biochemist and microbiologist, Nobel Prize laureate (born 1888) 1977 – Elvis Presley , American singer and actor (born 1935) 1978 –\n\n[DOC 8] 40–1913) 1872 1877 5 years Democratic TBA 24 Jacob Carl DeGress (TBA–TBA) 1877 1879 2 years Unknown TBA 25 William A. Saylor (TBA–TBA) 1st time 1879 1881 2 years Unknown TBA 26 L. M. Crooker (TBA–TBA) 1881 1883 2 years Unknown TBA 27 William A. Saylor (TBA–TBA) 2nd time 1883 1884 1 year Unknown TBA 28 John W. Robertson (TBA–TBA) 1884 1887 3 years Unknown TBA 29 Joseph Nalle (TBA–TBA) 1887 1890 3 years Unknown TBA 30 John McDonald (TBA–TBA) 1890 1895 5 years Unknown TBA 31 Louis Hancock (TBA–TBA) 1896 1897 1 year Unknown TBA 32 John Dodd McCall (TBA–TBA) 1897 1901 4 years Unknown TBA 33 Robert E. White (TBA–TBA) 1901 1905 4 years Unknown TBA 34 William D. Shelley (TBA–TBA) 1905 1907 2 years Unknown TBA 35 Frank M. Maddox (TBA–TBA) 1907 1909 2 years Unknown TB\n\n[DOC 9] st and composer (b. 1892 ) April 4 – Stefan Wolpe , German-born American composer (b. 1902 ) April 4 – Gil Hodges , American baseball player (b. 1924 ) April 5 – Isabel Jewell , American actress (b. 1907 ) April 6 Brian Donlevy , American actor (b. 1901 ) Heinrich Lübke , 2nd President of the Federal Republic of Germany (b. 1894 ) April 7 Abeid Karume , 1st President of Zanzibar (b. 1905 ) August Zaleski , 6th President of Poland (b. 1883 ) April 9 – James F. Byrnes , United States Secretary of State and Justice of the Supreme Court (b. 1882 ) April 10 – Henry de La Falaise , French film director, Croix de guerre recipient (b. 1898 ) April 16 – Yasunari Kawabata , Japanese novelist (b. 1899 ) April 20 – Andrea Andreen , Swedish physician (b. 1888 ) April 24\n\n[DOC 10] ing states and their standard time zones\n\n[DOC 11] (with most diaspora Protestants celebrating both days). [ citation needed ] Most Western Christian churches , most Eastern Catholic churches and civil calendars; also the Assyrian Church of the East . Gregorian calendar December 25 December 25 The Assyrian Church of the East adopted the Gregorian calendar in 1964. Economy Christmas is typically a peak selling season for retailers in many nations around the world; sales increase dramatically during this time as people purchase gifts, decorations, and supplies to celebrate. In the United States, the \"Christmas shopping season\" starts as early as October. In Canada, merchants begin advertising campaigns before Halloween (October 31) and step up their marketing following Remembrance Day on November 11. In the U\n\n[DOC 12] ng blue moon dates (timeanddate. com). Blue moon calculator (obliquity. com)\n\n[DOC 13] House November 26, 1783 August 19, 1784 8 months and 24 days Trenton, New Jersey French Arms Tavern November 1, 1784 December 24, 1784 1 month and 23 days New York, New York Federal Hall January 11, 1785 October 6, 1788 3 years, 11 months and 5 days Fraunces Tavern , Walter Livingston House October 6, 1788 March 3, 1789 4 months and 25 days United States Congress New York, New York Federal Hall March 4, 1789 December 5, 1790 1 year, 9 months and 1 day Philadelphia, Pennsylvania Congress Hall December 6, 1790 May 14, 1800 9 years, 5 months and 8 days Washington, D. C. United States Capitol November 17, 1800 August 24, 1814 13 years, 9 months and 7 days Blodgett's Hotel September 19, 1814 December 7, 1815 1 year, 2 months and 18 days Old Brick Capitol December\n\n[DOC 14] Establishments and disestablishments categories Establishments Disestablishments Works category Works Introductions v t e 1992 in various calendars Gregorian calendar 1992 MCMXCII Ab urbe condita 2745 Armenian calendar 1441 ԹՎ ՌՆԽԱ Assyrian calendar 6742 Baháʼí calendar 148–149 Balinese saka calendar 1913–1914 Bengali calendar 1398–1399 Berber calendar 2942 British Regnal year 40 Eliz. 2 – 41 Eliz. 2 Buddhist calendar 2536 Burmese calendar 1354 Byzantine calendar 7500–7501 Chinese calendar 辛未 年 (Metal Goat ) 4689 or 4482 — to — 壬申年 (Water Monkey ) 4690 or 4483 Coptic calendar 1708–1709 Discordian calendar 3158 Ethiopian calendar 1984–1985 Hebrew calendar 5752–5753 Hindu calendars - Vikram Samvat 2048–2049 - Shaka Samvat 1913–1914 - Kali Yuga 5092–5093 Holoce\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Documents provide the Basibasy mine (Titanium) and that the element (titanium) was first discovered in 1791 by William Gregor, but none of the kept documents include William Gregor's birth date, which is needed to identify the US holiday corresponding to his birthday.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 3016, + "osl": 824, + "total_tokens": 3840, + "latency_ms": 3571.53, + "tokens_per_second": 230.71 + }, + "context": { + "kept_docs_count": 14, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "8f02ee47-6bb4-4e41-9cce-a547aa9ed5eb", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T02:46:36.658505Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: The Basibasy mine is located in Madagascar. This mine is abundant in a specific chemical element that was discovered for the first time in 1791. The person who discovered this element was born on what is now known as a major US holiday - what holiday is this?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Mine in Basibasy, Atsimo-Andrefana, Madagascar Basibasy mine Basibasy mine Location Location Basibasy Atsimo-Andrefana Country Madagascar Coordinates 22°9′S 43°36′E  /  22.150°S 43.600°E  / -22.150; 43.600 Production Products Titanium The Basibasy mine is one of the largest titanium mines in Madagascar . The mine is located in Basibasy , Atsimo-Andrefana . The mine has reserves amounting to 446 million tonnes of ore grading 5.5% titanium . References ^ a b c This article about a specific mine is a stub . You can help Wikipedia by adding missing information . v t e This Atsimo-Andrefana location article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 2] tadtium Roentgenium Copernicium Nihonium Flerovium Moscovium Livermorium Tennessine Oganesson 32 columns Hydrogen Helium Lithium Beryllium Boron Carbon Nitrogen Oxygen Fluorine Neon Sodium Magnesium Aluminium Silicon Phosphorus Sulfur Chlorine Argon Potassium Calcium Scandium Titanium Vanadium Chromium Manganese Iron Cobalt Nickel Copper Zinc Gallium Germanium Arsenic Selenium Bromine Krypton Rubidium Strontium Yttrium Zirconium Niobium Molybdenum Technetium Ruthenium Rhodium Palladium Silver Cadmium Indium Tin Antimony Tellurium Iodine Xenon Caesium Barium Lutetium Hafnium Tantalum Tungsten Rhenium Osmium Iridium Platinum Gold Mercury (element) Thallium Lead Bismuth Polonium Astatine Radon Francium Radium Lawrencium Rutherfordium Dubnium Seaborgium Bohrium\n\n[DOC 3] the lead mineral mimetite and the nickel mineral niccolite , and others. But he is best known for one of his earliest discoveries: in 1791, while analysing the minerals in a black sand he had discovered in the Manaccan valley, he isolated the calx of an unknown metal which he named manaccanite . Later in 1791, Martin Heinrich Klaproth discovered what is now known as the transition metal , titanium in the mineral rutile . Believing this to be a new discovery, Klaproth named it titanium after the Titans of Greek Mythology , but eventually it was clarified that Gregor made the discovery first. Gregor was credited with the discovery, but the element kept the name chosen by Klaproth. Gregor later found titanium in corundum from Tibet , and in a tourmaline from a\n\n[DOC 4] Scottish-American naturalist (1838–1914) John Muir Portrait by Carleton E. Watkins c. 1875 Born ( 1838-04-21 ) April 21, 1838 Dunbar , Scotland Died December 24, 1914 (1914-12-24) (aged 76) Los Angeles, California, U. S. Alma mater University of Wisconsin–Madison Occupations Farmer inventor naturalist philosopher writer botanist zoologist geologist environmentalist Spouse Louisa Strentzel ( m. 1880; died 1905) Children 2 Signature John Muir ( / m jʊər / MURE ; April 21, 1838 – December 24, 1914), also known as \"John of the Mountains\" and \"Father of the National Parks \", was a Scottish-born American naturalist , author, environmental philosopher , botanist , zoologist , glaciologist , and early advocate for the preservation of wilderness in the United States.\n\n[DOC 5] ian-American actor (born 1882) 1957 – Irving Langmuir , American chemist and physicist, Nobel Prize laureate (born 1881) 1958 – Jacob M. Lomakin , Soviet Consul General in New York City, journalist and economist (born 1904) 1959 – William Halsey, Jr. , American admiral (born 1882) 1959 – Wanda Landowska , Polish-French harpsichord player (born 1879) 1961 – Abdul Haq , Pakistani linguist and scholar (born 1870) 1963 – Joan Eardley , British artist (born 1921) 1971 – Spyros Skouras , Greek-American businessman (born 1893) 1972 – Pierre Brasseur , French actor and screenwriter (born 1905) 1973 – Selman Waksman , Ukrainian-American biochemist and microbiologist, Nobel Prize laureate (born 1888) 1977 – Elvis Presley , American singer and actor (born 1935) 1978 –\n\n[DOC 6] 40–1913) 1872 1877 5 years Democratic TBA 24 Jacob Carl DeGress (TBA–TBA) 1877 1879 2 years Unknown TBA 25 William A. Saylor (TBA–TBA) 1st time 1879 1881 2 years Unknown TBA 26 L. M. Crooker (TBA–TBA) 1881 1883 2 years Unknown TBA 27 William A. Saylor (TBA–TBA) 2nd time 1883 1884 1 year Unknown TBA 28 John W. Robertson (TBA–TBA) 1884 1887 3 years Unknown TBA 29 Joseph Nalle (TBA–TBA) 1887 1890 3 years Unknown TBA 30 John McDonald (TBA–TBA) 1890 1895 5 years Unknown TBA 31 Louis Hancock (TBA–TBA) 1896 1897 1 year Unknown TBA 32 John Dodd McCall (TBA–TBA) 1897 1901 4 years Unknown TBA 33 Robert E. White (TBA–TBA) 1901 1905 4 years Unknown TBA 34 William D. Shelley (TBA–TBA) 1905 1907 2 years Unknown TBA 35 Frank M. Maddox (TBA–TBA) 1907 1909 2 years Unknown TB\n\n[DOC 7] st and composer (b. 1892 ) April 4 – Stefan Wolpe , German-born American composer (b. 1902 ) April 4 – Gil Hodges , American baseball player (b. 1924 ) April 5 – Isabel Jewell , American actress (b. 1907 ) April 6 Brian Donlevy , American actor (b. 1901 ) Heinrich Lübke , 2nd President of the Federal Republic of Germany (b. 1894 ) April 7 Abeid Karume , 1st President of Zanzibar (b. 1905 ) August Zaleski , 6th President of Poland (b. 1883 ) April 9 – James F. Byrnes , United States Secretary of State and Justice of the Supreme Court (b. 1882 ) April 10 – Henry de La Falaise , French film director, Croix de guerre recipient (b. 1898 ) April 16 – Yasunari Kawabata , Japanese novelist (b. 1899 ) April 20 – Andrea Andreen , Swedish physician (b. 1888 ) April 24\n\n[DOC 8] ing states and their standard time zones\n\n[DOC 9] (with most diaspora Protestants celebrating both days). [ citation needed ] Most Western Christian churches , most Eastern Catholic churches and civil calendars; also the Assyrian Church of the East . Gregorian calendar December 25 December 25 The Assyrian Church of the East adopted the Gregorian calendar in 1964. Economy Christmas is typically a peak selling season for retailers in many nations around the world; sales increase dramatically during this time as people purchase gifts, decorations, and supplies to celebrate. In the United States, the \"Christmas shopping season\" starts as early as October. In Canada, merchants begin advertising campaigns before Halloween (October 31) and step up their marketing following Remembrance Day on November 11. In the U\n\n[DOC 10] ng blue moon dates (timeanddate. com). Blue moon calculator (obliquity. com)\n\n[DOC 11] House November 26, 1783 August 19, 1784 8 months and 24 days Trenton, New Jersey French Arms Tavern November 1, 1784 December 24, 1784 1 month and 23 days New York, New York Federal Hall January 11, 1785 October 6, 1788 3 years, 11 months and 5 days Fraunces Tavern , Walter Livingston House October 6, 1788 March 3, 1789 4 months and 25 days United States Congress New York, New York Federal Hall March 4, 1789 December 5, 1790 1 year, 9 months and 1 day Philadelphia, Pennsylvania Congress Hall December 6, 1790 May 14, 1800 9 years, 5 months and 8 days Washington, D. C. United States Capitol November 17, 1800 August 24, 1814 13 years, 9 months and 7 days Blodgett's Hotel September 19, 1814 December 7, 1815 1 year, 2 months and 18 days Old Brick Capitol December\n\n[DOC 12] Establishments and disestablishments categories Establishments Disestablishments Works category Works Introductions v t e 1992 in various calendars Gregorian calendar 1992 MCMXCII Ab urbe condita 2745 Armenian calendar 1441 ԹՎ ՌՆԽԱ Assyrian calendar 6742 Baháʼí calendar 148–149 Balinese saka calendar 1913–1914 Bengali calendar 1398–1399 Berber calendar 2942 British Regnal year 40 Eliz. 2 – 41 Eliz. 2 Buddhist calendar 2536 Burmese calendar 1354 Byzantine calendar 7500–7501 Chinese calendar 辛未 年 (Metal Goat ) 4689 or 4482 — to — 壬申年 (Water Monkey ) 4690 or 4483 Coptic calendar 1708–1709 Discordian calendar 3158 Ethiopian calendar 1984–1985 Hebrew calendar 5752–5753 Hindu calendars - Vikram Samvat 2048–2049 - Shaka Samvat 1913–1914 - Kali Yuga 5092–5093 Holoce\n\n\nSEARCH HISTORY: - Query: '1791 discovered element' → 2 docs\n- Query: 'Basibasy mine Madagascar element' → 4 docs\n- Query: 'William Gregor birthday' → 5 docs\n- Query: 'Titanium discovery 1791' → 1 docs\n- Query: 'William Gregor birth date' → 4 docs\n- Query: 'US federal holidays dates' → 5 docs\n- Query: 'William Gregor birthdate' → 0 docs\n- Query: 'Martin Heinrich Klaproth birthdate' → 0 docs\n- Query: 'US holiday July 4' → 4 docs\n\nFEEDBACK: First query will identify which chemical element was first isolated in 1791. Second query links the Basibasy mine to the specific element it contains. Third query retrieves the discoverer's birth date to determine the US holiday.\nThe first two queries retrieve the element (titanium) and the discoverer's birth date. The third query obtains a list of official U.S. holidays with their calendar dates, allowing us to match the discoverer's birthday to the corresponding holiday.\nWe need the exact birth date of the person who first isolated titanium in 1791 (either William Gregor or Martin Heinrich Klaproth) and then match that date to a major U.S. holiday. The first two queries retrieve the discoverer’s birth date; the third query confirms that July 4 is Independence Day, the major U.S. holiday that may correspond to the discoverer’s birthday.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Titanium Basibasy mine Madagascar\",\n \"William Gregor biography\",\n \"United States federal holidays list\"\n ],\n \"feedback\": \"First query confirms the element (titanium). Second retrieves the discoverer's (William Gregor) birth date. Third provides US holiday dates to match the birth date to a major holiday.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 3063, + "osl": 78, + "total_tokens": 3141, + "latency_ms": 12908.09, + "tokens_per_second": 6.04 + }, + "context": { + "kept_docs_count": 12, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "70f126c3-598b-4be9-b2ab-f568be4b5284", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T02:47:15.809899Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: The Basibasy mine is located in Madagascar. This mine is abundant in a specific chemical element that was discovered for the first time in 1791. The person who discovered this element was born on what is now known as a major US holiday - what holiday is this?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] ember 1789 he announced his discovery to the Royal Prussian Academy of sciences in Berlin. He also discovered zirconium in 1789, separating it in the form of its \"earth\" zirconia, oxide ZrO 2 . Klaproth analyzed a brightly-colored form of the mineral called \"hyacinth\" from Ceylon. He gave the new element the name zirconium based on its Persian name \"zargun\", gold-colored. Klaproth characterised uranium and zirconium as distinct elements , though he was unable to isolate them. Klaproth independently discovered cerium (1803), a rare earth element , around the same time as Jöns Jacob Berzelius and Wilhelm Hisinger , in the winter of 1803. William Gregor of Cornwall was the first to identify the element titanium in 1791, correctly concluding that he had found a\n\n[DOC 2] Gregor (1791) First isolation Jöns Jakob Berzelius (1825) Named by Martin Heinrich Klaproth (1795) Isotopes of titanium v e Main isotopes Decay Isotope abun­dance half-life ( t 1/2 ) mode pro­duct 44 Ti synth 59.1 y ε 44 Sc 45 Ti synth 3.08 h β + 45 Sc 46 Ti 8.25% stable 47 Ti 7.44% stable 48 Ti 73.7% stable 49 Ti 5.41% stable 50 Ti 5.18% stable Category: Titanium view talk edit | references Titanium is a chemical element ; it has symbol Ti and atomic number 22. Found in nature only as an oxide , it can be reduced to produce a lustrous transition metal with a silver color , low density , and high strength that is resistant to corrosion in sea water , aqua regia , and chlorine . Titanium was discovered in Cornwall , Great Britain , by William Gregor in 1791 a\n\n[DOC 3] Mine in Basibasy, Atsimo-Andrefana, Madagascar Basibasy mine Basibasy mine Location Location Basibasy Atsimo-Andrefana Country Madagascar Coordinates 22°9′S 43°36′E  /  22.150°S 43.600°E  / -22.150; 43.600 Production Products Titanium The Basibasy mine is one of the largest titanium mines in Madagascar . The mine is located in Basibasy , Atsimo-Andrefana . The mine has reserves amounting to 446 million tonnes of ore grading 5.5% titanium . References ^ a b c This article about a specific mine is a stub . You can help Wikipedia by adding missing information . v t e This Atsimo-Andrefana location article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 4] tadtium Roentgenium Copernicium Nihonium Flerovium Moscovium Livermorium Tennessine Oganesson 32 columns Hydrogen Helium Lithium Beryllium Boron Carbon Nitrogen Oxygen Fluorine Neon Sodium Magnesium Aluminium Silicon Phosphorus Sulfur Chlorine Argon Potassium Calcium Scandium Titanium Vanadium Chromium Manganese Iron Cobalt Nickel Copper Zinc Gallium Germanium Arsenic Selenium Bromine Krypton Rubidium Strontium Yttrium Zirconium Niobium Molybdenum Technetium Ruthenium Rhodium Palladium Silver Cadmium Indium Tin Antimony Tellurium Iodine Xenon Caesium Barium Lutetium Hafnium Tantalum Tungsten Rhenium Osmium Iridium Platinum Gold Mercury (element) Thallium Lead Bismuth Polonium Astatine Radon Francium Radium Lawrencium Rutherfordium Dubnium Seaborgium Bohrium\n\n[DOC 5] the lead mineral mimetite and the nickel mineral niccolite , and others. But he is best known for one of his earliest discoveries: in 1791, while analysing the minerals in a black sand he had discovered in the Manaccan valley, he isolated the calx of an unknown metal which he named manaccanite . Later in 1791, Martin Heinrich Klaproth discovered what is now known as the transition metal , titanium in the mineral rutile . Believing this to be a new discovery, Klaproth named it titanium after the Titans of Greek Mythology , but eventually it was clarified that Gregor made the discovery first. Gregor was credited with the discovery, but the element kept the name chosen by Klaproth. Gregor later found titanium in corundum from Tibet , and in a tourmaline from a\n\n[DOC 6] Scottish-American naturalist (1838–1914) John Muir Portrait by Carleton E. Watkins c. 1875 Born ( 1838-04-21 ) April 21, 1838 Dunbar , Scotland Died December 24, 1914 (1914-12-24) (aged 76) Los Angeles, California, U. S. Alma mater University of Wisconsin–Madison Occupations Farmer inventor naturalist philosopher writer botanist zoologist geologist environmentalist Spouse Louisa Strentzel ( m. 1880; died 1905) Children 2 Signature John Muir ( / m jʊər / MURE ; April 21, 1838 – December 24, 1914), also known as \"John of the Mountains\" and \"Father of the National Parks \", was a Scottish-born American naturalist , author, environmental philosopher , botanist , zoologist , glaciologist , and early advocate for the preservation of wilderness in the United States.\n\n[DOC 7] ian-American actor (born 1882) 1957 – Irving Langmuir , American chemist and physicist, Nobel Prize laureate (born 1881) 1958 – Jacob M. Lomakin , Soviet Consul General in New York City, journalist and economist (born 1904) 1959 – William Halsey, Jr. , American admiral (born 1882) 1959 – Wanda Landowska , Polish-French harpsichord player (born 1879) 1961 – Abdul Haq , Pakistani linguist and scholar (born 1870) 1963 – Joan Eardley , British artist (born 1921) 1971 – Spyros Skouras , Greek-American businessman (born 1893) 1972 – Pierre Brasseur , French actor and screenwriter (born 1905) 1973 – Selman Waksman , Ukrainian-American biochemist and microbiologist, Nobel Prize laureate (born 1888) 1977 – Elvis Presley , American singer and actor (born 1935) 1978 –\n\n[DOC 8] 40–1913) 1872 1877 5 years Democratic TBA 24 Jacob Carl DeGress (TBA–TBA) 1877 1879 2 years Unknown TBA 25 William A. Saylor (TBA–TBA) 1st time 1879 1881 2 years Unknown TBA 26 L. M. Crooker (TBA–TBA) 1881 1883 2 years Unknown TBA 27 William A. Saylor (TBA–TBA) 2nd time 1883 1884 1 year Unknown TBA 28 John W. Robertson (TBA–TBA) 1884 1887 3 years Unknown TBA 29 Joseph Nalle (TBA–TBA) 1887 1890 3 years Unknown TBA 30 John McDonald (TBA–TBA) 1890 1895 5 years Unknown TBA 31 Louis Hancock (TBA–TBA) 1896 1897 1 year Unknown TBA 32 John Dodd McCall (TBA–TBA) 1897 1901 4 years Unknown TBA 33 Robert E. White (TBA–TBA) 1901 1905 4 years Unknown TBA 34 William D. Shelley (TBA–TBA) 1905 1907 2 years Unknown TBA 35 Frank M. Maddox (TBA–TBA) 1907 1909 2 years Unknown TB\n\n[DOC 9] st and composer (b. 1892 ) April 4 – Stefan Wolpe , German-born American composer (b. 1902 ) April 4 – Gil Hodges , American baseball player (b. 1924 ) April 5 – Isabel Jewell , American actress (b. 1907 ) April 6 Brian Donlevy , American actor (b. 1901 ) Heinrich Lübke , 2nd President of the Federal Republic of Germany (b. 1894 ) April 7 Abeid Karume , 1st President of Zanzibar (b. 1905 ) August Zaleski , 6th President of Poland (b. 1883 ) April 9 – James F. Byrnes , United States Secretary of State and Justice of the Supreme Court (b. 1882 ) April 10 – Henry de La Falaise , French film director, Croix de guerre recipient (b. 1898 ) April 16 – Yasunari Kawabata , Japanese novelist (b. 1899 ) April 20 – Andrea Andreen , Swedish physician (b. 1888 ) April 24\n\n[DOC 10] ing states and their standard time zones\n\n[DOC 11] (with most diaspora Protestants celebrating both days). [ citation needed ] Most Western Christian churches , most Eastern Catholic churches and civil calendars; also the Assyrian Church of the East . Gregorian calendar December 25 December 25 The Assyrian Church of the East adopted the Gregorian calendar in 1964. Economy Christmas is typically a peak selling season for retailers in many nations around the world; sales increase dramatically during this time as people purchase gifts, decorations, and supplies to celebrate. In the United States, the \"Christmas shopping season\" starts as early as October. In Canada, merchants begin advertising campaigns before Halloween (October 31) and step up their marketing following Remembrance Day on November 11. In the U\n\n[DOC 12] ng blue moon dates (timeanddate. com). Blue moon calculator (obliquity. com)\n\n[DOC 13] House November 26, 1783 August 19, 1784 8 months and 24 days Trenton, New Jersey French Arms Tavern November 1, 1784 December 24, 1784 1 month and 23 days New York, New York Federal Hall January 11, 1785 October 6, 1788 3 years, 11 months and 5 days Fraunces Tavern , Walter Livingston House October 6, 1788 March 3, 1789 4 months and 25 days United States Congress New York, New York Federal Hall March 4, 1789 December 5, 1790 1 year, 9 months and 1 day Philadelphia, Pennsylvania Congress Hall December 6, 1790 May 14, 1800 9 years, 5 months and 8 days Washington, D. C. United States Capitol November 17, 1800 August 24, 1814 13 years, 9 months and 7 days Blodgett's Hotel September 19, 1814 December 7, 1815 1 year, 2 months and 18 days Old Brick Capitol December\n\n[DOC 14] Establishments and disestablishments categories Establishments Disestablishments Works category Works Introductions v t e 1992 in various calendars Gregorian calendar 1992 MCMXCII Ab urbe condita 2745 Armenian calendar 1441 ԹՎ ՌՆԽԱ Assyrian calendar 6742 Baháʼí calendar 148–149 Balinese saka calendar 1913–1914 Bengali calendar 1398–1399 Berber calendar 2942 British Regnal year 40 Eliz. 2 – 41 Eliz. 2 Buddhist calendar 2536 Burmese calendar 1354 Byzantine calendar 7500–7501 Chinese calendar 辛未 年 (Metal Goat ) 4689 or 4482 — to — 壬申年 (Water Monkey ) 4690 or 4483 Coptic calendar 1708–1709 Discordian calendar 3158 Ethiopian calendar 1984–1985 Hebrew calendar 5752–5753 Hindu calendars - Vikram Samvat 2048–2049 - Shaka Samvat 1913–1914 - Kali Yuga 5092–5093 Holoce\n\n[DOC 15] FALO Louis Trichardt Airport Louis Trichardt , South Africa UTC+02:00 LCE MHLC Golosón International Airport La Ceiba , Honduras UTC−06:00 LCF MGRD Río Dulce Airport (Las Vegas Airport) Río Dulce, Guatemala UTC−06:00 LCG LECO A Coruña Airport A Coruña , Galicia , Spain UTC+01:00 Mar-Oct LCH KLCH Lake Charles Regional Airport Lake Charles, Louisiana , United States UTC−06:00 Mar-Nov LCI KLCI Laconia Municipal Airport Laconia, New Hampshire , United States UTC−05:00 Mar-Nov LCJ EPLL Łódź Władysław Reymont Airport Łódź , Poland UTC+01:00 Mar-Oct LCK KLCK Rickenbacker International Airport Columbus, Ohio , United States UTC−05:00 Mar-Nov LCL MULM La Coloma Airport Pinar del Río , Cuba UTC−05:00 Mar-Nov LCM SACC La Cumbre Airport La Cumbre , Córdoba , Argentina U\n\n[DOC 16] list\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents identify the Basibasy mine's primary product as titanium and state that titanium was first discovered in 1791 by William Gregor. However, none of the provided documents include William Gregor's birth date, which is required to determine the US holiday on which he was born. Therefore, the necessary information to answer the holiday question is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 3245, + "osl": 1107, + "total_tokens": 4352, + "latency_ms": 6214.09, + "tokens_per_second": 178.14 + }, + "context": { + "kept_docs_count": 16, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "1a1a2049-9759-4493-b763-996a88930532", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T02:47:30.004242Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: The Basibasy mine is located in Madagascar. This mine is abundant in a specific chemical element that was discovered for the first time in 1791. The person who discovered this element was born on what is now known as a major US holiday - what holiday is this?\n\nDOCUMENTS:\n\n[DOC 1] ember 1789 he announced his discovery to the Royal Prussian Academy of sciences in Berlin. He also discovered zirconium in 1789, separating it in the form of its \"earth\" zirconia, oxide ZrO 2 . Klaproth analyzed a brightly-colored form of the mineral called \"hyacinth\" from Ceylon. He gave the new element the name zirconium based on its Persian name \"zargun\", gold-colored. Klaproth characterised uranium and zirconium as distinct elements , though he was unable to isolate them. Klaproth independently discovered cerium (1803), a rare earth element , around the same time as Jöns Jacob Berzelius and Wilhelm Hisinger , in the winter of 1803. William Gregor of Cornwall was the first to identify the element titanium in 1791, correctly concluding that he had found a\n\n[DOC 2] Gregor (1791) First isolation Jöns Jakob Berzelius (1825) Named by Martin Heinrich Klaproth (1795) Isotopes of titanium v e Main isotopes Decay Isotope abun­dance half-life ( t 1/2 ) mode pro­duct 44 Ti synth 59.1 y ε 44 Sc 45 Ti synth 3.08 h β + 45 Sc 46 Ti 8.25% stable 47 Ti 7.44% stable 48 Ti 73.7% stable 49 Ti 5.41% stable 50 Ti 5.18% stable Category: Titanium view talk edit | references Titanium is a chemical element ; it has symbol Ti and atomic number 22. Found in nature only as an oxide , it can be reduced to produce a lustrous transition metal with a silver color , low density , and high strength that is resistant to corrosion in sea water , aqua regia , and chlorine . Titanium was discovered in Cornwall , Great Britain , by William Gregor in 1791 a\n\n[DOC 3] Mine in Basibasy, Atsimo-Andrefana, Madagascar Basibasy mine Basibasy mine Location Location Basibasy Atsimo-Andrefana Country Madagascar Coordinates 22°9′S 43°36′E  /  22.150°S 43.600°E  / -22.150; 43.600 Production Products Titanium The Basibasy mine is one of the largest titanium mines in Madagascar . The mine is located in Basibasy , Atsimo-Andrefana . The mine has reserves amounting to 446 million tonnes of ore grading 5.5% titanium . References ^ a b c This article about a specific mine is a stub . You can help Wikipedia by adding missing information . v t e This Atsimo-Andrefana location article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 4] tadtium Roentgenium Copernicium Nihonium Flerovium Moscovium Livermorium Tennessine Oganesson 32 columns Hydrogen Helium Lithium Beryllium Boron Carbon Nitrogen Oxygen Fluorine Neon Sodium Magnesium Aluminium Silicon Phosphorus Sulfur Chlorine Argon Potassium Calcium Scandium Titanium Vanadium Chromium Manganese Iron Cobalt Nickel Copper Zinc Gallium Germanium Arsenic Selenium Bromine Krypton Rubidium Strontium Yttrium Zirconium Niobium Molybdenum Technetium Ruthenium Rhodium Palladium Silver Cadmium Indium Tin Antimony Tellurium Iodine Xenon Caesium Barium Lutetium Hafnium Tantalum Tungsten Rhenium Osmium Iridium Platinum Gold Mercury (element) Thallium Lead Bismuth Polonium Astatine Radon Francium Radium Lawrencium Rutherfordium Dubnium Seaborgium Bohrium\n\n[DOC 5] the lead mineral mimetite and the nickel mineral niccolite , and others. But he is best known for one of his earliest discoveries: in 1791, while analysing the minerals in a black sand he had discovered in the Manaccan valley, he isolated the calx of an unknown metal which he named manaccanite . Later in 1791, Martin Heinrich Klaproth discovered what is now known as the transition metal , titanium in the mineral rutile . Believing this to be a new discovery, Klaproth named it titanium after the Titans of Greek Mythology , but eventually it was clarified that Gregor made the discovery first. Gregor was credited with the discovery, but the element kept the name chosen by Klaproth. Gregor later found titanium in corundum from Tibet , and in a tourmaline from a\n\n[DOC 6] Scottish-American naturalist (1838–1914) John Muir Portrait by Carleton E. Watkins c. 1875 Born ( 1838-04-21 ) April 21, 1838 Dunbar , Scotland Died December 24, 1914 (1914-12-24) (aged 76) Los Angeles, California, U. S. Alma mater University of Wisconsin–Madison Occupations Farmer inventor naturalist philosopher writer botanist zoologist geologist environmentalist Spouse Louisa Strentzel ( m. 1880; died 1905) Children 2 Signature John Muir ( / m jʊər / MURE ; April 21, 1838 – December 24, 1914), also known as \"John of the Mountains\" and \"Father of the National Parks \", was a Scottish-born American naturalist , author, environmental philosopher , botanist , zoologist , glaciologist , and early advocate for the preservation of wilderness in the United States.\n\n[DOC 7] ian-American actor (born 1882) 1957 – Irving Langmuir , American chemist and physicist, Nobel Prize laureate (born 1881) 1958 – Jacob M. Lomakin , Soviet Consul General in New York City, journalist and economist (born 1904) 1959 – William Halsey, Jr. , American admiral (born 1882) 1959 – Wanda Landowska , Polish-French harpsichord player (born 1879) 1961 – Abdul Haq , Pakistani linguist and scholar (born 1870) 1963 – Joan Eardley , British artist (born 1921) 1971 – Spyros Skouras , Greek-American businessman (born 1893) 1972 – Pierre Brasseur , French actor and screenwriter (born 1905) 1973 – Selman Waksman , Ukrainian-American biochemist and microbiologist, Nobel Prize laureate (born 1888) 1977 – Elvis Presley , American singer and actor (born 1935) 1978 –\n\n[DOC 8] 40–1913) 1872 1877 5 years Democratic TBA 24 Jacob Carl DeGress (TBA–TBA) 1877 1879 2 years Unknown TBA 25 William A. Saylor (TBA–TBA) 1st time 1879 1881 2 years Unknown TBA 26 L. M. Crooker (TBA–TBA) 1881 1883 2 years Unknown TBA 27 William A. Saylor (TBA–TBA) 2nd time 1883 1884 1 year Unknown TBA 28 John W. Robertson (TBA–TBA) 1884 1887 3 years Unknown TBA 29 Joseph Nalle (TBA–TBA) 1887 1890 3 years Unknown TBA 30 John McDonald (TBA–TBA) 1890 1895 5 years Unknown TBA 31 Louis Hancock (TBA–TBA) 1896 1897 1 year Unknown TBA 32 John Dodd McCall (TBA–TBA) 1897 1901 4 years Unknown TBA 33 Robert E. White (TBA–TBA) 1901 1905 4 years Unknown TBA 34 William D. Shelley (TBA–TBA) 1905 1907 2 years Unknown TBA 35 Frank M. Maddox (TBA–TBA) 1907 1909 2 years Unknown TB\n\n[DOC 9] st and composer (b. 1892 ) April 4 – Stefan Wolpe , German-born American composer (b. 1902 ) April 4 – Gil Hodges , American baseball player (b. 1924 ) April 5 – Isabel Jewell , American actress (b. 1907 ) April 6 Brian Donlevy , American actor (b. 1901 ) Heinrich Lübke , 2nd President of the Federal Republic of Germany (b. 1894 ) April 7 Abeid Karume , 1st President of Zanzibar (b. 1905 ) August Zaleski , 6th President of Poland (b. 1883 ) April 9 – James F. Byrnes , United States Secretary of State and Justice of the Supreme Court (b. 1882 ) April 10 – Henry de La Falaise , French film director, Croix de guerre recipient (b. 1898 ) April 16 – Yasunari Kawabata , Japanese novelist (b. 1899 ) April 20 – Andrea Andreen , Swedish physician (b. 1888 ) April 24\n\n[DOC 10] ing states and their standard time zones\n\n[DOC 11] (with most diaspora Protestants celebrating both days). [ citation needed ] Most Western Christian churches , most Eastern Catholic churches and civil calendars; also the Assyrian Church of the East . Gregorian calendar December 25 December 25 The Assyrian Church of the East adopted the Gregorian calendar in 1964. Economy Christmas is typically a peak selling season for retailers in many nations around the world; sales increase dramatically during this time as people purchase gifts, decorations, and supplies to celebrate. In the United States, the \"Christmas shopping season\" starts as early as October. In Canada, merchants begin advertising campaigns before Halloween (October 31) and step up their marketing following Remembrance Day on November 11. In the U\n\n[DOC 12] ng blue moon dates (timeanddate. com). Blue moon calculator (obliquity. com)\n\n[DOC 13] House November 26, 1783 August 19, 1784 8 months and 24 days Trenton, New Jersey French Arms Tavern November 1, 1784 December 24, 1784 1 month and 23 days New York, New York Federal Hall January 11, 1785 October 6, 1788 3 years, 11 months and 5 days Fraunces Tavern , Walter Livingston House October 6, 1788 March 3, 1789 4 months and 25 days United States Congress New York, New York Federal Hall March 4, 1789 December 5, 1790 1 year, 9 months and 1 day Philadelphia, Pennsylvania Congress Hall December 6, 1790 May 14, 1800 9 years, 5 months and 8 days Washington, D. C. United States Capitol November 17, 1800 August 24, 1814 13 years, 9 months and 7 days Blodgett's Hotel September 19, 1814 December 7, 1815 1 year, 2 months and 18 days Old Brick Capitol December\n\n[DOC 14] Establishments and disestablishments categories Establishments Disestablishments Works category Works Introductions v t e 1992 in various calendars Gregorian calendar 1992 MCMXCII Ab urbe condita 2745 Armenian calendar 1441 ԹՎ ՌՆԽԱ Assyrian calendar 6742 Baháʼí calendar 148–149 Balinese saka calendar 1913–1914 Bengali calendar 1398–1399 Berber calendar 2942 British Regnal year 40 Eliz. 2 – 41 Eliz. 2 Buddhist calendar 2536 Burmese calendar 1354 Byzantine calendar 7500–7501 Chinese calendar 辛未 年 (Metal Goat ) 4689 or 4482 — to — 壬申年 (Water Monkey ) 4690 or 4483 Coptic calendar 1708–1709 Discordian calendar 3158 Ethiopian calendar 1984–1985 Hebrew calendar 5752–5753 Hindu calendars - Vikram Samvat 2048–2049 - Shaka Samvat 1913–1914 - Kali Yuga 5092–5093 Holoce\n\n[DOC 15] FALO Louis Trichardt Airport Louis Trichardt , South Africa UTC+02:00 LCE MHLC Golosón International Airport La Ceiba , Honduras UTC−06:00 LCF MGRD Río Dulce Airport (Las Vegas Airport) Río Dulce, Guatemala UTC−06:00 LCG LECO A Coruña Airport A Coruña , Galicia , Spain UTC+01:00 Mar-Oct LCH KLCH Lake Charles Regional Airport Lake Charles, Louisiana , United States UTC−06:00 Mar-Nov LCI KLCI Laconia Municipal Airport Laconia, New Hampshire , United States UTC−05:00 Mar-Nov LCJ EPLL Łódź Władysław Reymont Airport Łódź , Poland UTC+01:00 Mar-Oct LCK KLCK Rickenbacker International Airport Columbus, Ohio , United States UTC−05:00 Mar-Nov LCL MULM La Coloma Airport Pinar del Río , Cuba UTC−05:00 Mar-Nov LCM SACC La Cumbre Airport La Cumbre , Córdoba , Argentina U\n\n[DOC 16] list\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 3086, + "osl": 537, + "total_tokens": 3623, + "latency_ms": 14193.64, + "tokens_per_second": 37.83 + }, + "context": { + "num_documents_used": 16, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:47:30.005037Z", + "summary": { + "total_llm_calls": 11, + "total_hops": 5, + "total_input_tokens": 25876, + "total_output_tokens": 14407, + "total_tokens": 40283, + "total_latency_ms": 156490.21, + "average_tokens_per_second": 115.3, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Basibasy_mine", + "https://en.wikipedia.org/wiki/Titanium", + "https://en.wikipedia.org/wiki/William_Gregor", + "https://en.wikipedia.org/wiki/Christmas" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "Christmas" + } + }, + { + "query_id": "13", + "original_query": "One of Barbara Kingsolver's best known novels is about an American missionary family which moves to Africa. At the time, the country they move to was a Belgian colony. Which year did it become independent?", + "timestamp_start": "2026-05-16T02:47:30.016794Z", + "llm_calls": [ + { + "call_id": "0228ac35-7db5-4456-ab96-646031426469", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:47:34.831651Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: One of Barbara Kingsolver's best known novels is about an American missionary family which moves to Africa. At the time, the country they move to was a Belgian colony. Which year did it become independent?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"The Poisonwood Bible\",\n \"Belgian Congo independence\",\n \"Democratic Republic of the Congo independence year\"\n ],\n \"feedback\": \"Need to identify Kingsolver's novel (The Poisonwood Bible) and learn the independence year of the Belgian Congo (now DRC). These three short queries target the novel and the country's independence information.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 478, + "osl": 328, + "total_tokens": 806, + "latency_ms": 4814.69, + "tokens_per_second": 68.12 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "feebd612-459c-439d-9348-a4c4cff1b9dd", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:47:44.923024Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: One of Barbara Kingsolver's best known novels is about an American missionary family which moves to Africa. At the time, the country they move to was a Belgian colony. Which year did it become independent?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] 1998 novel by Barbara Kingsolver The Poisonwood Bible Author Barbara Kingsolver Language English Genre Literary fiction Historical fiction Publisher Harper Publication date 1998 Publication place United States Media type Print (hardback & paperback) and audio-CD Pages 546 (hardcover), 543 (paperback) ISBN 0-06-017540-0 OCLC 38916924 Dewey Decimal 813/.54 21 LC Class PS3561. I496 P65 1998 The Poisonwood Bible is a 1998 novel by Barbara Kingsolver , which tells the story of a missionary family, the Prices, who in 1959 move from the U. S. state of Georgia to the village of Kilanga in the Belgian Congo , close to the Kwilu River . The novel's title refers to Bible errata . The father of the family creates his own \"misprint\" of the Bible. He concludes his sermons\n\n[NEW 2] r list. The Poisonwood Bible , published in 1998, is one of her best-known works; it chronicles the lives of the wife and daughters of a Baptist missionary on a Christian mission in Africa. Although the setting of the novel is somewhat similar to Kingsolver's own childhood in the Democratic Republic of Congo (then Zaire ), the novel is not autobiographical. The novel was chosen as an Oprah's Book Club selection. The Poisonwood Bible won the National Book Prize of South Africa and was shortlisted for both the Pulitzer Prize and PEN/Faulkner Award . Her next novel, published in 2000, was Prodigal Summer , set in southern Appalachia. In 2000, she was awarded the National Humanities Medal by the U. S. President Bill Clinton . Kingsolver wrote a Los Angeles Times\n\n[NEW 3] War II 1940–1945 Independence post–1960 Congo Crisis 1960–1965 Congo–Léopoldville 1960–1971 Zaire ( Shaba I / II ) 1971–1997 First Congo War 1996–1997 Second Congo War 1998–2003 Joseph Kabila presidency 2001–2019 Félix Tshisekedi presidency 2019–present See also: Years DRC Portal v t e The Belgian Congo ( French : Congo belge , pronounced [kɔ̃ɡo bɛlʒ] ; Dutch : Belgisch-Congo ) was a Belgian colony in Central Africa from 1908 until independence in 1960. It is today the Democratic Republic of the Congo (DRC). Colonial rule in the Congo began in the late 19th century. King Leopold II of the Belgians attempted to persuade the Belgian government to support colonial expansion around the then-largely unexploited Congo Basin . Their ambivalence resulted in Leopold\n\n[NEW 4] War, Belgium annexed the Prussian districts of Eupen and Malmedy in 1925, thereby causing the presence of a German-speaking minority. German forces again invaded the country in May 1940 , and 40,690 Belgians, over half of them Jews, were killed during the subsequent occupation and the Holocaust . From September 1944 to February 1945 the Allies liberated Belgium. After World War II , a general strike forced King Leopold III to abdicate in 1951 in favour of his son, Prince Baudouin , since many Belgians thought he had collaborated with Germany during the war. The Belgian Congo gained independence in 1960 during the Congo Crisis ; Ruanda-Urundi followed with its independence two years later. Belgium joined NATO as a founding member and formed the Benelux group\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 4 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[1,1,1,1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1018, + "osl": 700, + "total_tokens": 1718, + "latency_ms": 9472.6, + "tokens_per_second": 73.9 + }, + "context": { + "num_documents_evaluated": 4, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "80b669af-b80e-4896-8029-a1a9461bfa09", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:47:49.086189Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: One of Barbara Kingsolver's best known novels is about an American missionary family which moves to Africa. At the time, the country they move to was a Belgian colony. Which year did it become independent?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] 1998 novel by Barbara Kingsolver The Poisonwood Bible Author Barbara Kingsolver Language English Genre Literary fiction Historical fiction Publisher Harper Publication date 1998 Publication place United States Media type Print (hardback & paperback) and audio-CD Pages 546 (hardcover), 543 (paperback) ISBN 0-06-017540-0 OCLC 38916924 Dewey Decimal 813/.54 21 LC Class PS3561. I496 P65 1998 The Poisonwood Bible is a 1998 novel by Barbara Kingsolver , which tells the story of a missionary family, the Prices, who in 1959 move from the U. S. state of Georgia to the village of Kilanga in the Belgian Congo , close to the Kwilu River . The novel's title refers to Bible errata . The father of the family creates his own \"misprint\" of the Bible. He concludes his sermons\n\n[DOC 2] r list. The Poisonwood Bible , published in 1998, is one of her best-known works; it chronicles the lives of the wife and daughters of a Baptist missionary on a Christian mission in Africa. Although the setting of the novel is somewhat similar to Kingsolver's own childhood in the Democratic Republic of Congo (then Zaire ), the novel is not autobiographical. The novel was chosen as an Oprah's Book Club selection. The Poisonwood Bible won the National Book Prize of South Africa and was shortlisted for both the Pulitzer Prize and PEN/Faulkner Award . Her next novel, published in 2000, was Prodigal Summer , set in southern Appalachia. In 2000, she was awarded the National Humanities Medal by the U. S. President Bill Clinton . Kingsolver wrote a Los Angeles Times\n\n[DOC 3] War II 1940–1945 Independence post–1960 Congo Crisis 1960–1965 Congo–Léopoldville 1960–1971 Zaire ( Shaba I / II ) 1971–1997 First Congo War 1996–1997 Second Congo War 1998–2003 Joseph Kabila presidency 2001–2019 Félix Tshisekedi presidency 2019–present See also: Years DRC Portal v t e The Belgian Congo ( French : Congo belge , pronounced [kɔ̃ɡo bɛlʒ] ; Dutch : Belgisch-Congo ) was a Belgian colony in Central Africa from 1908 until independence in 1960. It is today the Democratic Republic of the Congo (DRC). Colonial rule in the Congo began in the late 19th century. King Leopold II of the Belgians attempted to persuade the Belgian government to support colonial expansion around the then-largely unexploited Congo Basin . Their ambivalence resulted in Leopold\n\n[DOC 4] War, Belgium annexed the Prussian districts of Eupen and Malmedy in 1925, thereby causing the presence of a German-speaking minority. German forces again invaded the country in May 1940 , and 40,690 Belgians, over half of them Jews, were killed during the subsequent occupation and the Holocaust . From September 1944 to February 1945 the Allies liberated Belgium. After World War II , a general strike forced King Leopold III to abdicate in 1951 in favour of his son, Prince Baudouin , since many Belgians thought he had collaborated with Germany during the war. The Belgian Congo gained independence in 1960 during the Congo Crisis ; Ruanda-Urundi followed with its independence two years later. Belgium joined NATO as a founding member and formed the Benelux group\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": true,\n \"reasoning\": \"DOC1 identifies the novel as The Poisonwood Bible, featuring a missionary family moving to the Belgian Congo. DOC3 states that the Belgian Congo gained independence in 1960. These documents together provide the required year of independence.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1027, + "osl": 60, + "total_tokens": 1087, + "latency_ms": 4162.31, + "tokens_per_second": 14.42 + }, + "context": { + "kept_docs_count": 4, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "25ad9b3a-727f-4d0f-91a2-84e1dabe574d", + "component": "answer_generator", + "hop_count": 2, + "timestamp": "2026-05-16T02:47:51.069127Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: One of Barbara Kingsolver's best known novels is about an American missionary family which moves to Africa. At the time, the country they move to was a Belgian colony. Which year did it become independent?\n\nDOCUMENTS:\n\n[DOC 1] 1998 novel by Barbara Kingsolver The Poisonwood Bible Author Barbara Kingsolver Language English Genre Literary fiction Historical fiction Publisher Harper Publication date 1998 Publication place United States Media type Print (hardback & paperback) and audio-CD Pages 546 (hardcover), 543 (paperback) ISBN 0-06-017540-0 OCLC 38916924 Dewey Decimal 813/.54 21 LC Class PS3561. I496 P65 1998 The Poisonwood Bible is a 1998 novel by Barbara Kingsolver , which tells the story of a missionary family, the Prices, who in 1959 move from the U. S. state of Georgia to the village of Kilanga in the Belgian Congo , close to the Kwilu River . The novel's title refers to Bible errata . The father of the family creates his own \"misprint\" of the Bible. He concludes his sermons\n\n[DOC 2] r list. The Poisonwood Bible , published in 1998, is one of her best-known works; it chronicles the lives of the wife and daughters of a Baptist missionary on a Christian mission in Africa. Although the setting of the novel is somewhat similar to Kingsolver's own childhood in the Democratic Republic of Congo (then Zaire ), the novel is not autobiographical. The novel was chosen as an Oprah's Book Club selection. The Poisonwood Bible won the National Book Prize of South Africa and was shortlisted for both the Pulitzer Prize and PEN/Faulkner Award . Her next novel, published in 2000, was Prodigal Summer , set in southern Appalachia. In 2000, she was awarded the National Humanities Medal by the U. S. President Bill Clinton . Kingsolver wrote a Los Angeles Times\n\n[DOC 3] War II 1940–1945 Independence post–1960 Congo Crisis 1960–1965 Congo–Léopoldville 1960–1971 Zaire ( Shaba I / II ) 1971–1997 First Congo War 1996–1997 Second Congo War 1998–2003 Joseph Kabila presidency 2001–2019 Félix Tshisekedi presidency 2019–present See also: Years DRC Portal v t e The Belgian Congo ( French : Congo belge , pronounced [kɔ̃ɡo bɛlʒ] ; Dutch : Belgisch-Congo ) was a Belgian colony in Central Africa from 1908 until independence in 1960. It is today the Democratic Republic of the Congo (DRC). Colonial rule in the Congo began in the late 19th century. King Leopold II of the Belgians attempted to persuade the Belgian government to support colonial expansion around the then-largely unexploited Congo Basin . Their ambivalence resulted in Leopold\n\n[DOC 4] War, Belgium annexed the Prussian districts of Eupen and Malmedy in 1925, thereby causing the presence of a German-speaking minority. German forces again invaded the country in May 1940 , and 40,690 Belgians, over half of them Jews, were killed during the subsequent occupation and the Holocaust . From September 1944 to February 1945 the Allies liberated Belgium. After World War II , a general strike forced King Leopold III to abdicate in 1951 in favour of his son, Prince Baudouin , since many Belgians thought he had collaborated with Germany during the war. The Belgian Congo gained independence in 1960 during the Congo Crisis ; Ruanda-Urundi followed with its independence two years later. Belgium joined NATO as a founding member and formed the Benelux group\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "1960", + "finish_reason": "stop" + }, + "metrics": { + "isl": 925, + "osl": 118, + "total_tokens": 1043, + "latency_ms": 1982.34, + "tokens_per_second": 59.53 + }, + "context": { + "num_documents_used": 4, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:47:51.069938Z", + "summary": { + "total_llm_calls": 4, + "total_hops": 2, + "total_input_tokens": 3448, + "total_output_tokens": 1206, + "total_tokens": 4654, + "total_latency_ms": 20431.94, + "average_tokens_per_second": 53.99, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Barbara_Kingsolver", + "https://en.wikipedia.org/wiki/The_Poisonwood_Bible", + "https://en.wikipedia.org/wiki/Belgian_Congo" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "1960", + "ground_truth_answer": "1960" + } + }, + { + "query_id": "14", + "original_query": "Which football player got 15 or more assists in La Liga during the 2010-2011 season and also played for Arsenal at one point in his career?", + "timestamp_start": "2026-05-16T02:47:51.082297Z", + "llm_calls": [ + { + "call_id": "41e00601-4712-49b6-bcf5-5ff439247aca", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:47:52.428463Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: Which football player got 15 or more assists in La Liga during the 2010-2011 season and also played for Arsenal at one point in his career?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"La Liga 2010-11 assists 15\",\n \"2010-11 La Liga top assists\",\n \"Arsenal players who played in La Liga\"\n ],\n \"feedback\": \"Need to identify any player with 15+ assists in La Liga 2010-11 and who also had a spell at Arsenal. First two queries target the assist leaders for that season; the third gathers Arsenal players with La Liga experience to intersect the results.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 468, + "osl": 752, + "total_tokens": 1220, + "latency_ms": 1346.01, + "tokens_per_second": 558.69 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "cb876c73-2863-4352-9cfb-da102e2103cc", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:47:56.042759Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: Which football player got 15 or more assists in La Liga during the 2010-2011 season and also played for Arsenal at one point in his career?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] 0 10 Salomón Rondón Málaga 14 0 Source: Marca Top assists Rank Player Club Assists 1 Mesut Özil Real Madrid 18 1 Lionel Messi Barcelona 18 3 Dani Alves Barcelona 15 4 Xabi Prieto Real Sociedad 13 5 Juan Mata Valencia 12 6 Ángel Di María Real Madrid 11 7 Santi Cazorla Villarreal 10 Cristiano Ronaldo Real Madrid 9 Valdo Levante 8 Borja Valero Villarreal Source: ESPN Soccernet Archived 26 October 2010 at the Wayback Machine Zamora Trophy The Ricardo Zamora Trophy is awarded by newspaper Marca to the goalkeeper with the lowest ratio of goals conceded to matches played. A goalkeeper had to play at least 28 matches of 60 or more minutes to be eligible for the trophy. Rank Player Club Goals against Matches Average 1 Víctor Valdés Barcelona 16 32 0.50 2 Iker Casill\n\n[NEW 2] lub Goals 1 Mateo Retegui Tigre 19 2 Franco Cristaldo Huracán 14 3 Enzo Copetti Racing 11 4 Adam Bareiro San Lorenzo 10 5 Renzo López Central Córdoba (SdE) 9 Miguel Borja River Plate 7 Cristian Colmán Arsenal / Barracas Central 8 Ramón Ábila Colón Leandro Fernández Independiente Blas Armoa Tigre Source: AFA Top assists Rank Player Club Assists 1 Gastón Togni Defensa y Justicia 9 Martín Ojeda Godoy Cruz 3 Sebastián Villa Boca Juniors 7 Rodrigo Garro Talleres (C) 5 Iván Ramírez Central Córdoba (SdE) 6 Facundo Colidio Tigre 7 Gabriel Ávalos Argentinos Juniors 5 Iván Tapia Barracas Central Francisco González Metilli Central Córdoba (SdE) Franco Zapiola Estudiantes (LP) Franco Cristaldo Huracán Nicolás de la Cruz River Plate Juan Fernando Quintero River Plate Lau\n\n[NEW 3] 49 26 2024–25 La Liga 34 27 3 3 13 11 2 1 52 42 2025–26 La Liga 25 12 3 0 9 4 1 1 38 17 Total 128 81 12 7 38 24 7 6 185 118 Career total 663 475 79 52 168 117 22 16 932 660 ^ Includes Polish Cup , DFB-Pokal , Copa del Rey ^ Appearances in UEFA Cup ^ a b Appearances in UEFA Europa League ^ Appearance in Polish Super Cup ^ a b c d e f g h i j k l m n Appearances in UEFA Champions League ^ a b c d e f g h i j Appearance in DFL-Supercup ^ One appearance in DFL-Supercup, two appearances and two goals in FIFA Club World Cup , one appearance in UEFA Super Cup ^ Five appearances and five goals in UEFA Champions League, two appearances and one goal in UEFA Europa League ^ a b c d Appearance(s) in Supercopa de España International As of match played 31 March 2026 Appe\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 3 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 944, + "osl": 390, + "total_tokens": 1334, + "latency_ms": 2759.8, + "tokens_per_second": 141.31 + }, + "context": { + "num_documents_evaluated": 3, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "d9dd2c0b-4cf2-468a-9819-3393950c5346", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:47:57.971934Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: Which football player got 15 or more assists in La Liga during the 2010-2011 season and also played for Arsenal at one point in his career?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] 0 10 Salomón Rondón Málaga 14 0 Source: Marca Top assists Rank Player Club Assists 1 Mesut Özil Real Madrid 18 1 Lionel Messi Barcelona 18 3 Dani Alves Barcelona 15 4 Xabi Prieto Real Sociedad 13 5 Juan Mata Valencia 12 6 Ángel Di María Real Madrid 11 7 Santi Cazorla Villarreal 10 Cristiano Ronaldo Real Madrid 9 Valdo Levante 8 Borja Valero Villarreal Source: ESPN Soccernet Archived 26 October 2010 at the Wayback Machine Zamora Trophy The Ricardo Zamora Trophy is awarded by newspaper Marca to the goalkeeper with the lowest ratio of goals conceded to matches played. A goalkeeper had to play at least 28 matches of 60 or more minutes to be eligible for the trophy. Rank Player Club Goals against Matches Average 1 Víctor Valdés Barcelona 16 32 0.50 2 Iker Casill\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": true,\n \"reasoning\": \"DOC 1 lists the top La Liga assist providers for the 2010‑2011 season, showing Mesut Özil (Real Madrid) with 18 assists, which meets the '15 or more assists' criterion. Özil is also known to have played for Arsenal (2008‑2013), satisfying the second requirement. Therefore the documents contain all needed facts to answer the question.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 497, + "osl": 429, + "total_tokens": 926, + "latency_ms": 1928.52, + "tokens_per_second": 222.45 + }, + "context": { + "kept_docs_count": 1, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "a5331137-3263-494b-8501-cede295bd0f8", + "component": "answer_generator", + "hop_count": 2, + "timestamp": "2026-05-16T02:48:00.262984Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: Which football player got 15 or more assists in La Liga during the 2010-2011 season and also played for Arsenal at one point in his career?\n\nDOCUMENTS:\n\n[DOC 1] 0 10 Salomón Rondón Málaga 14 0 Source: Marca Top assists Rank Player Club Assists 1 Mesut Özil Real Madrid 18 1 Lionel Messi Barcelona 18 3 Dani Alves Barcelona 15 4 Xabi Prieto Real Sociedad 13 5 Juan Mata Valencia 12 6 Ángel Di María Real Madrid 11 7 Santi Cazorla Villarreal 10 Cristiano Ronaldo Real Madrid 9 Valdo Levante 8 Borja Valero Villarreal Source: ESPN Soccernet Archived 26 October 2010 at the Wayback Machine Zamora Trophy The Ricardo Zamora Trophy is awarded by newspaper Marca to the goalkeeper with the lowest ratio of goals conceded to matches played. A goalkeeper had to play at least 28 matches of 60 or more minutes to be eligible for the trophy. Rank Player Club Goals against Matches Average 1 Víctor Valdés Barcelona 16 32 0.50 2 Iker Casill\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Mesut Özil.", + "finish_reason": "stop" + }, + "metrics": { + "isl": 375, + "osl": 247, + "total_tokens": 622, + "latency_ms": 2290.57, + "tokens_per_second": 107.83 + }, + "context": { + "num_documents_used": 1, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:48:00.263675Z", + "summary": { + "total_llm_calls": 4, + "total_hops": 2, + "total_input_tokens": 2284, + "total_output_tokens": 1818, + "total_tokens": 4102, + "total_latency_ms": 8324.9, + "average_tokens_per_second": 257.57, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/2010–11_La_Liga", + "https://en.wikipedia.org/wiki/Mesut_Özil", + "https://en.wikipedia.org/wiki/Dani_Alves", + "https://en.wikipedia.org/wiki/Lionel_Messi" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Mesut Özil.", + "ground_truth_answer": "This was Mesut Ozil." + } + }, + { + "query_id": "15", + "original_query": "In Slovakia there is a well known Film Festival called the Bratistlava International Film Festival. What city/ town was the film editor for the Grand Prix winner of 2003 born in?", + "timestamp_start": "2026-05-16T02:48:00.276628Z", + "llm_calls": [ + { + "call_id": "e07231d6-3e53-4205-80a3-3b303fa4e70a", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:48:07.296038Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: In Slovakia there is a well known Film Festival called the Bratistlava International Film Festival. What city/ town was the film editor for the Grand Prix winner of 2003 born in?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Bratislava International Film Festival 2003 Grand Prix\",\n \"Grand Prix 2003 Bratislava film editor\",\n \"film editor birthplace\"\n ],\n \"feedback\": \"First query aims to identify the winning film. Second query targets the editor of that winning film. Third query retrieves the editor's city/town of birth.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 476, + "osl": 474, + "total_tokens": 950, + "latency_ms": 7019.28, + "tokens_per_second": 67.53 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "f65795fb-7176-47eb-aa36-121296cd611d", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:48:13.128601Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: In Slovakia there is a well known Film Festival called the Bratistlava International Film Festival. What city/ town was the film editor for the Grand Prix winner of 2003 born in?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] zech Republic ) Best Actor – Maruf Pulodzoda (Angel on the Right, Tajikistan ) FIPRESCI Award – The Island (directed by Constanza Quatriglio, Italy) 2004 IFF Bratislava Grand Prix – Or (My Treasure) (directed by Keren Yedaya, Israel) Best Director – Atiq Rahimi ( Earth and Ashes , Afghanistan ) Best Actress – Lindsay Duncan and Paula Sage ( AfterLife , United Kingdom) Best Actor – Pietro Sibille (Days of Santiago, Peru ) FIPRESCI Award – Private Madness (directed by Joachim Lafosse , Belgium) 2005 IFF Bratislava Grand Prix – The Cave of the Yellow Dog (directed by Byambasuren Davaa , Mongolia ) Best Director – George Clooney ( Good Night, and Good Luck. , United States) Best Actress – Stephanie James ( A Way of Life , United Kingdom) Best Actor – Pavel Liška\n\n[NEW 2] d Best Editing ( Jiří Brožek ). Cast Kateřina Holánová as Olga Simáková Jan Budař as Stanislav Pichlík Miroslav Donutil as Miroslav Norbacher Martin Pechlát as Jaroslav Pichlík Jaroslava Pokorná as Miriam Simáková Pavla Tomicová as PhDr. Vlasta Kulková - Jará Ivana Hloužková as Marie Norbacherová Marek Daniel as Richard Klech Ivana Uhlířová as Jaroslava Pleváková Pavel Liška as Jan Bedura Filip Rajmont as Pavel Velicka Simona Peková as Jitka Spácilová Zuzana Valchárová-Poulová as Zorka V. Nadezda Chroboková as Simona P. Martina Nováková-Hamadáková as Martina N. Plot The film follows several couples over one night in Brno . The focus is on a 20-something couple with unspecified learning difficulties, Olinka ( Kateřina Holánová ) and Standa ( Jan Budař ), who\n\n[NEW 3] Czech film editor Jiří Brožek Born ( 1947-03-11 ) 11 March 1947 (age 79) Roudnice nad Labem Czechoslovakia Alma mater Film and TV School of the Academy of Performing Arts in Prague Occupation film editor Years active 1970–2020 Jiří Brožek (born 11 March 1947) is a Czech film editor. Biography During 1967‒1973 he attended Editing and Directing at FAMU . Then he started to work in Barrandov Movie Studios , from 1976 Brožek is self-employed. During the career he edited more than 100 feature films, variety of the TV production and many TV series. He cooperated with Ladislav Smoljak ( Ball Lightning , Waiter, Scarper! , Jára Cimrman Lying, Sleeping ) Karel Kachyňa ( Love Between the Raindrops , Forbidden Dreams ), Jiří Menzel ( Cutting It Short , The Snowdrop Fes\n\n[NEW 4] German-born American filmmaker (1902–1981) William Wyler Wyler in 1945 Born Willi Wyler ( 1902-07-01 ) July 1, 1902 Mülhausen , German Empire (now in France ) Died July 27, 1981 (1981-07-27) (aged 79) Beverly Hills , California, U. S. Resting place Forest Lawn Memorial Park, Glendale , California, U. S. Citizenship Switzerland U. S. (from 1928) Occupations Film director producer Years active 1925–1970 Spouses Margaret Sullavan ( m. 1934; div. 1936) Margaret Tallichet ( m. 1938) Children 5 Relatives Carl Laemmle Jr. (cousin) Military career Allegiance United States Branch Army National Guard Army Air Forces Service years 1921–1922 (National Guard) 1942–1945 (Army Air Forces) Rank Lieutenant Colonel Unit Army Signal Corps New York Army National Guard Conflicts\n\n[NEW 5] American filmmaker (born 1958) Chris Columbus Columbus in 2025 Born Christopher Joseph Columbus ( 1958-09-10 ) September 10, 1958 (age 67) Spangler, Pennsylvania , U. S. Education New York University ( BA ) Occupations Director producer screenwriter Years active 1984–present Spouse Monica Devereux ( m. 1983) Children 4 Christopher Joseph Columbus (born September 10, 1958) is an American filmmaker. Born in Spangler, Pennsylvania , Columbus studied film at New York University's Tisch School of the Arts where he developed an interest in filmmaking. After writing screenplays for several teen comedies in the mid-1980s, including Gremlins , The Goonies , and Young Sherlock Holmes , he made his directorial debut with a teen adventure, Adventures in Babysitting (198\n\n[NEW 6] American filmmaker (1944–2017) Jonathan Demme Demme in 2015 Born Robert Jonathan Demme ( 1944-02-22 ) February 22, 1944 Baldwin, New York , U. S. Died April 26, 2017 (2017-04-26) (aged 73) New York City, U. S. Education University of Florida Occupations Director producer screenwriter Works Full list Spouses Evelyn Purcell ( m. 1970; div. 1980) Joanne Howard ( m. 1987) Children 3 Relatives Robert W. Castle (cousin) Ted Demme (nephew) Robert Jonathan Demme ( / ˈ d ɛ m i / DEM -ee ; February 22, 1944 – April 26, 2017) was an American filmmaker. His career of directing, producing, and screenwriting spanned more than 30 years and 70 feature films, documentaries, and television productions. In addition to being an Academy Award and a Directors Guild of America Awa\n\n[NEW 7] Database (official site) Filmsite. org\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 7 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 1, 1, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1587, + "osl": 992, + "total_tokens": 2579, + "latency_ms": 5286.97, + "tokens_per_second": 187.63 + }, + "context": { + "num_documents_evaluated": 7, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "4d7f3fa0-a0e8-4959-ac3e-97c2fa63d5db", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:48:14.409046Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: In Slovakia there is a well known Film Festival called the Bratistlava International Film Festival. What city/ town was the film editor for the Grand Prix winner of 2003 born in?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] d Best Editing ( Jiří Brožek ). Cast Kateřina Holánová as Olga Simáková Jan Budař as Stanislav Pichlík Miroslav Donutil as Miroslav Norbacher Martin Pechlát as Jaroslav Pichlík Jaroslava Pokorná as Miriam Simáková Pavla Tomicová as PhDr. Vlasta Kulková - Jará Ivana Hloužková as Marie Norbacherová Marek Daniel as Richard Klech Ivana Uhlířová as Jaroslava Pleváková Pavel Liška as Jan Bedura Filip Rajmont as Pavel Velicka Simona Peková as Jitka Spácilová Zuzana Valchárová-Poulová as Zorka V. Nadezda Chroboková as Simona P. Martina Nováková-Hamadáková as Martina N. Plot The film follows several couples over one night in Brno . The focus is on a 20-something couple with unspecified learning difficulties, Olinka ( Kateřina Holánová ) and Standa ( Jan Budař ), who\n\n[DOC 2] Czech film editor Jiří Brožek Born ( 1947-03-11 ) 11 March 1947 (age 79) Roudnice nad Labem Czechoslovakia Alma mater Film and TV School of the Academy of Performing Arts in Prague Occupation film editor Years active 1970–2020 Jiří Brožek (born 11 March 1947) is a Czech film editor. Biography During 1967‒1973 he attended Editing and Directing at FAMU . Then he started to work in Barrandov Movie Studios , from 1976 Brožek is self-employed. During the career he edited more than 100 feature films, variety of the TV production and many TV series. He cooperated with Ladislav Smoljak ( Ball Lightning , Waiter, Scarper! , Jára Cimrman Lying, Sleeping ) Karel Kachyňa ( Love Between the Raindrops , Forbidden Dreams ), Jiří Menzel ( Cutting It Short , The Snowdrop Fes\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide the birthplace (Roudnice nad Labem) of film editor Jiří Brožek, but they do not establish that Jiří Brožek was the editor of the Grand Prix winner of the 2003 Bratistlava International Film Festival. The link between the festival's 2003 Grand Prix-winning film and its editor is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 771, + "osl": 469, + "total_tokens": 1240, + "latency_ms": 1279.65, + "tokens_per_second": 366.51 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "b97a2486-ecea-48b5-bc5c-50cb498eeada", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:48:21.521112Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: In Slovakia there is a well known Film Festival called the Bratistlava International Film Festival. What city/ town was the film editor for the Grand Prix winner of 2003 born in?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] d Best Editing ( Jiří Brožek ). Cast Kateřina Holánová as Olga Simáková Jan Budař as Stanislav Pichlík Miroslav Donutil as Miroslav Norbacher Martin Pechlát as Jaroslav Pichlík Jaroslava Pokorná as Miriam Simáková Pavla Tomicová as PhDr. Vlasta Kulková - Jará Ivana Hloužková as Marie Norbacherová Marek Daniel as Richard Klech Ivana Uhlířová as Jaroslava Pleváková Pavel Liška as Jan Bedura Filip Rajmont as Pavel Velicka Simona Peková as Jitka Spácilová Zuzana Valchárová-Poulová as Zorka V. Nadezda Chroboková as Simona P. Martina Nováková-Hamadáková as Martina N. Plot The film follows several couples over one night in Brno . The focus is on a 20-something couple with unspecified learning difficulties, Olinka ( Kateřina Holánová ) and Standa ( Jan Budař ), who\n\n[DOC 2] Czech film editor Jiří Brožek Born ( 1947-03-11 ) 11 March 1947 (age 79) Roudnice nad Labem Czechoslovakia Alma mater Film and TV School of the Academy of Performing Arts in Prague Occupation film editor Years active 1970–2020 Jiří Brožek (born 11 March 1947) is a Czech film editor. Biography During 1967‒1973 he attended Editing and Directing at FAMU . Then he started to work in Barrandov Movie Studios , from 1976 Brožek is self-employed. During the career he edited more than 100 feature films, variety of the TV production and many TV series. He cooperated with Ladislav Smoljak ( Ball Lightning , Waiter, Scarper! , Jára Cimrman Lying, Sleeping ) Karel Kachyňa ( Love Between the Raindrops , Forbidden Dreams ), Jiří Menzel ( Cutting It Short , The Snowdrop Fes\n\n\nSEARCH HISTORY: - Query: 'Bratislava International Film Festival 2003 Grand Prix' → 1 docs\n- Query: 'Grand Prix 2003 Bratislava film editor' → 2 docs\n- Query: 'film editor birthplace' → 4 docs\n\nFEEDBACK: First query aims to identify the winning film. Second query targets the editor of that winning film. Third query retrieves the editor's city/town of birth.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"2003 Bratislava International Film Festival Grand Prix winner\",\n \"Grand Prix 2003 Bratislava film editor\",\n \"Jiří Brožek birthplace\"\n ],\n \"feedback\": \"First query will identify the winning film of 2003. Second query targets the editor of that winning film. Third query directly retrieves the editor's city/town of birth.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 988, + "osl": 444, + "total_tokens": 1432, + "latency_ms": 7111.49, + "tokens_per_second": 62.43 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "a6d26e4a-f753-42fb-a940-f9707cb725c6", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T02:48:42.478133Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: In Slovakia there is a well known Film Festival called the Bratistlava International Film Festival. What city/ town was the film editor for the Grand Prix winner of 2003 born in?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] ily and close friends, including his father, with whom he had a strained and formal relationship. He became engaged to several women but never married. He died relatively unknown in 1924 of tuberculosis , aged 40. His literary executor and friend Max Brod ignored Kafka's wishes to destroy his remaining works, publishing them to eventual acclaim. Life Early life Franz Kafka's parents, Hermann and Julie Kafka Kafka was born near the Old Town Square in Prague , then part of the Austro-Hungarian Empire . His family were German-speaking middle-class Ashkenazi Jews . His father, Hermann Kafka, was the fourth child of Jakob Kafka, a shochet or kosher butcher in Osek , a Czech village with a large Jewish population located near Strakonice in southern Bohemia . Herma\n\n[NEW 2] her, Ľudovit Fico, was a forklift operator, and his mother, Emilie Ficová, worked in a shoe store. He has two siblings. His brother Ladislav is a construction entrepreneur, and his sister Lucia Chabadová, who is fourteen years younger, is a prosecutor. Fico grew up and lived with his family in the village of Hrušovany , until the age of six, when they moved to the nearby town of Topoľčany. Fico has described his childhood ambitions as wanting to become either a politician, a sports reporter, or an archaeologist. After completing elementary school, he enrolled in the local gymnasium of Topoľčany, graduating in the summer of 1982. Later the same year, he enrolled in the Law Faculty of the Comenius University in Bratislava . His teachers were impressed with him\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] d Best Editing ( Jiří Brožek ). Cast Kateřina Holánová as Olga Simáková Jan Budař as Stanislav Pichlík Miroslav Donutil as Miroslav Norbacher Martin Pechlát as Jaroslav Pichlík Jaroslava Pokorná as Miriam Simáková Pavla Tomicová as PhDr. Vlasta Kulková - Jará Ivana Hloužková as Marie Norbacherová Ma...\n[KEPT 2] Czech film editor Jiří Brožek Born ( 1947-03-11 ) 11 March 1947 (age 79) Roudnice nad Labem Czechoslovakia Alma mater Film and TV School of the Academy of Performing Arts in Prague Occupation film editor Years active 1970–2020 Jiří Brožek (born 11 March 1947) is a Czech film editor. Biography During...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 2 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 830, + "osl": 590, + "total_tokens": 1420, + "latency_ms": 20375.63, + "tokens_per_second": 28.96 + }, + "context": { + "num_documents_evaluated": 2, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "e6339a38-3284-4cb9-ad89-d04aa2d3e2aa", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T02:48:55.596992Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: In Slovakia there is a well known Film Festival called the Bratistlava International Film Festival. What city/ town was the film editor for the Grand Prix winner of 2003 born in?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] d Best Editing ( Jiří Brožek ). Cast Kateřina Holánová as Olga Simáková Jan Budař as Stanislav Pichlík Miroslav Donutil as Miroslav Norbacher Martin Pechlát as Jaroslav Pichlík Jaroslava Pokorná as Miriam Simáková Pavla Tomicová as PhDr. Vlasta Kulková - Jará Ivana Hloužková as Marie Norbacherová Marek Daniel as Richard Klech Ivana Uhlířová as Jaroslava Pleváková Pavel Liška as Jan Bedura Filip Rajmont as Pavel Velicka Simona Peková as Jitka Spácilová Zuzana Valchárová-Poulová as Zorka V. Nadezda Chroboková as Simona P. Martina Nováková-Hamadáková as Martina N. Plot The film follows several couples over one night in Brno . The focus is on a 20-something couple with unspecified learning difficulties, Olinka ( Kateřina Holánová ) and Standa ( Jan Budař ), who\n\n[DOC 2] Czech film editor Jiří Brožek Born ( 1947-03-11 ) 11 March 1947 (age 79) Roudnice nad Labem Czechoslovakia Alma mater Film and TV School of the Academy of Performing Arts in Prague Occupation film editor Years active 1970–2020 Jiří Brožek (born 11 March 1947) is a Czech film editor. Biography During 1967‒1973 he attended Editing and Directing at FAMU . Then he started to work in Barrandov Movie Studios , from 1976 Brožek is self-employed. During the career he edited more than 100 feature films, variety of the TV production and many TV series. He cooperated with Ladislav Smoljak ( Ball Lightning , Waiter, Scarper! , Jára Cimrman Lying, Sleeping ) Karel Kachyňa ( Love Between the Raindrops , Forbidden Dreams ), Jiří Menzel ( Cutting It Short , The Snowdrop Fes\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": true,\n \"reasoning\": \"Document 1 identifies Jiří Brožek as the film editor of the relevant film (Best Editing (Jiří Brožek)). Document 2 provides Jiří Brožek's birthplace: Roudnice nad Labem, Czechoslovakia. These documents together give the required city/town of the film editor for the Grand Prix winner of 2003.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 755, + "osl": 590, + "total_tokens": 1345, + "latency_ms": 13117.96, + "tokens_per_second": 44.98 + }, + "context": { + "kept_docs_count": 2, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "a78700f6-6e10-479c-bd56-66759feb66c2", + "component": "answer_generator", + "hop_count": 3, + "timestamp": "2026-05-16T02:48:57.042466Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: In Slovakia there is a well known Film Festival called the Bratistlava International Film Festival. What city/ town was the film editor for the Grand Prix winner of 2003 born in?\n\nDOCUMENTS:\n\n[DOC 1] d Best Editing ( Jiří Brožek ). Cast Kateřina Holánová as Olga Simáková Jan Budař as Stanislav Pichlík Miroslav Donutil as Miroslav Norbacher Martin Pechlát as Jaroslav Pichlík Jaroslava Pokorná as Miriam Simáková Pavla Tomicová as PhDr. Vlasta Kulková - Jará Ivana Hloužková as Marie Norbacherová Marek Daniel as Richard Klech Ivana Uhlířová as Jaroslava Pleváková Pavel Liška as Jan Bedura Filip Rajmont as Pavel Velicka Simona Peková as Jitka Spácilová Zuzana Valchárová-Poulová as Zorka V. Nadezda Chroboková as Simona P. Martina Nováková-Hamadáková as Martina N. Plot The film follows several couples over one night in Brno . The focus is on a 20-something couple with unspecified learning difficulties, Olinka ( Kateřina Holánová ) and Standa ( Jan Budař ), who\n\n[DOC 2] Czech film editor Jiří Brožek Born ( 1947-03-11 ) 11 March 1947 (age 79) Roudnice nad Labem Czechoslovakia Alma mater Film and TV School of the Academy of Performing Arts in Prague Occupation film editor Years active 1970–2020 Jiří Brožek (born 11 March 1947) is a Czech film editor. Biography During 1967‒1973 he attended Editing and Directing at FAMU . Then he started to work in Barrandov Movie Studios , from 1976 Brožek is self-employed. During the career he edited more than 100 feature films, variety of the TV production and many TV series. He cooperated with Ladislav Smoljak ( Ball Lightning , Waiter, Scarper! , Jára Cimrman Lying, Sleeping ) Karel Kachyňa ( Love Between the Raindrops , Forbidden Dreams ), Jiří Menzel ( Cutting It Short , The Snowdrop Fes\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Roudnice nad Labem.", + "finish_reason": "stop" + }, + "metrics": { + "isl": 622, + "osl": 190, + "total_tokens": 812, + "latency_ms": 1444.88, + "tokens_per_second": 131.5 + }, + "context": { + "num_documents_used": 2, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:48:57.043300Z", + "summary": { + "total_llm_calls": 7, + "total_hops": 3, + "total_input_tokens": 6029, + "total_output_tokens": 3749, + "total_tokens": 9778, + "total_latency_ms": 55635.86, + "average_tokens_per_second": 127.08, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Bratislava_International_Film_Festival", + "https://en.wikipedia.org/wiki/Boredom_in_Brno", + "https://en.wikipedia.org/wiki/Ji%C5%99%C3%AD_Bro%C5%BEek", + "https://en.wikipedia.org/wiki/Roudnice_nad_Labem" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Roudnice nad Labem.", + "ground_truth_answer": "Roudnice nad Labem" + } + }, + { + "query_id": "16", + "original_query": "On March 7th, 2012, the director James Cameron explored a very deep underseas trench. As of August 3, 2024, how many times would the tallest building in San Francisco fit end to end from the bottom of the New Britain Trench to the surface of the ocean? The answer should be a rounded-off whole number. ", + "timestamp_start": "2026-05-16T02:48:57.056610Z", + "llm_calls": [ + { + "call_id": "89b2770c-f70f-4deb-ac69-06682e6bcbdc", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:48:59.455440Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: On March 7th, 2012, the director James Cameron explored a very deep underseas trench. As of August 3, 2024, how many times would the tallest building in San Francisco fit end to end from the bottom of the New Britain Trench to the surface of the ocean? The answer should be a rounded-off whole number. \n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"New Britain Trench depth\",\n \"tallest building San Francisco height\",\n \"Salesforce Tower height\"\n ],\n \"feedback\": \"Need the depth of the New Britain Trench (bottom to sea surface) and the height of San Francisco's tallest building (Salesforce Tower) to compute how many times the building fits into the trench depth.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 509, + "osl": 426, + "total_tokens": 935, + "latency_ms": 2398.7, + "tokens_per_second": 177.6 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "172ae551-c030-4c09-b713-410632994061", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:49:02.885597Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: On March 7th, 2012, the director James Cameron explored a very deep underseas trench. As of August 3, 2024, how many times would the tallest building in San Francisco fit end to end from the bottom of the New Britain Trench to the surface of the ocean? The answer should be a rounded-off whole number. \n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] o List of oceanic trenches References ^ a b ^ a b c ^ a b c d ^ ^ ^ ^ ^ ^ ^ a b ^ a b ^ ^ International Council of Scientific Unions, International Geophysical Committee (1969). Annals of the International Geophysical Year, Volumes 46-48 p.99 Oxford: Pergamon Press ^ External links Sketch of the area Russian language map of the area 82°24′00′′N 19°31′00′′E  /  82.40000°N 19.51667°E  / 82.40000; 19.51667\n\n[NEW 2] received their name \"Islas Salomón\" from the legend of the biblical land of Ophir — fabled as the source of King Solomon 's wealth which was hoped to be discovered, in the first 1568 voyage by their discoverer Spanish navigator Álvaro de Mendaña de Neira . They were so named after his voyage when it was mapped. Deepest point The Solomon Sea roughly corresponds with the Solomon Sea Plate , a tectonic feature, and includes the New Britain Trench , in the New Britain subduction zone , which reaches its maximum depth at 29,988 feet (9,140 m) below sea level in the Planet Deep. References ^ ^ ^ ^ External links Map of the Solomon Sea. at the Library of Congress Web Archives (archived 2006-10-21)\n\n[NEW 3] orean Basin), which is about 4,000 m (13,000 ft) deep. The bathymetry of the ocean bottom is marked by fault block ridges, abyssal plains , ocean deeps , and basins. The average depth of the Arctic Ocean is 1,038 m (3,406 ft). The deepest point is Molloy Hole in the Fram Strait , at about 5,550 m (18,210 ft). The two major basins are further subdivided by ridges into the Canada Basin (between Beaufort Shelf of North America and the Alpha Ridge ), Makarov Basin (between the Alpha and Lomonosov Ridges), Amundsen Basin (between Lomonosov and Gakkel ridges), and Nansen Basin (between the Gakkel Ridge and the continental shelf that includes the Franz Josef Land ). Geology The crystalline basement rocks of mountains around the Arctic Ocean were recrystallized or f\n\n[NEW 4] on Depth (km) MMI Deaths Notes December 16, 1920 7.9 China , Ningxia 15.0 XII 273,407 November 11, 1922 8.3–8.6 Chile , Atacama 35.0 XI 1,000 September 1, 1923 7.9–8.2 Japan , Kanagawa Prefecture 23.0 XI 142,800 April 14, 1924 8.3 Philippines , Davao Region 15.0 IX 500 March 16, 1925 6.9–7.0 China , Yunnan 26.0 IX 5,000 October 22, 1926 6.0 Turkey , Kars Province 7.0 IX 360 May 22, 1927 7.7 China , Gansu 15.0 XI 40,912 December 1, 1928 7.6 Chile , Maule Region 20.0 IX 279 May 1, 1929 7.2 Turkmen Soviet Socialist Republic , Ahal Region 10.0 IX 3,257–3,800 July 23, 1930 6.6 Italy , Irpinia 15.0 X 1,404 August 10, 1931 8.0 China , Xinjiang Province 20.0 XI 10,000 May 20, 1932 5.4 Persia , Mazandaran province 12.0 VIII 1,070 August 25, 1933 7.3 China , Sichuan P\n\n[NEW 5] Skyline of San Francisco San Francisco from the Twin Peaks in 2021 Tallest building Salesforce Tower (2018) Tallest building height 1,070 ft (326.1 m) First 150 m+ building 44 Montgomery (1967) Number of tall buildings (2026) Taller than 100 m (328 ft) 100 Taller than 150 m (492 ft) 27 Taller than 200 m (656 ft) 5 Taller than 300 m (984 ft) 1 Number of tall buildings — feet Taller than 300 ft (91.4 m) 124 The Financial District from Ina Coolbrith Park in 2021 San Francisco skyline from Mission Bay during sunset in 2024 San Francisco , a major city in the U. S state of California , has over 480 high-rises, 124 of which are at least 300 feet (91 m) tall as of 2026. The tallest building in the city is Salesforce Tower , the city's sole supertall skyscraper . He\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 5 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 1, 0, 0, 1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1380, + "osl": 542, + "total_tokens": 1922, + "latency_ms": 2768.18, + "tokens_per_second": 195.8 + }, + "context": { + "num_documents_evaluated": 5, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "8ba8401d-71ec-4e49-836b-52a854059001", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:49:11.538781Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: On March 7th, 2012, the director James Cameron explored a very deep underseas trench. As of August 3, 2024, how many times would the tallest building in San Francisco fit end to end from the bottom of the New Britain Trench to the surface of the ocean? The answer should be a rounded-off whole number. \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] o List of oceanic trenches References ^ a b ^ a b c ^ a b c d ^ ^ ^ ^ ^ ^ ^ a b ^ a b ^ ^ International Council of Scientific Unions, International Geophysical Committee (1969). Annals of the International Geophysical Year, Volumes 46-48 p.99 Oxford: Pergamon Press ^ External links Sketch of the area Russian language map of the area 82°24′00′′N 19°31′00′′E  /  82.40000°N 19.51667°E  / 82.40000; 19.51667\n\n[DOC 2] received their name \"Islas Salomón\" from the legend of the biblical land of Ophir — fabled as the source of King Solomon 's wealth which was hoped to be discovered, in the first 1568 voyage by their discoverer Spanish navigator Álvaro de Mendaña de Neira . They were so named after his voyage when it was mapped. Deepest point The Solomon Sea roughly corresponds with the Solomon Sea Plate , a tectonic feature, and includes the New Britain Trench , in the New Britain subduction zone , which reaches its maximum depth at 29,988 feet (9,140 m) below sea level in the Planet Deep. References ^ ^ ^ ^ External links Map of the Solomon Sea. at the Library of Congress Web Archives (archived 2006-10-21)\n\n[DOC 3] Skyline of San Francisco San Francisco from the Twin Peaks in 2021 Tallest building Salesforce Tower (2018) Tallest building height 1,070 ft (326.1 m) First 150 m+ building 44 Montgomery (1967) Number of tall buildings (2026) Taller than 100 m (328 ft) 100 Taller than 150 m (492 ft) 27 Taller than 200 m (656 ft) 5 Taller than 300 m (984 ft) 1 Number of tall buildings — feet Taller than 300 ft (91.4 m) 124 The Financial District from Ina Coolbrith Park in 2021 San Francisco skyline from Mission Bay during sunset in 2024 San Francisco , a major city in the U. S state of California , has over 480 high-rises, 124 of which are at least 300 feet (91 m) tall as of 2026. The tallest building in the city is Salesforce Tower , the city's sole supertall skyscraper . He\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"sufficient\": true, \"reasoning\": \"Document 2 gives the maximum depth of the New Britain Trench as 29,988 ft (9,140 m) below sea level. Document 3 provides the height of San Francisco's tallest building, Salesforce Tower, as 1,070 ft (326.1 m). These two facts allow the calculation (29,988 ft ÷ 1,070 ft ≈ 28.0), yielding the required rounded whole number. No additional information is needed.\"}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 825, + "osl": 119, + "total_tokens": 944, + "latency_ms": 8652.43, + "tokens_per_second": 13.75 + }, + "context": { + "kept_docs_count": 3, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "c271986a-d7cd-4b54-89eb-b26440d87a25", + "component": "answer_generator", + "hop_count": 2, + "timestamp": "2026-05-16T02:49:14.399430Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: On March 7th, 2012, the director James Cameron explored a very deep underseas trench. As of August 3, 2024, how many times would the tallest building in San Francisco fit end to end from the bottom of the New Britain Trench to the surface of the ocean? The answer should be a rounded-off whole number. \n\nDOCUMENTS:\n\n[DOC 1] o List of oceanic trenches References ^ a b ^ a b c ^ a b c d ^ ^ ^ ^ ^ ^ ^ a b ^ a b ^ ^ International Council of Scientific Unions, International Geophysical Committee (1969). Annals of the International Geophysical Year, Volumes 46-48 p.99 Oxford: Pergamon Press ^ External links Sketch of the area Russian language map of the area 82°24′00′′N 19°31′00′′E  /  82.40000°N 19.51667°E  / 82.40000; 19.51667\n\n[DOC 2] received their name \"Islas Salomón\" from the legend of the biblical land of Ophir — fabled as the source of King Solomon 's wealth which was hoped to be discovered, in the first 1568 voyage by their discoverer Spanish navigator Álvaro de Mendaña de Neira . They were so named after his voyage when it was mapped. Deepest point The Solomon Sea roughly corresponds with the Solomon Sea Plate , a tectonic feature, and includes the New Britain Trench , in the New Britain subduction zone , which reaches its maximum depth at 29,988 feet (9,140 m) below sea level in the Planet Deep. References ^ ^ ^ ^ External links Map of the Solomon Sea. at the Library of Congress Web Archives (archived 2006-10-21)\n\n[DOC 3] Skyline of San Francisco San Francisco from the Twin Peaks in 2021 Tallest building Salesforce Tower (2018) Tallest building height 1,070 ft (326.1 m) First 150 m+ building 44 Montgomery (1967) Number of tall buildings (2026) Taller than 100 m (328 ft) 100 Taller than 150 m (492 ft) 27 Taller than 200 m (656 ft) 5 Taller than 300 m (984 ft) 1 Number of tall buildings — feet Taller than 300 ft (91.4 m) 124 The Financial District from Ina Coolbrith Park in 2021 San Francisco skyline from Mission Bay during sunset in 2024 San Francisco , a major city in the U. S state of California , has over 480 high-rises, 124 of which are at least 300 feet (91 m) tall as of 2026. The tallest building in the city is Salesforce Tower , the city's sole supertall skyscraper . He\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "28", + "finish_reason": "stop" + }, + "metrics": { + "isl": 728, + "osl": 184, + "total_tokens": 912, + "latency_ms": 2860.02, + "tokens_per_second": 64.34 + }, + "context": { + "num_documents_used": 3, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:49:14.400111Z", + "summary": { + "total_llm_calls": 4, + "total_hops": 2, + "total_input_tokens": 3442, + "total_output_tokens": 1271, + "total_tokens": 4713, + "total_latency_ms": 16679.33, + "average_tokens_per_second": 112.87, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/James_Cameron", + "https://en.wikipedia.org/wiki/Solomon_Sea#Deepest_point", + "https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_San_Francisco" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "28", + "ground_truth_answer": "28" + } + }, + { + "query_id": "17", + "original_query": "In August of 2024, what is the first name of the mayor of the U.S. state capital city who attended the same university as at least one U.S. president and whose city is home to an outgoing or former full member of the Big 12 Conference", + "timestamp_start": "2026-05-16T02:49:14.414713Z", + "llm_calls": [ + { + "call_id": "3a5791d7-2677-4979-a0e0-aa100aff65d4", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:49:23.257330Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: In August of 2024, what is the first name of the mayor of the U.S. state capital city who attended the same university as at least one U.S. president and whose city is home to an outgoing or former full member of the Big 12 Conference\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Austin mayor 2024\",\n \"Kirk Watson education\",\n \"University of Texas alumni presidents\"\n ],\n \"feedback\": \"Need to identify the mayor of Austin (state capital) in August 2024, confirm his first name, verify his university (UT Austin) and that at least one U.S. president also attended that university.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 477, + "osl": 2265, + "total_tokens": 2742, + "latency_ms": 8842.47, + "tokens_per_second": 256.15 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "efc487dd-dded-444c-903c-0ef413bf431e", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:50:04.767245Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: In August of 2024, what is the first name of the mayor of the U.S. state capital city who attended the same university as at least one U.S. president and whose city is home to an outgoing or former full member of the Big 12 Conference\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] foreign policy. That is not in our power. The resolution, however, has the power to divide Austin — and will.\" Electoral history 2024 2024 Austin mayoral election Candidate Votes % Kirk Watson 175,096 50.0041% Carmen Llanes Pulido 70,550 20.14% Kathie Tovo 58,280 16.64% Jeffrey Bowen 29,383 8.39% Doug Greco 16,865 4.82% 2022 2022 Austin mayoral run-off Party Candidate Votes % Nonpartisan Kirk Watson 57,346 50.39 Nonpartisan Celia Israel 56,460 49.61 Total votes 113,806 100 2022 Austin mayoral election Party Candidate Votes % Nonpartisan Celia Israel 121,862 39.99 Nonpartisan Kirk Watson 106,508 34.95 Nonpartisan Jennifer Virden 56,189 18.44 Nonpartisan Phil Campero Brual 7,295 2.39 Nonpartisan Anthony Bradshaw 7,102 2.33 Nonpartisan Gary Spellman 5,781 1.90\n\n[DOC 2] Head of the Austin, Texas municipal government Mayor of Austin Flag of the City of Austin Incumbent Kirk Watson since January 6, 2023 Residence Private residence Term length Four years renewable once Inaugural holder Edwin Waller Formation 1840 Salary $134,191 Website austintexas . gov /mayor The mayor of Austin is the official head of the city of Austin in the U. S. state of Texas . The office was established in 1840 after Austin incorporated as a city in 1839. The mayor of Austin is elected to a four-year term and limited to serving no more than two terms. Kirk Watson took office as mayor on January 6, 2023, for a third term and was re-elected to a fourth term in 2024, having served as mayor from 1997 to 2001. Duties and powers Austin has a council–manager\n\n[DOC 3] rsity of Bradford Doctor of Technology (DTec) USA 1975 Westminster College (Utah) Doctor of Laws (LLD) France 1976 University of Paris (Sorbonne) Docteur USA 1981 Wesleyan College (Georgia) Dr of Public Administration (DPA) USA 1982 Westminster College (Missouri) Doctor of Laws (DL) England 19 July 1985 University of Kent Doctor of Laws (LL. D) Canada 7 June 1991 University of Calgary Doctor of Laws (LL. D) England 1994 Goldsmiths, University of London Honorary Fellow USA 1994 Bellarmine College (Kentucky) Doctor of Laws (HLD) England 21 June 1997 Open University Doctor of the University (D. Univ) Wales 1998 University of Wales Doctor of Laws (LL. D) England 18 July 2001 University of Greenwich Doctor of Laws (LL. D) England — Royal College of Organists Fell\n\n[DOC 4] her degree instead of four because she had taken two semesters off owing to her acting work. On 25 May 2014, she graduated from Brown University with a Bachelor of Arts degree in English literature. In 2016, she was appointed a visiting fellow at Lady Margaret Hall, Oxford . In 2023, she began a Master of Studies in creative writing at New College, Oxford . Acting career 1999–2009: Harry Potter and worldwide recognition In 1999, casting began for Harry Potter and the Philosopher's Stone , the film adaptation of British author J. K. Rowling 's best-selling novel . Casting agents found Watson through her Oxford theatre teacher. She had acted in school plays, but had no film acting experience. Her first audition took place when she was nine years old. Although\n\n[DOC 5] exas University of Virginia ( BS , MS ) University of Texas, Austin ( JD ) January 3, 2019 Dripping Springs Texas 22 Troy Nehls Republican ( 1968-04-07 ) April 7, 1968 (age 57) Sheriff of Fort Bend County Liberty University ( BA ) University of Houston–Downtown ( MA ) January 3, 2021 Richmond Texas 23 Tony Gonzales Republican ( 1980-10-10 ) October 10, 1980 (age 45) United States Navy Chaminade University ( AA ) Excelsior College ( BS ) American Public University ( MA ) January 3, 2021 San Antonio Texas 24 Beth Van Duyne Republican ( 1970-11-16 ) November 16, 1970 (age 55) Mayor of Irving Cornell University ( BA ) January 3, 2021 Irving Texas 25 Roger Williams Republican ( 1949-09-13 ) September 13, 1949 (age 76) Secretary of State of Texas Texas Christian U\n\n[DOC 6] football player Raymond Radway , football player Daryl Richardson , football player Bernard Scott , football player Jeev Milkha Singh (1996), golfer Gilbert Tuhabonye , runner and author Charcandrick West , football player Allen Wilson , football coach Earl Young , runner Art Briles , former head coach at Baylor University Rusty Whitt , coach Wes Kittley , coach of Texas Tech Red Raiders track and field Faculty Everett Ferguson , patristics scholar Douglas A. Foster , professor of church history Michael A. O'Donnell , professor of family studies Notes When James Cox's wife became ill, his brother, Alonzo B. Cox, filled in for him to finish the term. References ^ ^ As of June 30, 2024. ^ ^ ^ ^ ^ ^ [Sources: John C. Stevens, _No Ordinary University_, p. 248; J\n\n[DOC 7] its 2008 edition due to financial concerns. The university's national literary magazine, New Madrid with editor Ann Neelon, featured work from a range of nationally recognized authors and received acclaim from sources as diverse as La Bloga , a leading Hispanic journal, and New Pages , a leading national review of literary magazines. A lack of funding led to the suspension of publication in 2018. Presidents Presidents of the university include: John W. Carr, 1923–1926 Rainey T. Wells, 1926–1932 John W. Carr, 1933–1936 James H. Richmond, 1936–1945 Ralph H. Woods, 1945–1968 Harry M. Sparks, 1968–1973 Constantine W. Curris , 1973–1983 Kala M. Stroup, 1983–1990 James L. Booth, 1989–1990 (Acting) Ronald J. Kurth , 1990–1994 Samuel Kern Alexander , 1994–2001 Field\n\n[DOC 8] Politics of Texas Constitution and law Constitution of the United States Constitution of Texas Texas law Executive Governor Greg Abbott (R) Lieutenant Governor Dan Patrick (R) Attorney General Ken Paxton (R) Secretary of State John B. Scott (R) Comptroller of Public Accounts Glenn Hegar (R) Land Commissioner George P. Bush (R) Legislature Texas Senate President Dan Patrick (R) President pro tempore Donna Campbell (R) Texas House of Representatives Speaker Dade Phelan (R) Speaker pro tempore ( vacant ) Judiciary Texas Supreme Court Chief Justice Nathan Hecht (R) Justice Jimmy Blacklock (R) Justice Debra Lehrmann (R) Justice John P. Devine (R) Justice Rebeca Huddle (R) Justice Jane Bland (R) Justice Jeffrey S. Boyd (R) Justice J. Brett Busby (R) Justice Evan A\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents provide information about Austin, Texas (the state capital) and its mayor Kirk Watson, including his first name and that Austin is home to an outgoing full member of the Big 12 (University of Texas). However, they do not contain any information about Watson's education or the university he attended, so we cannot verify that he attended the same university as a U.S. president. The missing educational detail prevents us from definitively answering the question.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1953, + "osl": 675, + "total_tokens": 2628, + "latency_ms": 14265.4, + "tokens_per_second": 47.32 + }, + "context": { + "kept_docs_count": 8, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "8c138b8a-eeb4-40d4-a0f0-d491dfdf837b", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:50:14.188822Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: In August of 2024, what is the first name of the mayor of the U.S. state capital city who attended the same university as at least one U.S. president and whose city is home to an outgoing or former full member of the Big 12 Conference\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] foreign policy. That is not in our power. The resolution, however, has the power to divide Austin — and will.\" Electoral history 2024 2024 Austin mayoral election Candidate Votes % Kirk Watson 175,096 50.0041% Carmen Llanes Pulido 70,550 20.14% Kathie Tovo 58,280 16.64% Jeffrey Bowen 29,383 8.39% Doug Greco 16,865 4.82% 2022 2022 Austin mayoral run-off Party Candidate Votes % Nonpartisan Kirk Watson 57,346 50.39 Nonpartisan Celia Israel 56,460 49.61 Total votes 113,806 100 2022 Austin mayoral election Party Candidate Votes % Nonpartisan Celia Israel 121,862 39.99 Nonpartisan Kirk Watson 106,508 34.95 Nonpartisan Jennifer Virden 56,189 18.44 Nonpartisan Phil Campero Brual 7,295 2.39 Nonpartisan Anthony Bradshaw 7,102 2.33 Nonpartisan Gary Spellman 5,781 1.90\n\n[DOC 2] Head of the Austin, Texas municipal government Mayor of Austin Flag of the City of Austin Incumbent Kirk Watson since January 6, 2023 Residence Private residence Term length Four years renewable once Inaugural holder Edwin Waller Formation 1840 Salary $134,191 Website austintexas . gov /mayor The mayor of Austin is the official head of the city of Austin in the U. S. state of Texas . The office was established in 1840 after Austin incorporated as a city in 1839. The mayor of Austin is elected to a four-year term and limited to serving no more than two terms. Kirk Watson took office as mayor on January 6, 2023, for a third term and was re-elected to a fourth term in 2024, having served as mayor from 1997 to 2001. Duties and powers Austin has a council–manager\n\n[DOC 3] rsity of Bradford Doctor of Technology (DTec) USA 1975 Westminster College (Utah) Doctor of Laws (LLD) France 1976 University of Paris (Sorbonne) Docteur USA 1981 Wesleyan College (Georgia) Dr of Public Administration (DPA) USA 1982 Westminster College (Missouri) Doctor of Laws (DL) England 19 July 1985 University of Kent Doctor of Laws (LL. D) Canada 7 June 1991 University of Calgary Doctor of Laws (LL. D) England 1994 Goldsmiths, University of London Honorary Fellow USA 1994 Bellarmine College (Kentucky) Doctor of Laws (HLD) England 21 June 1997 Open University Doctor of the University (D. Univ) Wales 1998 University of Wales Doctor of Laws (LL. D) England 18 July 2001 University of Greenwich Doctor of Laws (LL. D) England — Royal College of Organists Fell\n\n[DOC 4] her degree instead of four because she had taken two semesters off owing to her acting work. On 25 May 2014, she graduated from Brown University with a Bachelor of Arts degree in English literature. In 2016, she was appointed a visiting fellow at Lady Margaret Hall, Oxford . In 2023, she began a Master of Studies in creative writing at New College, Oxford . Acting career 1999–2009: Harry Potter and worldwide recognition In 1999, casting began for Harry Potter and the Philosopher's Stone , the film adaptation of British author J. K. Rowling 's best-selling novel . Casting agents found Watson through her Oxford theatre teacher. She had acted in school plays, but had no film acting experience. Her first audition took place when she was nine years old. Although\n\n[DOC 5] exas University of Virginia ( BS , MS ) University of Texas, Austin ( JD ) January 3, 2019 Dripping Springs Texas 22 Troy Nehls Republican ( 1968-04-07 ) April 7, 1968 (age 57) Sheriff of Fort Bend County Liberty University ( BA ) University of Houston–Downtown ( MA ) January 3, 2021 Richmond Texas 23 Tony Gonzales Republican ( 1980-10-10 ) October 10, 1980 (age 45) United States Navy Chaminade University ( AA ) Excelsior College ( BS ) American Public University ( MA ) January 3, 2021 San Antonio Texas 24 Beth Van Duyne Republican ( 1970-11-16 ) November 16, 1970 (age 55) Mayor of Irving Cornell University ( BA ) January 3, 2021 Irving Texas 25 Roger Williams Republican ( 1949-09-13 ) September 13, 1949 (age 76) Secretary of State of Texas Texas Christian U\n\n[DOC 6] football player Raymond Radway , football player Daryl Richardson , football player Bernard Scott , football player Jeev Milkha Singh (1996), golfer Gilbert Tuhabonye , runner and author Charcandrick West , football player Allen Wilson , football coach Earl Young , runner Art Briles , former head coach at Baylor University Rusty Whitt , coach Wes Kittley , coach of Texas Tech Red Raiders track and field Faculty Everett Ferguson , patristics scholar Douglas A. Foster , professor of church history Michael A. O'Donnell , professor of family studies Notes When James Cox's wife became ill, his brother, Alonzo B. Cox, filled in for him to finish the term. References ^ ^ As of June 30, 2024. ^ ^ ^ ^ ^ ^ [Sources: John C. Stevens, _No Ordinary University_, p. 248; J\n\n[DOC 7] its 2008 edition due to financial concerns. The university's national literary magazine, New Madrid with editor Ann Neelon, featured work from a range of nationally recognized authors and received acclaim from sources as diverse as La Bloga , a leading Hispanic journal, and New Pages , a leading national review of literary magazines. A lack of funding led to the suspension of publication in 2018. Presidents Presidents of the university include: John W. Carr, 1923–1926 Rainey T. Wells, 1926–1932 John W. Carr, 1933–1936 James H. Richmond, 1936–1945 Ralph H. Woods, 1945–1968 Harry M. Sparks, 1968–1973 Constantine W. Curris , 1973–1983 Kala M. Stroup, 1983–1990 James L. Booth, 1989–1990 (Acting) Ronald J. Kurth , 1990–1994 Samuel Kern Alexander , 1994–2001 Field\n\n[DOC 8] Politics of Texas Constitution and law Constitution of the United States Constitution of Texas Texas law Executive Governor Greg Abbott (R) Lieutenant Governor Dan Patrick (R) Attorney General Ken Paxton (R) Secretary of State John B. Scott (R) Comptroller of Public Accounts Glenn Hegar (R) Land Commissioner George P. Bush (R) Legislature Texas Senate President Dan Patrick (R) President pro tempore Donna Campbell (R) Texas House of Representatives Speaker Dade Phelan (R) Speaker pro tempore ( vacant ) Judiciary Texas Supreme Court Chief Justice Nathan Hecht (R) Justice Jimmy Blacklock (R) Justice Debra Lehrmann (R) Justice John P. Devine (R) Justice Rebeca Huddle (R) Justice Jane Bland (R) Justice Jeffrey S. Boyd (R) Justice J. Brett Busby (R) Justice Evan A\n\n\nSEARCH HISTORY: - Query: 'Austin mayor 2024' → 2 docs\n- Query: 'Kirk Watson education' → 2 docs\n- Query: 'University of Texas alumni presidents' → 4 docs\n\nFEEDBACK: Need to identify the mayor of Austin (state capital) in August 2024, confirm his first name, verify his university (UT Austin) and that at least one U.S. president also attended that university.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Kirk Watson University\",\n \"University of Texas presidents\",\n \"UT Austin Big 12 member\"\n ],\n \"feedback\": \"We need to verify the mayor’s alma mater (University of Texas) and confirm that at least one U.S. president also attended that university. Additionally, we must confirm that the city (Austin) hosts an outgoing or former full Big 12 member (UT Austin). The suggested queries target each missing piece.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2156, + "osl": 100, + "total_tokens": 2256, + "latency_ms": 9420.95, + "tokens_per_second": 10.61 + }, + "context": { + "kept_docs_count": 8, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "95064762-b9d3-4777-9564-012a6b36ddd2", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T02:50:24.897824Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: In August of 2024, what is the first name of the mayor of the U.S. state capital city who attended the same university as at least one U.S. president and whose city is home to an outgoing or former full member of the Big 12 Conference\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Founding members of the Big 12 from the Big Eight: Colorado Iowa State Kansas Kansas State Missouri Nebraska Oklahoma Oklahoma State Founding members of the Big 12 from the Southwest Conference: Baylor Texas Texas A&M Texas Tech Current members with the longest continuous association with the Big Eight Conference / Southwest Conference / Big 12 Conference. Institution Started Current Association In Continuous Years Note Kansas 1907 119 Iowa State 1908 118 Kansas State 1913 113 Baylor 1915 111 Texas Tech 1956 70 Oklahoma State 1958 68 Previously: 10 years in the Southwest Conference (1914–1924); 3 years in the MVIAA (1924–1927) TCU 2012 14 Previously: 73 years in the Southwest Conference (1923–1996) West Virginia 2012 14 BYU 2023 3 UCF 2023 3 Cincinnati 2023\n\n\nKEPT DOCUMENTS (context only):\n[8 documents already kept as relevant]\n[KEPT 1] foreign policy. That is not in our power. The resolution, however, has the power to divide Austin — and will.\" Electoral history 2024 2024 Austin mayoral election Candidate Votes % Kirk Watson 175,096 50.0041% Carmen Llanes Pulido 70,550 20.14% Kathie Tovo 58,280 16.64% Jeffrey Bowen 29,383 8.39% Do...\n[KEPT 2] Head of the Austin, Texas municipal government Mayor of Austin Flag of the City of Austin Incumbent Kirk Watson since January 6, 2023 Residence Private residence Term length Four years renewable once Inaugural holder Edwin Waller Formation 1840 Salary $134,191 Website austintexas . gov /mayor The ma...\n[KEPT 3] rsity of Bradford Doctor of Technology (DTec) USA 1975 Westminster College (Utah) Doctor of Laws (LLD) France 1976 University of Paris (Sorbonne) Docteur USA 1981 Wesleyan College (Georgia) Dr of Public Administration (DPA) USA 1982 Westminster College (Missouri) Doctor of Laws (DL) England 19 July ...\n[KEPT 4] her degree instead of four because she had taken two semesters off owing to her acting work. On 25 May 2014, she graduated from Brown University with a Bachelor of Arts degree in English literature. In 2016, she was appointed a visiting fellow at Lady Margaret Hall, Oxford . In 2023, she began a Mas...\n[KEPT 5] exas University of Virginia ( BS , MS ) University of Texas, Austin ( JD ) January 3, 2019 Dripping Springs Texas 22 Troy Nehls Republican ( 1968-04-07 ) April 7, 1968 (age 57) Sheriff of Fort Bend County Liberty University ( BA ) University of Houston–Downtown ( MA ) January 3, 2021 Richmond Texas ...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 918, + "osl": 2097, + "total_tokens": 3015, + "latency_ms": 10155.26, + "tokens_per_second": 206.49 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 8 + } + }, + { + "call_id": "13631bda-ed5b-4a7c-b249-b154c072a035", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T02:50:33.165738Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: In August of 2024, what is the first name of the mayor of the U.S. state capital city who attended the same university as at least one U.S. president and whose city is home to an outgoing or former full member of the Big 12 Conference\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] foreign policy. That is not in our power. The resolution, however, has the power to divide Austin — and will.\" Electoral history 2024 2024 Austin mayoral election Candidate Votes % Kirk Watson 175,096 50.0041% Carmen Llanes Pulido 70,550 20.14% Kathie Tovo 58,280 16.64% Jeffrey Bowen 29,383 8.39% Doug Greco 16,865 4.82% 2022 2022 Austin mayoral run-off Party Candidate Votes % Nonpartisan Kirk Watson 57,346 50.39 Nonpartisan Celia Israel 56,460 49.61 Total votes 113,806 100 2022 Austin mayoral election Party Candidate Votes % Nonpartisan Celia Israel 121,862 39.99 Nonpartisan Kirk Watson 106,508 34.95 Nonpartisan Jennifer Virden 56,189 18.44 Nonpartisan Phil Campero Brual 7,295 2.39 Nonpartisan Anthony Bradshaw 7,102 2.33 Nonpartisan Gary Spellman 5,781 1.90\n\n[DOC 2] Head of the Austin, Texas municipal government Mayor of Austin Flag of the City of Austin Incumbent Kirk Watson since January 6, 2023 Residence Private residence Term length Four years renewable once Inaugural holder Edwin Waller Formation 1840 Salary $134,191 Website austintexas . gov /mayor The mayor of Austin is the official head of the city of Austin in the U. S. state of Texas . The office was established in 1840 after Austin incorporated as a city in 1839. The mayor of Austin is elected to a four-year term and limited to serving no more than two terms. Kirk Watson took office as mayor on January 6, 2023, for a third term and was re-elected to a fourth term in 2024, having served as mayor from 1997 to 2001. Duties and powers Austin has a council–manager\n\n[DOC 3] rsity of Bradford Doctor of Technology (DTec) USA 1975 Westminster College (Utah) Doctor of Laws (LLD) France 1976 University of Paris (Sorbonne) Docteur USA 1981 Wesleyan College (Georgia) Dr of Public Administration (DPA) USA 1982 Westminster College (Missouri) Doctor of Laws (DL) England 19 July 1985 University of Kent Doctor of Laws (LL. D) Canada 7 June 1991 University of Calgary Doctor of Laws (LL. D) England 1994 Goldsmiths, University of London Honorary Fellow USA 1994 Bellarmine College (Kentucky) Doctor of Laws (HLD) England 21 June 1997 Open University Doctor of the University (D. Univ) Wales 1998 University of Wales Doctor of Laws (LL. D) England 18 July 2001 University of Greenwich Doctor of Laws (LL. D) England — Royal College of Organists Fell\n\n[DOC 4] her degree instead of four because she had taken two semesters off owing to her acting work. On 25 May 2014, she graduated from Brown University with a Bachelor of Arts degree in English literature. In 2016, she was appointed a visiting fellow at Lady Margaret Hall, Oxford . In 2023, she began a Master of Studies in creative writing at New College, Oxford . Acting career 1999–2009: Harry Potter and worldwide recognition In 1999, casting began for Harry Potter and the Philosopher's Stone , the film adaptation of British author J. K. Rowling 's best-selling novel . Casting agents found Watson through her Oxford theatre teacher. She had acted in school plays, but had no film acting experience. Her first audition took place when she was nine years old. Although\n\n[DOC 5] exas University of Virginia ( BS , MS ) University of Texas, Austin ( JD ) January 3, 2019 Dripping Springs Texas 22 Troy Nehls Republican ( 1968-04-07 ) April 7, 1968 (age 57) Sheriff of Fort Bend County Liberty University ( BA ) University of Houston–Downtown ( MA ) January 3, 2021 Richmond Texas 23 Tony Gonzales Republican ( 1980-10-10 ) October 10, 1980 (age 45) United States Navy Chaminade University ( AA ) Excelsior College ( BS ) American Public University ( MA ) January 3, 2021 San Antonio Texas 24 Beth Van Duyne Republican ( 1970-11-16 ) November 16, 1970 (age 55) Mayor of Irving Cornell University ( BA ) January 3, 2021 Irving Texas 25 Roger Williams Republican ( 1949-09-13 ) September 13, 1949 (age 76) Secretary of State of Texas Texas Christian U\n\n[DOC 6] football player Raymond Radway , football player Daryl Richardson , football player Bernard Scott , football player Jeev Milkha Singh (1996), golfer Gilbert Tuhabonye , runner and author Charcandrick West , football player Allen Wilson , football coach Earl Young , runner Art Briles , former head coach at Baylor University Rusty Whitt , coach Wes Kittley , coach of Texas Tech Red Raiders track and field Faculty Everett Ferguson , patristics scholar Douglas A. Foster , professor of church history Michael A. O'Donnell , professor of family studies Notes When James Cox's wife became ill, his brother, Alonzo B. Cox, filled in for him to finish the term. References ^ ^ As of June 30, 2024. ^ ^ ^ ^ ^ ^ [Sources: John C. Stevens, _No Ordinary University_, p. 248; J\n\n[DOC 7] its 2008 edition due to financial concerns. The university's national literary magazine, New Madrid with editor Ann Neelon, featured work from a range of nationally recognized authors and received acclaim from sources as diverse as La Bloga , a leading Hispanic journal, and New Pages , a leading national review of literary magazines. A lack of funding led to the suspension of publication in 2018. Presidents Presidents of the university include: John W. Carr, 1923–1926 Rainey T. Wells, 1926–1932 John W. Carr, 1933–1936 James H. Richmond, 1936–1945 Ralph H. Woods, 1945–1968 Harry M. Sparks, 1968–1973 Constantine W. Curris , 1973–1983 Kala M. Stroup, 1983–1990 James L. Booth, 1989–1990 (Acting) Ronald J. Kurth , 1990–1994 Samuel Kern Alexander , 1994–2001 Field\n\n[DOC 8] Politics of Texas Constitution and law Constitution of the United States Constitution of Texas Texas law Executive Governor Greg Abbott (R) Lieutenant Governor Dan Patrick (R) Attorney General Ken Paxton (R) Secretary of State John B. Scott (R) Comptroller of Public Accounts Glenn Hegar (R) Land Commissioner George P. Bush (R) Legislature Texas Senate President Dan Patrick (R) President pro tempore Donna Campbell (R) Texas House of Representatives Speaker Dade Phelan (R) Speaker pro tempore ( vacant ) Judiciary Texas Supreme Court Chief Justice Nathan Hecht (R) Justice Jimmy Blacklock (R) Justice Debra Lehrmann (R) Justice John P. Devine (R) Justice Rebeca Huddle (R) Justice Jane Bland (R) Justice Jeffrey S. Boyd (R) Justice J. Brett Busby (R) Justice Evan A\n\n[DOC 9] Founding members of the Big 12 from the Big Eight: Colorado Iowa State Kansas Kansas State Missouri Nebraska Oklahoma Oklahoma State Founding members of the Big 12 from the Southwest Conference: Baylor Texas Texas A&M Texas Tech Current members with the longest continuous association with the Big Eight Conference / Southwest Conference / Big 12 Conference. Institution Started Current Association In Continuous Years Note Kansas 1907 119 Iowa State 1908 118 Kansas State 1913 113 Baylor 1915 111 Texas Tech 1956 70 Oklahoma State 1958 68 Previously: 10 years in the Southwest Conference (1914–1924); 3 years in the MVIAA (1924–1927) TCU 2012 14 Previously: 73 years in the Southwest Conference (1923–1996) West Virginia 2012 14 BYU 2023 3 UCF 2023 3 Cincinnati 2023\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents identify the mayor of Austin (a U.S. state capital) as Kirk Watson (DOC 2) and note that Austin is home to the University of Texas, a Big 12 full member (DOC 9). However, none of the documents provide information about Kirk Watson's alma mater or show that he attended a university also attended by any U.S. president. This missing education link means we cannot confirm the required condition, so the available documents are insufficient to answer the question.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2163, + "osl": 1725, + "total_tokens": 3888, + "latency_ms": 8266.94, + "tokens_per_second": 208.66 + }, + "context": { + "kept_docs_count": 9, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "ec94e582-504c-435c-a795-ab3e7581f676", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T02:50:39.600840Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: In August of 2024, what is the first name of the mayor of the U.S. state capital city who attended the same university as at least one U.S. president and whose city is home to an outgoing or former full member of the Big 12 Conference\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] foreign policy. That is not in our power. The resolution, however, has the power to divide Austin — and will.\" Electoral history 2024 2024 Austin mayoral election Candidate Votes % Kirk Watson 175,096 50.0041% Carmen Llanes Pulido 70,550 20.14% Kathie Tovo 58,280 16.64% Jeffrey Bowen 29,383 8.39% Doug Greco 16,865 4.82% 2022 2022 Austin mayoral run-off Party Candidate Votes % Nonpartisan Kirk Watson 57,346 50.39 Nonpartisan Celia Israel 56,460 49.61 Total votes 113,806 100 2022 Austin mayoral election Party Candidate Votes % Nonpartisan Celia Israel 121,862 39.99 Nonpartisan Kirk Watson 106,508 34.95 Nonpartisan Jennifer Virden 56,189 18.44 Nonpartisan Phil Campero Brual 7,295 2.39 Nonpartisan Anthony Bradshaw 7,102 2.33 Nonpartisan Gary Spellman 5,781 1.90\n\n[DOC 2] Head of the Austin, Texas municipal government Mayor of Austin Flag of the City of Austin Incumbent Kirk Watson since January 6, 2023 Residence Private residence Term length Four years renewable once Inaugural holder Edwin Waller Formation 1840 Salary $134,191 Website austintexas . gov /mayor The mayor of Austin is the official head of the city of Austin in the U. S. state of Texas . The office was established in 1840 after Austin incorporated as a city in 1839. The mayor of Austin is elected to a four-year term and limited to serving no more than two terms. Kirk Watson took office as mayor on January 6, 2023, for a third term and was re-elected to a fourth term in 2024, having served as mayor from 1997 to 2001. Duties and powers Austin has a council–manager\n\n[DOC 3] rsity of Bradford Doctor of Technology (DTec) USA 1975 Westminster College (Utah) Doctor of Laws (LLD) France 1976 University of Paris (Sorbonne) Docteur USA 1981 Wesleyan College (Georgia) Dr of Public Administration (DPA) USA 1982 Westminster College (Missouri) Doctor of Laws (DL) England 19 July 1985 University of Kent Doctor of Laws (LL. D) Canada 7 June 1991 University of Calgary Doctor of Laws (LL. D) England 1994 Goldsmiths, University of London Honorary Fellow USA 1994 Bellarmine College (Kentucky) Doctor of Laws (HLD) England 21 June 1997 Open University Doctor of the University (D. Univ) Wales 1998 University of Wales Doctor of Laws (LL. D) England 18 July 2001 University of Greenwich Doctor of Laws (LL. D) England — Royal College of Organists Fell\n\n[DOC 4] her degree instead of four because she had taken two semesters off owing to her acting work. On 25 May 2014, she graduated from Brown University with a Bachelor of Arts degree in English literature. In 2016, she was appointed a visiting fellow at Lady Margaret Hall, Oxford . In 2023, she began a Master of Studies in creative writing at New College, Oxford . Acting career 1999–2009: Harry Potter and worldwide recognition In 1999, casting began for Harry Potter and the Philosopher's Stone , the film adaptation of British author J. K. Rowling 's best-selling novel . Casting agents found Watson through her Oxford theatre teacher. She had acted in school plays, but had no film acting experience. Her first audition took place when she was nine years old. Although\n\n[DOC 5] exas University of Virginia ( BS , MS ) University of Texas, Austin ( JD ) January 3, 2019 Dripping Springs Texas 22 Troy Nehls Republican ( 1968-04-07 ) April 7, 1968 (age 57) Sheriff of Fort Bend County Liberty University ( BA ) University of Houston–Downtown ( MA ) January 3, 2021 Richmond Texas 23 Tony Gonzales Republican ( 1980-10-10 ) October 10, 1980 (age 45) United States Navy Chaminade University ( AA ) Excelsior College ( BS ) American Public University ( MA ) January 3, 2021 San Antonio Texas 24 Beth Van Duyne Republican ( 1970-11-16 ) November 16, 1970 (age 55) Mayor of Irving Cornell University ( BA ) January 3, 2021 Irving Texas 25 Roger Williams Republican ( 1949-09-13 ) September 13, 1949 (age 76) Secretary of State of Texas Texas Christian U\n\n[DOC 6] football player Raymond Radway , football player Daryl Richardson , football player Bernard Scott , football player Jeev Milkha Singh (1996), golfer Gilbert Tuhabonye , runner and author Charcandrick West , football player Allen Wilson , football coach Earl Young , runner Art Briles , former head coach at Baylor University Rusty Whitt , coach Wes Kittley , coach of Texas Tech Red Raiders track and field Faculty Everett Ferguson , patristics scholar Douglas A. Foster , professor of church history Michael A. O'Donnell , professor of family studies Notes When James Cox's wife became ill, his brother, Alonzo B. Cox, filled in for him to finish the term. References ^ ^ As of June 30, 2024. ^ ^ ^ ^ ^ ^ [Sources: John C. Stevens, _No Ordinary University_, p. 248; J\n\n[DOC 7] its 2008 edition due to financial concerns. The university's national literary magazine, New Madrid with editor Ann Neelon, featured work from a range of nationally recognized authors and received acclaim from sources as diverse as La Bloga , a leading Hispanic journal, and New Pages , a leading national review of literary magazines. A lack of funding led to the suspension of publication in 2018. Presidents Presidents of the university include: John W. Carr, 1923–1926 Rainey T. Wells, 1926–1932 John W. Carr, 1933–1936 James H. Richmond, 1936–1945 Ralph H. Woods, 1945–1968 Harry M. Sparks, 1968–1973 Constantine W. Curris , 1973–1983 Kala M. Stroup, 1983–1990 James L. Booth, 1989–1990 (Acting) Ronald J. Kurth , 1990–1994 Samuel Kern Alexander , 1994–2001 Field\n\n[DOC 8] Politics of Texas Constitution and law Constitution of the United States Constitution of Texas Texas law Executive Governor Greg Abbott (R) Lieutenant Governor Dan Patrick (R) Attorney General Ken Paxton (R) Secretary of State John B. Scott (R) Comptroller of Public Accounts Glenn Hegar (R) Land Commissioner George P. Bush (R) Legislature Texas Senate President Dan Patrick (R) President pro tempore Donna Campbell (R) Texas House of Representatives Speaker Dade Phelan (R) Speaker pro tempore ( vacant ) Judiciary Texas Supreme Court Chief Justice Nathan Hecht (R) Justice Jimmy Blacklock (R) Justice Debra Lehrmann (R) Justice John P. Devine (R) Justice Rebeca Huddle (R) Justice Jane Bland (R) Justice Jeffrey S. Boyd (R) Justice J. Brett Busby (R) Justice Evan A\n\n[DOC 9] Founding members of the Big 12 from the Big Eight: Colorado Iowa State Kansas Kansas State Missouri Nebraska Oklahoma Oklahoma State Founding members of the Big 12 from the Southwest Conference: Baylor Texas Texas A&M Texas Tech Current members with the longest continuous association with the Big Eight Conference / Southwest Conference / Big 12 Conference. Institution Started Current Association In Continuous Years Note Kansas 1907 119 Iowa State 1908 118 Kansas State 1913 113 Baylor 1915 111 Texas Tech 1956 70 Oklahoma State 1958 68 Previously: 10 years in the Southwest Conference (1914–1924); 3 years in the MVIAA (1924–1927) TCU 2012 14 Previously: 73 years in the Southwest Conference (1923–1996) West Virginia 2012 14 BYU 2023 3 UCF 2023 3 Cincinnati 2023\n\n\nSEARCH HISTORY: - Query: 'Austin mayor 2024' → 2 docs\n- Query: 'Kirk Watson education' → 2 docs\n- Query: 'University of Texas alumni presidents' → 4 docs\n- Query: 'Kirk Watson University' → 0 docs\n- Query: 'University of Texas presidents' → 0 docs\n- Query: 'UT Austin Big 12 member' → 1 docs\n\nFEEDBACK: Need to identify the mayor of Austin (state capital) in August 2024, confirm his first name, verify his university (UT Austin) and that at least one U.S. president also attended that university.\nWe need to verify the mayor’s alma mater (University of Texas) and confirm that at least one U.S. president also attended that university. Additionally, we must confirm that the city (Austin) hosts an outgoing or former full Big 12 member (UT Austin). The suggested queries target each missing piece.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Kirk Watson education\",\n \"Lyndon B. Johnson University of Texas\",\n \"University of Texas Big 12\"\n ],\n \"feedback\": \"Need to confirm mayor Kirk Watson attended the University of Texas at Austin, verify that President Lyndon B. Johnson also attended that university, and show that the university is a full member of the Big 12 Conference, establishing the link to the city of Austin.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2507, + "osl": 436, + "total_tokens": 2943, + "latency_ms": 6434.39, + "tokens_per_second": 67.76 + }, + "context": { + "kept_docs_count": 9, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "ce4a524f-16d5-47b2-92c3-a911e3aa70b4", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T02:50:44.749140Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: In August of 2024, what is the first name of the mayor of the U.S. state capital city who attended the same university as at least one U.S. president and whose city is home to an outgoing or former full member of the Big 12 Conference\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] University (transferred) Harvard University Stanford Graduate School of Business (auditor) Lyndon B. Johnson Johnson City High School Southwest Texas State Teachers College (now Texas State University ) Georgetown University Law Center (withdrew) Richard Nixon Whittier High School Whittier College Duke University School of Law Gerald Ford Grand Rapids South High School University of Michigan University of Michigan Law School (transferred) Yale Law School Jimmy Carter Plains High School Georgia Southwestern College (transferred) Georgia Institute of Technology (transferred) United States Naval Academy Union College (Postgraduate Nuclear Physics Course Program) Ronald Reagan Dixon High School Eureka College none George H. W. Bush Phillips Academy Yale Univers\n\n\nKEPT DOCUMENTS (context only):\n[9 documents already kept as relevant]\n[KEPT 1] foreign policy. That is not in our power. The resolution, however, has the power to divide Austin — and will.\" Electoral history 2024 2024 Austin mayoral election Candidate Votes % Kirk Watson 175,096 50.0041% Carmen Llanes Pulido 70,550 20.14% Kathie Tovo 58,280 16.64% Jeffrey Bowen 29,383 8.39% Do...\n[KEPT 2] Head of the Austin, Texas municipal government Mayor of Austin Flag of the City of Austin Incumbent Kirk Watson since January 6, 2023 Residence Private residence Term length Four years renewable once Inaugural holder Edwin Waller Formation 1840 Salary $134,191 Website austintexas . gov /mayor The ma...\n[KEPT 3] rsity of Bradford Doctor of Technology (DTec) USA 1975 Westminster College (Utah) Doctor of Laws (LLD) France 1976 University of Paris (Sorbonne) Docteur USA 1981 Wesleyan College (Georgia) Dr of Public Administration (DPA) USA 1982 Westminster College (Missouri) Doctor of Laws (DL) England 19 July ...\n[KEPT 4] her degree instead of four because she had taken two semesters off owing to her acting work. On 25 May 2014, she graduated from Brown University with a Bachelor of Arts degree in English literature. In 2016, she was appointed a visiting fellow at Lady Margaret Hall, Oxford . In 2023, she began a Mas...\n[KEPT 5] exas University of Virginia ( BS , MS ) University of Texas, Austin ( JD ) January 3, 2019 Dripping Springs Texas 22 Troy Nehls Republican ( 1968-04-07 ) April 7, 1968 (age 57) Sheriff of Fort Bend County Liberty University ( BA ) University of Houston–Downtown ( MA ) January 3, 2021 Richmond Texas ...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 862, + "osl": 543, + "total_tokens": 1405, + "latency_ms": 4514.49, + "tokens_per_second": 120.28 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 9 + } + }, + { + "call_id": "a8dd8f27-d95c-427a-9579-8c04d691d258", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T02:50:51.672637Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: In August of 2024, what is the first name of the mayor of the U.S. state capital city who attended the same university as at least one U.S. president and whose city is home to an outgoing or former full member of the Big 12 Conference\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] foreign policy. That is not in our power. The resolution, however, has the power to divide Austin — and will.\" Electoral history 2024 2024 Austin mayoral election Candidate Votes % Kirk Watson 175,096 50.0041% Carmen Llanes Pulido 70,550 20.14% Kathie Tovo 58,280 16.64% Jeffrey Bowen 29,383 8.39% Doug Greco 16,865 4.82% 2022 2022 Austin mayoral run-off Party Candidate Votes % Nonpartisan Kirk Watson 57,346 50.39 Nonpartisan Celia Israel 56,460 49.61 Total votes 113,806 100 2022 Austin mayoral election Party Candidate Votes % Nonpartisan Celia Israel 121,862 39.99 Nonpartisan Kirk Watson 106,508 34.95 Nonpartisan Jennifer Virden 56,189 18.44 Nonpartisan Phil Campero Brual 7,295 2.39 Nonpartisan Anthony Bradshaw 7,102 2.33 Nonpartisan Gary Spellman 5,781 1.90\n\n[DOC 2] Head of the Austin, Texas municipal government Mayor of Austin Flag of the City of Austin Incumbent Kirk Watson since January 6, 2023 Residence Private residence Term length Four years renewable once Inaugural holder Edwin Waller Formation 1840 Salary $134,191 Website austintexas . gov /mayor The mayor of Austin is the official head of the city of Austin in the U. S. state of Texas . The office was established in 1840 after Austin incorporated as a city in 1839. The mayor of Austin is elected to a four-year term and limited to serving no more than two terms. Kirk Watson took office as mayor on January 6, 2023, for a third term and was re-elected to a fourth term in 2024, having served as mayor from 1997 to 2001. Duties and powers Austin has a council–manager\n\n[DOC 3] rsity of Bradford Doctor of Technology (DTec) USA 1975 Westminster College (Utah) Doctor of Laws (LLD) France 1976 University of Paris (Sorbonne) Docteur USA 1981 Wesleyan College (Georgia) Dr of Public Administration (DPA) USA 1982 Westminster College (Missouri) Doctor of Laws (DL) England 19 July 1985 University of Kent Doctor of Laws (LL. D) Canada 7 June 1991 University of Calgary Doctor of Laws (LL. D) England 1994 Goldsmiths, University of London Honorary Fellow USA 1994 Bellarmine College (Kentucky) Doctor of Laws (HLD) England 21 June 1997 Open University Doctor of the University (D. Univ) Wales 1998 University of Wales Doctor of Laws (LL. D) England 18 July 2001 University of Greenwich Doctor of Laws (LL. D) England — Royal College of Organists Fell\n\n[DOC 4] her degree instead of four because she had taken two semesters off owing to her acting work. On 25 May 2014, she graduated from Brown University with a Bachelor of Arts degree in English literature. In 2016, she was appointed a visiting fellow at Lady Margaret Hall, Oxford . In 2023, she began a Master of Studies in creative writing at New College, Oxford . Acting career 1999–2009: Harry Potter and worldwide recognition In 1999, casting began for Harry Potter and the Philosopher's Stone , the film adaptation of British author J. K. Rowling 's best-selling novel . Casting agents found Watson through her Oxford theatre teacher. She had acted in school plays, but had no film acting experience. Her first audition took place when she was nine years old. Although\n\n[DOC 5] exas University of Virginia ( BS , MS ) University of Texas, Austin ( JD ) January 3, 2019 Dripping Springs Texas 22 Troy Nehls Republican ( 1968-04-07 ) April 7, 1968 (age 57) Sheriff of Fort Bend County Liberty University ( BA ) University of Houston–Downtown ( MA ) January 3, 2021 Richmond Texas 23 Tony Gonzales Republican ( 1980-10-10 ) October 10, 1980 (age 45) United States Navy Chaminade University ( AA ) Excelsior College ( BS ) American Public University ( MA ) January 3, 2021 San Antonio Texas 24 Beth Van Duyne Republican ( 1970-11-16 ) November 16, 1970 (age 55) Mayor of Irving Cornell University ( BA ) January 3, 2021 Irving Texas 25 Roger Williams Republican ( 1949-09-13 ) September 13, 1949 (age 76) Secretary of State of Texas Texas Christian U\n\n[DOC 6] football player Raymond Radway , football player Daryl Richardson , football player Bernard Scott , football player Jeev Milkha Singh (1996), golfer Gilbert Tuhabonye , runner and author Charcandrick West , football player Allen Wilson , football coach Earl Young , runner Art Briles , former head coach at Baylor University Rusty Whitt , coach Wes Kittley , coach of Texas Tech Red Raiders track and field Faculty Everett Ferguson , patristics scholar Douglas A. Foster , professor of church history Michael A. O'Donnell , professor of family studies Notes When James Cox's wife became ill, his brother, Alonzo B. Cox, filled in for him to finish the term. References ^ ^ As of June 30, 2024. ^ ^ ^ ^ ^ ^ [Sources: John C. Stevens, _No Ordinary University_, p. 248; J\n\n[DOC 7] its 2008 edition due to financial concerns. The university's national literary magazine, New Madrid with editor Ann Neelon, featured work from a range of nationally recognized authors and received acclaim from sources as diverse as La Bloga , a leading Hispanic journal, and New Pages , a leading national review of literary magazines. A lack of funding led to the suspension of publication in 2018. Presidents Presidents of the university include: John W. Carr, 1923–1926 Rainey T. Wells, 1926–1932 John W. Carr, 1933–1936 James H. Richmond, 1936–1945 Ralph H. Woods, 1945–1968 Harry M. Sparks, 1968–1973 Constantine W. Curris , 1973–1983 Kala M. Stroup, 1983–1990 James L. Booth, 1989–1990 (Acting) Ronald J. Kurth , 1990–1994 Samuel Kern Alexander , 1994–2001 Field\n\n[DOC 8] Politics of Texas Constitution and law Constitution of the United States Constitution of Texas Texas law Executive Governor Greg Abbott (R) Lieutenant Governor Dan Patrick (R) Attorney General Ken Paxton (R) Secretary of State John B. Scott (R) Comptroller of Public Accounts Glenn Hegar (R) Land Commissioner George P. Bush (R) Legislature Texas Senate President Dan Patrick (R) President pro tempore Donna Campbell (R) Texas House of Representatives Speaker Dade Phelan (R) Speaker pro tempore ( vacant ) Judiciary Texas Supreme Court Chief Justice Nathan Hecht (R) Justice Jimmy Blacklock (R) Justice Debra Lehrmann (R) Justice John P. Devine (R) Justice Rebeca Huddle (R) Justice Jane Bland (R) Justice Jeffrey S. Boyd (R) Justice J. Brett Busby (R) Justice Evan A\n\n[DOC 9] Founding members of the Big 12 from the Big Eight: Colorado Iowa State Kansas Kansas State Missouri Nebraska Oklahoma Oklahoma State Founding members of the Big 12 from the Southwest Conference: Baylor Texas Texas A&M Texas Tech Current members with the longest continuous association with the Big Eight Conference / Southwest Conference / Big 12 Conference. Institution Started Current Association In Continuous Years Note Kansas 1907 119 Iowa State 1908 118 Kansas State 1913 113 Baylor 1915 111 Texas Tech 1956 70 Oklahoma State 1958 68 Previously: 10 years in the Southwest Conference (1914–1924); 3 years in the MVIAA (1924–1927) TCU 2012 14 Previously: 73 years in the Southwest Conference (1923–1996) West Virginia 2012 14 BYU 2023 3 UCF 2023 3 Cincinnati 2023\n\n[DOC 10] University (transferred) Harvard University Stanford Graduate School of Business (auditor) Lyndon B. Johnson Johnson City High School Southwest Texas State Teachers College (now Texas State University ) Georgetown University Law Center (withdrew) Richard Nixon Whittier High School Whittier College Duke University School of Law Gerald Ford Grand Rapids South High School University of Michigan University of Michigan Law School (transferred) Yale Law School Jimmy Carter Plains High School Georgia Southwestern College (transferred) Georgia Institute of Technology (transferred) United States Naval Academy Union College (Postgraduate Nuclear Physics Course Program) Ronald Reagan Dixon High School Eureka College none George H. W. Bush Phillips Academy Yale Univers\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents provide information about the mayor of Austin (Kirk Watson) and list Big 12 member schools, but they do not contain any details about Watson's education or confirm that he attended the same university as a U.S. president. They also do not specify that Austin is home to an outgoing or former full member of the Big 12 Conference (UT Austin is a current member, not outgoing or former). Therefore, the required facts to answer the question are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2304, + "osl": 1334, + "total_tokens": 3638, + "latency_ms": 6922.66, + "tokens_per_second": 192.7 + }, + "context": { + "kept_docs_count": 10, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "2867be2d-eee0-46ab-8628-59c6d6b53c4c", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T02:50:59.701408Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: In August of 2024, what is the first name of the mayor of the U.S. state capital city who attended the same university as at least one U.S. president and whose city is home to an outgoing or former full member of the Big 12 Conference\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] foreign policy. That is not in our power. The resolution, however, has the power to divide Austin — and will.\" Electoral history 2024 2024 Austin mayoral election Candidate Votes % Kirk Watson 175,096 50.0041% Carmen Llanes Pulido 70,550 20.14% Kathie Tovo 58,280 16.64% Jeffrey Bowen 29,383 8.39% Doug Greco 16,865 4.82% 2022 2022 Austin mayoral run-off Party Candidate Votes % Nonpartisan Kirk Watson 57,346 50.39 Nonpartisan Celia Israel 56,460 49.61 Total votes 113,806 100 2022 Austin mayoral election Party Candidate Votes % Nonpartisan Celia Israel 121,862 39.99 Nonpartisan Kirk Watson 106,508 34.95 Nonpartisan Jennifer Virden 56,189 18.44 Nonpartisan Phil Campero Brual 7,295 2.39 Nonpartisan Anthony Bradshaw 7,102 2.33 Nonpartisan Gary Spellman 5,781 1.90\n\n[DOC 2] Head of the Austin, Texas municipal government Mayor of Austin Flag of the City of Austin Incumbent Kirk Watson since January 6, 2023 Residence Private residence Term length Four years renewable once Inaugural holder Edwin Waller Formation 1840 Salary $134,191 Website austintexas . gov /mayor The mayor of Austin is the official head of the city of Austin in the U. S. state of Texas . The office was established in 1840 after Austin incorporated as a city in 1839. The mayor of Austin is elected to a four-year term and limited to serving no more than two terms. Kirk Watson took office as mayor on January 6, 2023, for a third term and was re-elected to a fourth term in 2024, having served as mayor from 1997 to 2001. Duties and powers Austin has a council–manager\n\n[DOC 3] rsity of Bradford Doctor of Technology (DTec) USA 1975 Westminster College (Utah) Doctor of Laws (LLD) France 1976 University of Paris (Sorbonne) Docteur USA 1981 Wesleyan College (Georgia) Dr of Public Administration (DPA) USA 1982 Westminster College (Missouri) Doctor of Laws (DL) England 19 July 1985 University of Kent Doctor of Laws (LL. D) Canada 7 June 1991 University of Calgary Doctor of Laws (LL. D) England 1994 Goldsmiths, University of London Honorary Fellow USA 1994 Bellarmine College (Kentucky) Doctor of Laws (HLD) England 21 June 1997 Open University Doctor of the University (D. Univ) Wales 1998 University of Wales Doctor of Laws (LL. D) England 18 July 2001 University of Greenwich Doctor of Laws (LL. D) England — Royal College of Organists Fell\n\n[DOC 4] her degree instead of four because she had taken two semesters off owing to her acting work. On 25 May 2014, she graduated from Brown University with a Bachelor of Arts degree in English literature. In 2016, she was appointed a visiting fellow at Lady Margaret Hall, Oxford . In 2023, she began a Master of Studies in creative writing at New College, Oxford . Acting career 1999–2009: Harry Potter and worldwide recognition In 1999, casting began for Harry Potter and the Philosopher's Stone , the film adaptation of British author J. K. Rowling 's best-selling novel . Casting agents found Watson through her Oxford theatre teacher. She had acted in school plays, but had no film acting experience. Her first audition took place when she was nine years old. Although\n\n[DOC 5] exas University of Virginia ( BS , MS ) University of Texas, Austin ( JD ) January 3, 2019 Dripping Springs Texas 22 Troy Nehls Republican ( 1968-04-07 ) April 7, 1968 (age 57) Sheriff of Fort Bend County Liberty University ( BA ) University of Houston–Downtown ( MA ) January 3, 2021 Richmond Texas 23 Tony Gonzales Republican ( 1980-10-10 ) October 10, 1980 (age 45) United States Navy Chaminade University ( AA ) Excelsior College ( BS ) American Public University ( MA ) January 3, 2021 San Antonio Texas 24 Beth Van Duyne Republican ( 1970-11-16 ) November 16, 1970 (age 55) Mayor of Irving Cornell University ( BA ) January 3, 2021 Irving Texas 25 Roger Williams Republican ( 1949-09-13 ) September 13, 1949 (age 76) Secretary of State of Texas Texas Christian U\n\n[DOC 6] football player Raymond Radway , football player Daryl Richardson , football player Bernard Scott , football player Jeev Milkha Singh (1996), golfer Gilbert Tuhabonye , runner and author Charcandrick West , football player Allen Wilson , football coach Earl Young , runner Art Briles , former head coach at Baylor University Rusty Whitt , coach Wes Kittley , coach of Texas Tech Red Raiders track and field Faculty Everett Ferguson , patristics scholar Douglas A. Foster , professor of church history Michael A. O'Donnell , professor of family studies Notes When James Cox's wife became ill, his brother, Alonzo B. Cox, filled in for him to finish the term. References ^ ^ As of June 30, 2024. ^ ^ ^ ^ ^ ^ [Sources: John C. Stevens, _No Ordinary University_, p. 248; J\n\n[DOC 7] its 2008 edition due to financial concerns. The university's national literary magazine, New Madrid with editor Ann Neelon, featured work from a range of nationally recognized authors and received acclaim from sources as diverse as La Bloga , a leading Hispanic journal, and New Pages , a leading national review of literary magazines. A lack of funding led to the suspension of publication in 2018. Presidents Presidents of the university include: John W. Carr, 1923–1926 Rainey T. Wells, 1926–1932 John W. Carr, 1933–1936 James H. Richmond, 1936–1945 Ralph H. Woods, 1945–1968 Harry M. Sparks, 1968–1973 Constantine W. Curris , 1973–1983 Kala M. Stroup, 1983–1990 James L. Booth, 1989–1990 (Acting) Ronald J. Kurth , 1990–1994 Samuel Kern Alexander , 1994–2001 Field\n\n[DOC 8] Politics of Texas Constitution and law Constitution of the United States Constitution of Texas Texas law Executive Governor Greg Abbott (R) Lieutenant Governor Dan Patrick (R) Attorney General Ken Paxton (R) Secretary of State John B. Scott (R) Comptroller of Public Accounts Glenn Hegar (R) Land Commissioner George P. Bush (R) Legislature Texas Senate President Dan Patrick (R) President pro tempore Donna Campbell (R) Texas House of Representatives Speaker Dade Phelan (R) Speaker pro tempore ( vacant ) Judiciary Texas Supreme Court Chief Justice Nathan Hecht (R) Justice Jimmy Blacklock (R) Justice Debra Lehrmann (R) Justice John P. Devine (R) Justice Rebeca Huddle (R) Justice Jane Bland (R) Justice Jeffrey S. Boyd (R) Justice J. Brett Busby (R) Justice Evan A\n\n[DOC 9] Founding members of the Big 12 from the Big Eight: Colorado Iowa State Kansas Kansas State Missouri Nebraska Oklahoma Oklahoma State Founding members of the Big 12 from the Southwest Conference: Baylor Texas Texas A&M Texas Tech Current members with the longest continuous association with the Big Eight Conference / Southwest Conference / Big 12 Conference. Institution Started Current Association In Continuous Years Note Kansas 1907 119 Iowa State 1908 118 Kansas State 1913 113 Baylor 1915 111 Texas Tech 1956 70 Oklahoma State 1958 68 Previously: 10 years in the Southwest Conference (1914–1924); 3 years in the MVIAA (1924–1927) TCU 2012 14 Previously: 73 years in the Southwest Conference (1923–1996) West Virginia 2012 14 BYU 2023 3 UCF 2023 3 Cincinnati 2023\n\n[DOC 10] University (transferred) Harvard University Stanford Graduate School of Business (auditor) Lyndon B. Johnson Johnson City High School Southwest Texas State Teachers College (now Texas State University ) Georgetown University Law Center (withdrew) Richard Nixon Whittier High School Whittier College Duke University School of Law Gerald Ford Grand Rapids South High School University of Michigan University of Michigan Law School (transferred) Yale Law School Jimmy Carter Plains High School Georgia Southwestern College (transferred) Georgia Institute of Technology (transferred) United States Naval Academy Union College (Postgraduate Nuclear Physics Course Program) Ronald Reagan Dixon High School Eureka College none George H. W. Bush Phillips Academy Yale Univers\n\n\nSEARCH HISTORY: - Query: 'Austin mayor 2024' → 2 docs\n- Query: 'Kirk Watson education' → 2 docs\n- Query: 'University of Texas alumni presidents' → 4 docs\n- Query: 'Kirk Watson University' → 0 docs\n- Query: 'University of Texas presidents' → 0 docs\n- Query: 'UT Austin Big 12 member' → 1 docs\n- Query: 'Kirk Watson education' → 0 docs\n- Query: 'Lyndon B. Johnson University of Texas' → 1 docs\n- Query: 'University of Texas Big 12' → 0 docs\n\nFEEDBACK: Need to identify the mayor of Austin (state capital) in August 2024, confirm his first name, verify his university (UT Austin) and that at least one U.S. president also attended that university.\nWe need to verify the mayor’s alma mater (University of Texas) and confirm that at least one U.S. president also attended that university. Additionally, we must confirm that the city (Austin) hosts an outgoing or former full Big 12 member (UT Austin). The suggested queries target each missing piece.\nNeed to confirm mayor Kirk Watson attended the University of Texas at Austin, verify that President Lyndon B. Johnson also attended that university, and show that the university is a full member of the Big 12 Conference, establishing the link to the city of Austin.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Kirk Watson alma mater\",\n \"Lyndon B. Johnson University of Texas\",\n \"University of Texas Austin Big 12 member\"\n ],\n \"feedback\": \"Need to confirm mayor's first name and his university, verify a U.S. president attended the same university, and show that the university is (or was) a full Big 12 member (now outgoing).\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2749, + "osl": 597, + "total_tokens": 3346, + "latency_ms": 8028.16, + "tokens_per_second": 74.36 + }, + "context": { + "kept_docs_count": 10, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "91124605-1988-4ab5-9bd2-f6f78f2ef1b0", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T02:51:13.092072Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: In August of 2024, what is the first name of the mayor of the U.S. state capital city who attended the same university as at least one U.S. president and whose city is home to an outgoing or former full member of the Big 12 Conference\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] ^ a b ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ Updated 2013 by Sig Gissler. ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ Office of Undergraduate Admissions site about Campus Life Archived April 19, 2012, at the Wayback Machine . Retrieved September 12, 2007. ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ Baker Field: Birthplace of Sports Television Archived October 7, 2008, at the Wayback Machine . Columbia University. ^ ^ ^ Losing streak Archived January 15, 2021, at the Wayback Machine . Wikicu. com. Retrieved on September 7, 2013. ^ ^ a b ^ ^ a b ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ The Age (2009). Melbourne-raised Gorman new chief of Morgan Stanley Archived January 15, 2021, at the Wayback Machine\n\n[NEW 2] orthwest Christian College . Eugene, OR: unpublished MA thesis, University of Oregon, 1949. ^ ^ Griffeth, Ross J. Crusaders for Christ . Eugene, OR: Shelton-Turnbull, Inc., 1971. ^ ^\n\n\nKEPT DOCUMENTS (context only):\n[10 documents already kept as relevant]\n[KEPT 1] foreign policy. That is not in our power. The resolution, however, has the power to divide Austin — and will.\" Electoral history 2024 2024 Austin mayoral election Candidate Votes % Kirk Watson 175,096 50.0041% Carmen Llanes Pulido 70,550 20.14% Kathie Tovo 58,280 16.64% Jeffrey Bowen 29,383 8.39% Do...\n[KEPT 2] Head of the Austin, Texas municipal government Mayor of Austin Flag of the City of Austin Incumbent Kirk Watson since January 6, 2023 Residence Private residence Term length Four years renewable once Inaugural holder Edwin Waller Formation 1840 Salary $134,191 Website austintexas . gov /mayor The ma...\n[KEPT 3] rsity of Bradford Doctor of Technology (DTec) USA 1975 Westminster College (Utah) Doctor of Laws (LLD) France 1976 University of Paris (Sorbonne) Docteur USA 1981 Wesleyan College (Georgia) Dr of Public Administration (DPA) USA 1982 Westminster College (Missouri) Doctor of Laws (DL) England 19 July ...\n[KEPT 4] her degree instead of four because she had taken two semesters off owing to her acting work. On 25 May 2014, she graduated from Brown University with a Bachelor of Arts degree in English literature. In 2016, she was appointed a visiting fellow at Lady Margaret Hall, Oxford . In 2023, she began a Mas...\n[KEPT 5] exas University of Virginia ( BS , MS ) University of Texas, Austin ( JD ) January 3, 2019 Dripping Springs Texas 22 Troy Nehls Republican ( 1968-04-07 ) April 7, 1968 (age 57) Sheriff of Fort Bend County Liberty University ( BA ) University of Houston–Downtown ( MA ) January 3, 2021 Richmond Texas ...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 2 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1043, + "osl": 1191, + "total_tokens": 2234, + "latency_ms": 12783.52, + "tokens_per_second": 93.17 + }, + "context": { + "num_documents_evaluated": 2, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 10 + } + }, + { + "call_id": "05b5c9da-50ea-4e45-82ff-739c26969d74", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T02:51:16.688457Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: In August of 2024, what is the first name of the mayor of the U.S. state capital city who attended the same university as at least one U.S. president and whose city is home to an outgoing or former full member of the Big 12 Conference\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] foreign policy. That is not in our power. The resolution, however, has the power to divide Austin — and will.\" Electoral history 2024 2024 Austin mayoral election Candidate Votes % Kirk Watson 175,096 50.0041% Carmen Llanes Pulido 70,550 20.14% Kathie Tovo 58,280 16.64% Jeffrey Bowen 29,383 8.39% Doug Greco 16,865 4.82% 2022 2022 Austin mayoral run-off Party Candidate Votes % Nonpartisan Kirk Watson 57,346 50.39 Nonpartisan Celia Israel 56,460 49.61 Total votes 113,806 100 2022 Austin mayoral election Party Candidate Votes % Nonpartisan Celia Israel 121,862 39.99 Nonpartisan Kirk Watson 106,508 34.95 Nonpartisan Jennifer Virden 56,189 18.44 Nonpartisan Phil Campero Brual 7,295 2.39 Nonpartisan Anthony Bradshaw 7,102 2.33 Nonpartisan Gary Spellman 5,781 1.90\n\n[DOC 2] Head of the Austin, Texas municipal government Mayor of Austin Flag of the City of Austin Incumbent Kirk Watson since January 6, 2023 Residence Private residence Term length Four years renewable once Inaugural holder Edwin Waller Formation 1840 Salary $134,191 Website austintexas . gov /mayor The mayor of Austin is the official head of the city of Austin in the U. S. state of Texas . The office was established in 1840 after Austin incorporated as a city in 1839. The mayor of Austin is elected to a four-year term and limited to serving no more than two terms. Kirk Watson took office as mayor on January 6, 2023, for a third term and was re-elected to a fourth term in 2024, having served as mayor from 1997 to 2001. Duties and powers Austin has a council–manager\n\n[DOC 3] rsity of Bradford Doctor of Technology (DTec) USA 1975 Westminster College (Utah) Doctor of Laws (LLD) France 1976 University of Paris (Sorbonne) Docteur USA 1981 Wesleyan College (Georgia) Dr of Public Administration (DPA) USA 1982 Westminster College (Missouri) Doctor of Laws (DL) England 19 July 1985 University of Kent Doctor of Laws (LL. D) Canada 7 June 1991 University of Calgary Doctor of Laws (LL. D) England 1994 Goldsmiths, University of London Honorary Fellow USA 1994 Bellarmine College (Kentucky) Doctor of Laws (HLD) England 21 June 1997 Open University Doctor of the University (D. Univ) Wales 1998 University of Wales Doctor of Laws (LL. D) England 18 July 2001 University of Greenwich Doctor of Laws (LL. D) England — Royal College of Organists Fell\n\n[DOC 4] her degree instead of four because she had taken two semesters off owing to her acting work. On 25 May 2014, she graduated from Brown University with a Bachelor of Arts degree in English literature. In 2016, she was appointed a visiting fellow at Lady Margaret Hall, Oxford . In 2023, she began a Master of Studies in creative writing at New College, Oxford . Acting career 1999–2009: Harry Potter and worldwide recognition In 1999, casting began for Harry Potter and the Philosopher's Stone , the film adaptation of British author J. K. Rowling 's best-selling novel . Casting agents found Watson through her Oxford theatre teacher. She had acted in school plays, but had no film acting experience. Her first audition took place when she was nine years old. Although\n\n[DOC 5] exas University of Virginia ( BS , MS ) University of Texas, Austin ( JD ) January 3, 2019 Dripping Springs Texas 22 Troy Nehls Republican ( 1968-04-07 ) April 7, 1968 (age 57) Sheriff of Fort Bend County Liberty University ( BA ) University of Houston–Downtown ( MA ) January 3, 2021 Richmond Texas 23 Tony Gonzales Republican ( 1980-10-10 ) October 10, 1980 (age 45) United States Navy Chaminade University ( AA ) Excelsior College ( BS ) American Public University ( MA ) January 3, 2021 San Antonio Texas 24 Beth Van Duyne Republican ( 1970-11-16 ) November 16, 1970 (age 55) Mayor of Irving Cornell University ( BA ) January 3, 2021 Irving Texas 25 Roger Williams Republican ( 1949-09-13 ) September 13, 1949 (age 76) Secretary of State of Texas Texas Christian U\n\n[DOC 6] football player Raymond Radway , football player Daryl Richardson , football player Bernard Scott , football player Jeev Milkha Singh (1996), golfer Gilbert Tuhabonye , runner and author Charcandrick West , football player Allen Wilson , football coach Earl Young , runner Art Briles , former head coach at Baylor University Rusty Whitt , coach Wes Kittley , coach of Texas Tech Red Raiders track and field Faculty Everett Ferguson , patristics scholar Douglas A. Foster , professor of church history Michael A. O'Donnell , professor of family studies Notes When James Cox's wife became ill, his brother, Alonzo B. Cox, filled in for him to finish the term. References ^ ^ As of June 30, 2024. ^ ^ ^ ^ ^ ^ [Sources: John C. Stevens, _No Ordinary University_, p. 248; J\n\n[DOC 7] its 2008 edition due to financial concerns. The university's national literary magazine, New Madrid with editor Ann Neelon, featured work from a range of nationally recognized authors and received acclaim from sources as diverse as La Bloga , a leading Hispanic journal, and New Pages , a leading national review of literary magazines. A lack of funding led to the suspension of publication in 2018. Presidents Presidents of the university include: John W. Carr, 1923–1926 Rainey T. Wells, 1926–1932 John W. Carr, 1933–1936 James H. Richmond, 1936–1945 Ralph H. Woods, 1945–1968 Harry M. Sparks, 1968–1973 Constantine W. Curris , 1973–1983 Kala M. Stroup, 1983–1990 James L. Booth, 1989–1990 (Acting) Ronald J. Kurth , 1990–1994 Samuel Kern Alexander , 1994–2001 Field\n\n[DOC 8] Politics of Texas Constitution and law Constitution of the United States Constitution of Texas Texas law Executive Governor Greg Abbott (R) Lieutenant Governor Dan Patrick (R) Attorney General Ken Paxton (R) Secretary of State John B. Scott (R) Comptroller of Public Accounts Glenn Hegar (R) Land Commissioner George P. Bush (R) Legislature Texas Senate President Dan Patrick (R) President pro tempore Donna Campbell (R) Texas House of Representatives Speaker Dade Phelan (R) Speaker pro tempore ( vacant ) Judiciary Texas Supreme Court Chief Justice Nathan Hecht (R) Justice Jimmy Blacklock (R) Justice Debra Lehrmann (R) Justice John P. Devine (R) Justice Rebeca Huddle (R) Justice Jane Bland (R) Justice Jeffrey S. Boyd (R) Justice J. Brett Busby (R) Justice Evan A\n\n[DOC 9] Founding members of the Big 12 from the Big Eight: Colorado Iowa State Kansas Kansas State Missouri Nebraska Oklahoma Oklahoma State Founding members of the Big 12 from the Southwest Conference: Baylor Texas Texas A&M Texas Tech Current members with the longest continuous association with the Big Eight Conference / Southwest Conference / Big 12 Conference. Institution Started Current Association In Continuous Years Note Kansas 1907 119 Iowa State 1908 118 Kansas State 1913 113 Baylor 1915 111 Texas Tech 1956 70 Oklahoma State 1958 68 Previously: 10 years in the Southwest Conference (1914–1924); 3 years in the MVIAA (1924–1927) TCU 2012 14 Previously: 73 years in the Southwest Conference (1923–1996) West Virginia 2012 14 BYU 2023 3 UCF 2023 3 Cincinnati 2023\n\n[DOC 10] University (transferred) Harvard University Stanford Graduate School of Business (auditor) Lyndon B. Johnson Johnson City High School Southwest Texas State Teachers College (now Texas State University ) Georgetown University Law Center (withdrew) Richard Nixon Whittier High School Whittier College Duke University School of Law Gerald Ford Grand Rapids South High School University of Michigan University of Michigan Law School (transferred) Yale Law School Jimmy Carter Plains High School Georgia Southwestern College (transferred) Georgia Institute of Technology (transferred) United States Naval Academy Union College (Postgraduate Nuclear Physics Course Program) Ronald Reagan Dixon High School Eureka College none George H. W. Bush Phillips Academy Yale Univers\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide information about the mayor of Austin (Kirk Watson) and the fact that Austin is home to the University of Texas at Austin, a former full member of the Big 12 Conference. However, there is no information about the mayor's university education, which is required to verify that he attended the same university as at least one U.S. president. Without the mayor's alma mater, the question cannot be fully answered.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2304, + "osl": 575, + "total_tokens": 2879, + "latency_ms": 3595.54, + "tokens_per_second": 159.92 + }, + "context": { + "kept_docs_count": 10, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "207fce4d-b181-4ef7-9a9a-4294caaf5b1f", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T02:51:58.721812Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: In August of 2024, what is the first name of the mayor of the U.S. state capital city who attended the same university as at least one U.S. president and whose city is home to an outgoing or former full member of the Big 12 Conference\n\nDOCUMENTS:\n\n[DOC 1] foreign policy. That is not in our power. The resolution, however, has the power to divide Austin — and will.\" Electoral history 2024 2024 Austin mayoral election Candidate Votes % Kirk Watson 175,096 50.0041% Carmen Llanes Pulido 70,550 20.14% Kathie Tovo 58,280 16.64% Jeffrey Bowen 29,383 8.39% Doug Greco 16,865 4.82% 2022 2022 Austin mayoral run-off Party Candidate Votes % Nonpartisan Kirk Watson 57,346 50.39 Nonpartisan Celia Israel 56,460 49.61 Total votes 113,806 100 2022 Austin mayoral election Party Candidate Votes % Nonpartisan Celia Israel 121,862 39.99 Nonpartisan Kirk Watson 106,508 34.95 Nonpartisan Jennifer Virden 56,189 18.44 Nonpartisan Phil Campero Brual 7,295 2.39 Nonpartisan Anthony Bradshaw 7,102 2.33 Nonpartisan Gary Spellman 5,781 1.90\n\n[DOC 2] Head of the Austin, Texas municipal government Mayor of Austin Flag of the City of Austin Incumbent Kirk Watson since January 6, 2023 Residence Private residence Term length Four years renewable once Inaugural holder Edwin Waller Formation 1840 Salary $134,191 Website austintexas . gov /mayor The mayor of Austin is the official head of the city of Austin in the U. S. state of Texas . The office was established in 1840 after Austin incorporated as a city in 1839. The mayor of Austin is elected to a four-year term and limited to serving no more than two terms. Kirk Watson took office as mayor on January 6, 2023, for a third term and was re-elected to a fourth term in 2024, having served as mayor from 1997 to 2001. Duties and powers Austin has a council–manager\n\n[DOC 3] rsity of Bradford Doctor of Technology (DTec) USA 1975 Westminster College (Utah) Doctor of Laws (LLD) France 1976 University of Paris (Sorbonne) Docteur USA 1981 Wesleyan College (Georgia) Dr of Public Administration (DPA) USA 1982 Westminster College (Missouri) Doctor of Laws (DL) England 19 July 1985 University of Kent Doctor of Laws (LL. D) Canada 7 June 1991 University of Calgary Doctor of Laws (LL. D) England 1994 Goldsmiths, University of London Honorary Fellow USA 1994 Bellarmine College (Kentucky) Doctor of Laws (HLD) England 21 June 1997 Open University Doctor of the University (D. Univ) Wales 1998 University of Wales Doctor of Laws (LL. D) England 18 July 2001 University of Greenwich Doctor of Laws (LL. D) England — Royal College of Organists Fell\n\n[DOC 4] her degree instead of four because she had taken two semesters off owing to her acting work. On 25 May 2014, she graduated from Brown University with a Bachelor of Arts degree in English literature. In 2016, she was appointed a visiting fellow at Lady Margaret Hall, Oxford . In 2023, she began a Master of Studies in creative writing at New College, Oxford . Acting career 1999–2009: Harry Potter and worldwide recognition In 1999, casting began for Harry Potter and the Philosopher's Stone , the film adaptation of British author J. K. Rowling 's best-selling novel . Casting agents found Watson through her Oxford theatre teacher. She had acted in school plays, but had no film acting experience. Her first audition took place when she was nine years old. Although\n\n[DOC 5] exas University of Virginia ( BS , MS ) University of Texas, Austin ( JD ) January 3, 2019 Dripping Springs Texas 22 Troy Nehls Republican ( 1968-04-07 ) April 7, 1968 (age 57) Sheriff of Fort Bend County Liberty University ( BA ) University of Houston–Downtown ( MA ) January 3, 2021 Richmond Texas 23 Tony Gonzales Republican ( 1980-10-10 ) October 10, 1980 (age 45) United States Navy Chaminade University ( AA ) Excelsior College ( BS ) American Public University ( MA ) January 3, 2021 San Antonio Texas 24 Beth Van Duyne Republican ( 1970-11-16 ) November 16, 1970 (age 55) Mayor of Irving Cornell University ( BA ) January 3, 2021 Irving Texas 25 Roger Williams Republican ( 1949-09-13 ) September 13, 1949 (age 76) Secretary of State of Texas Texas Christian U\n\n[DOC 6] football player Raymond Radway , football player Daryl Richardson , football player Bernard Scott , football player Jeev Milkha Singh (1996), golfer Gilbert Tuhabonye , runner and author Charcandrick West , football player Allen Wilson , football coach Earl Young , runner Art Briles , former head coach at Baylor University Rusty Whitt , coach Wes Kittley , coach of Texas Tech Red Raiders track and field Faculty Everett Ferguson , patristics scholar Douglas A. Foster , professor of church history Michael A. O'Donnell , professor of family studies Notes When James Cox's wife became ill, his brother, Alonzo B. Cox, filled in for him to finish the term. References ^ ^ As of June 30, 2024. ^ ^ ^ ^ ^ ^ [Sources: John C. Stevens, _No Ordinary University_, p. 248; J\n\n[DOC 7] its 2008 edition due to financial concerns. The university's national literary magazine, New Madrid with editor Ann Neelon, featured work from a range of nationally recognized authors and received acclaim from sources as diverse as La Bloga , a leading Hispanic journal, and New Pages , a leading national review of literary magazines. A lack of funding led to the suspension of publication in 2018. Presidents Presidents of the university include: John W. Carr, 1923–1926 Rainey T. Wells, 1926–1932 John W. Carr, 1933–1936 James H. Richmond, 1936–1945 Ralph H. Woods, 1945–1968 Harry M. Sparks, 1968–1973 Constantine W. Curris , 1973–1983 Kala M. Stroup, 1983–1990 James L. Booth, 1989–1990 (Acting) Ronald J. Kurth , 1990–1994 Samuel Kern Alexander , 1994–2001 Field\n\n[DOC 8] Politics of Texas Constitution and law Constitution of the United States Constitution of Texas Texas law Executive Governor Greg Abbott (R) Lieutenant Governor Dan Patrick (R) Attorney General Ken Paxton (R) Secretary of State John B. Scott (R) Comptroller of Public Accounts Glenn Hegar (R) Land Commissioner George P. Bush (R) Legislature Texas Senate President Dan Patrick (R) President pro tempore Donna Campbell (R) Texas House of Representatives Speaker Dade Phelan (R) Speaker pro tempore ( vacant ) Judiciary Texas Supreme Court Chief Justice Nathan Hecht (R) Justice Jimmy Blacklock (R) Justice Debra Lehrmann (R) Justice John P. Devine (R) Justice Rebeca Huddle (R) Justice Jane Bland (R) Justice Jeffrey S. Boyd (R) Justice J. Brett Busby (R) Justice Evan A\n\n[DOC 9] Founding members of the Big 12 from the Big Eight: Colorado Iowa State Kansas Kansas State Missouri Nebraska Oklahoma Oklahoma State Founding members of the Big 12 from the Southwest Conference: Baylor Texas Texas A&M Texas Tech Current members with the longest continuous association with the Big Eight Conference / Southwest Conference / Big 12 Conference. Institution Started Current Association In Continuous Years Note Kansas 1907 119 Iowa State 1908 118 Kansas State 1913 113 Baylor 1915 111 Texas Tech 1956 70 Oklahoma State 1958 68 Previously: 10 years in the Southwest Conference (1914–1924); 3 years in the MVIAA (1924–1927) TCU 2012 14 Previously: 73 years in the Southwest Conference (1923–1996) West Virginia 2012 14 BYU 2023 3 UCF 2023 3 Cincinnati 2023\n\n[DOC 10] University (transferred) Harvard University Stanford Graduate School of Business (auditor) Lyndon B. Johnson Johnson City High School Southwest Texas State Teachers College (now Texas State University ) Georgetown University Law Center (withdrew) Richard Nixon Whittier High School Whittier College Duke University School of Law Gerald Ford Grand Rapids South High School University of Michigan University of Michigan Law School (transferred) Yale Law School Jimmy Carter Plains High School Georgia Southwestern College (transferred) Georgia Institute of Technology (transferred) United States Naval Academy Union College (Postgraduate Nuclear Physics Course Program) Ronald Reagan Dixon High School Eureka College none George H. W. Bush Phillips Academy Yale Univers\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2145, + "osl": 1201, + "total_tokens": 3346, + "latency_ms": 42032.72, + "tokens_per_second": 28.57 + }, + "context": { + "num_documents_used": 10, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:51:58.722635Z", + "summary": { + "total_llm_calls": 12, + "total_hops": 5, + "total_input_tokens": 21581, + "total_output_tokens": 12739, + "total_tokens": 34320, + "total_latency_ms": 135262.5, + "average_tokens_per_second": 122.17, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Big_12_Conference", + "https://en.wikipedia.org/wiki/Lincoln,_Nebraska", + "https://en.wikipedia.org/wiki/Leirion_Gaylor_Baird", + "https://en.wikipedia.org/wiki/List_of_presidents_of_the_United_States_by_education" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "Leirion" + } + }, + { + "query_id": "18", + "original_query": "How many years after the founding of the 50th most populous US city, based on 2023 estimate population data, did Frank Fox receive UK Patent (1344259)?", + "timestamp_start": "2026-05-16T02:51:58.738701Z", + "llm_calls": [ + { + "call_id": "daec7ecd-f244-4c02-bf6f-f6ed4edc5e65", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:52:10.916828Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: How many years after the founding of the 50th most populous US city, based on 2023 estimate population data, did Frank Fox receive UK Patent (1344259)?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"US cities population 2023\",\n \"Frank Fox patent 1344259\",\n \"founding year of city\"\n ],\n \"feedback\": \"Need the 50th most populous US city (2023 estimates) and its founding year, plus the grant date of UK Patent 1344259 held by Frank Fox. First query finds the city rank list, second retrieves patent details, third will be refined with the specific city name once identified.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 473, + "osl": 841, + "total_tokens": 1314, + "latency_ms": 12178.01, + "tokens_per_second": 69.06 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "202ade4c-099e-4398-8355-571e7f42ce97", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:52:15.528206Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: How many years after the founding of the 50th most populous US city, based on 2023 estimate population data, did Frank Fox receive UK Patent (1344259)?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] t. In 2024, the median age of the United States population was 39.1 years. Urbanization Most Americans live in suburbs and exurbs , such as Monroe Township , New Jersey , in the New York metropolitan area . About 82% of Americans live in metropolitan areas , particularly in suburbs and outer-ring exurbs ; about half of those reside in cities with populations over 50,000. In 2024, 346 incorporated U. S. municipalities had populations over 100,000, 11 cities had more than one million residents, and four cities— New York City , Los Angeles , Chicago , and Houston —had populations exceeding two million. Some 56 U. S. metropolitan areas have one million or more residents . More recently, the fastest-growing metropolitan areas were in the South, while southern met\n\n[NEW 2] ; the populations of other municipalities considered suburbs of a central city are listed separately, and unincorporated areas within urban agglomerations are not included. Therefore, a different ranking is evident when considering U. S. urban areas or metropolitan areas . 50 states and the District of Columbia 1. New York 2. Los Angeles 3. Chicago 4. Houston 5. Phoenix 6. Philadelphia 7. San Antonio 8. San Diego 9. Dallas 10. Jacksonville 11. Fort Worth 12. San Jose 13. Austin 14. Charlotte 15. Columbus 16. Indianapolis 17. San Francisco 18. Seattle 19. Denver 20. Oklahoma City Location of the 20 largest cities in the United States, based on U. S. Census Bureau estimates for July 1, 2024 This table lists the 346 incorporated places in the United States, ex\n\n[NEW 3] ial Machinery & Supplies & Components Everett, Washington 2016-07-01 0001659166 2016 FOXA Fox Corporation (Class A) Communication Services Broadcasting New York City , New York 2019-03-19 0001754301 2019 FOX Fox Corporation (Class B) Communication Services Broadcasting New York City , New York 2019-03-19 0001754301 2019 BEN Franklin Resources Financials Asset Management & Custody Banks San Mateo, California 1998-04-30 0000038777 1947 FCX Freeport-McMoRan Materials Copper Phoenix, Arizona 2011-07-01 0000831259 1912 GRMN Garmin Consumer Discretionary Consumer Electronics Schaffhausen , Switzerland 2012-12-12 0001121788 1989 IT Gartner Information Technology IT Consulting & Other Services Stamford, Connecticut 2017-04-05 0000749251 1979 GE GE Aerospace Industri\n\n[NEW 4] –139; Lacey 2002 , pp. 124–125; Pimlott 2001 , p. 86 ^ Bond 2006 , p. 10; Brandreth 2004 , pp. 132–136, 166–169; Lacey 2002 , pp. 119, 126, 135 ^ Heald 2007 , p. 77 ^ ^ Crawford 1950 , p. 180 ^ ; Brandreth 2004 , p. 314 ^ Heald 2007 , p. xviii ^ Hoey 2002 , pp. 55–56; Pimlott 2001 , pp. 101, 137 ^ ^ a b ^ Hoey 2002 , p. 58; Pimlott 2001 , pp. 133–134 ^ Hoey 2002 , p. 59; Petropoulos 2006 , p. 363 ^ Bradford 2012 , p. 61 ^ Letters Patent, 22 October 1948; Hoey 2002 , pp. 69–70; Pimlott 2001 , pp. 155–156 ^ Pimlott 2001 , p. 163 ^ Brandreth 2004 , pp. 226–238; Pimlott 2001 , pp. 145, 159–163, 167 ^ Brandreth 2004 , pp. 240–241; Lacey 2002 , p. 166; Pimlott 2001 , pp. 169–172 ^ Brandreth 2004 , pp. 245–247; Lacey 2002 , p. 166; Pimlott 2001 , pp. 173–176; Shawc\n\n[NEW 5] ^ a b Peet 1989 , p. 165. ^ Barrier 1999 , p. 566. ^ Thomas 1997 , p. 106. ^ a b ^ ^ ^ ^ a b c d ^ a b ^ ^ a b Smith 2012 , p. 18. ^ a b c Koenig 1997 , p. 117. ^ a b c Maltin 1995 , p. 181. ^ Thomas & Johnston 1993 , p. 135. ^ Dakin & Saxon 2020 , p. 49. ^ Frankham & Hollifield 2015 , pp. 278. ^ a b Maltin 1995 , p. 183. ^ a b c d e f g h Webb 2011 , p. 255. ^ a b c d Maltin 1995 , p. 184. ^ a b c d Beck 2005 , p. 185. ^ ^ a b Dakin & Saxon 2020 , p. 48. ^ a b ^ a b ^ a b c d ^ a b c d e f g h i j ^ a b Frankham & Hollifield 2015 , p. 281. ^ Smith 2012 , p. 38. ^ a b ^ ^ Frankham & Hollifield 2015 , pp. 3. ^ a b ^ Frankham & Hollifield 2015 , p. 276. ^ a b Frankham & Hollifield 2015 , p. 279. ^ a b Frankham & Hollifield 2015 , p. 280. ^ Maltin 1995 , pp. 18\n\n[NEW 6] . BBC ^ Feldman, Anthony and Ford, Peter (1989) Scientists & inventors . Bloomsbury Books, p. 128, ISBN 1-870630-23-8 . ^ Fox Talbot, William Henry and Jammes, André (1973) William H. Fox Talbot, inventor of the negative-positive process , Macmillan, p. 95. ^ ^ ^ ^ History of Kodak, Milestones-chronology: 1878–1929 Archived 10 February 2012 at the Wayback Machine . kodak. com ^ ^ ^ ^ ^ a b ^ a b ^ ^ Schewe, Jeff (2012). The Digital Negative: Raw Image Processing In Lightroom , Camera Raw , and Photoshop . Berkeley, California: Peachpit Press, ISBN 0-321-83957-9 , p. 72. ^ ^ ^ ^ ^ Twede, David. Introduction to Full-Spectrum and Infrared photography . surrealcolor.110mb. com ^ ^ ^ ^ Ng, Ren (July 2006) Digital Light Field Photography . PhD Thesis, Stanford Un\n\n[NEW 7] 08, 449 (S. D. Ohio 1980) (indicating that a statute neither requiring nor permitting an anticompetitive collaboration gives the private party enough freedom of choice to preclude preemption), aff'd in part and remanded in part, 679 F.2d 656 (6th Cir. 1982) ^ Rice , 458 U. S. at 659. ^ Id. (citing New Motor Vehicle Bd. v. Orrin W. Fox Co. , 439 U. S. 96, 110–11 (1978); Exxon Corp. v. Governor of MD. , 437 U. S. 117, 129–34 (1978); Joseph E. Seagram & Sons v. Hostetter , 384 U. S. 35, 45–46 (1966)). ^ New Motor Vehicle Bd. v. Orrin W. Fox Co. , 439 U. S. 96, 110–11 (1978) (quoting Exxon Corp. v. Governor of MD. , 437 U. S. 117, 133 (1978)). ^ Rice v. Norman Williams Co. , 458 U. S. 654, 662 (1982). ^ H. R. Rep. No. 1707, 51st Cong., 1st Sess., p. 1. ^ 21 Cong\n\n[NEW 8] rict Lahore Founded Between 1st and 7th centuries CE City status 1040 ; 986 years ago ( 1040 ) Capital status 25 June 1206 ; 819 years ago ( 25 June 1206 ) 27 May 1586 ; 439 years ago ( 27 May 1586 ) 12 April 1801 ; 224 years ago ( 12 April 1801 ) Metropolitan status 3 February 1890 ; 136 years ago ( 3 February 1890 ) Metropolitan seat Lahore Town Hall Zones 10 Ravi Shalimar Aziz Bhatti Data Gunj Buksh Gulberg Samanabad Iqbal Nishtar Wagah Cantonment Government • Type Metropolitan corporation • Body Lahore Metropolitan Corporation • Mayor None • Deputy Mayors 9 Zonal Mayors • Deputy Commissioner Syed Musa Raza (BPS-19 PAS) • Capital City Police Officer Bilal Siddiqui Kamyana (BPS-21 PSP) • Punjab Assembly 30 members Sami Ullah Khan ( PML-N ; PP-145 ) Ghazali\n\n[NEW 9] ircle References ^ ^ {{ cite web }} : CS1 maint: url-status ( link ) ^ ^ a b (a) This contains supporting materials for the following book: (b) ^ Figures in main tables are preferentially cited. Part of former estimates can be read at ^ Chandler defines a city as a continuously built-up area (urban) with suburbs but without farmland inside the municipality. Figures in main tables are preferentially cited. Part of Chandler's estimates are summarized or modified at: (a) (b) (c) ^ The date that the population of Beidha, Basta and Çatalhöyük is estimated to be 1,000 is given as 7500 BCE in Morris's published text (p. 632). ^ a b c Suggested to be the largest cities in Modelski's text, but not given constantly prior to 3700 BCE (p. 3, p. 17, and p. 20). No entry\n\n[NEW 10] 10 2,695,598 −6.9% 2020 2,746,388 1.9% 2024 (est.) 2,721,308 −0.9% United States Census Bureau 2010–2020 During its first hundred years, Chicago was one of the fastest-growing cities in the world. When founded in 1833, fewer than 200 people had settled on what was then the American frontier. By the time of its first census, seven years later, the population had reached over 4,000. In the forty years from 1850 to 1890, the city's population grew from slightly under 30,000 to over 1 million. By the 1890 census, Chicago was the second most populous city in the United States. By 1900, it was the fifth largest in the world behind Berlin, Paris, New York, and London, and the largest city founded in the prior century. Within sixty years of the Great Chicago Fire of\n\n[NEW 11] I, First Session 1664–1665 ^ ^ a b c d e f ^ see introduction of 2013 edition of Rydell, Robert W. All the world's a fair: Visions of empire at American international expositions, 1876–1916. University of Chicago Press, 2013. ^ Birgit Breugal for the EXPO2000 Hannover GmbH Hannover, the EXPO-BOOK The Official Catalogue of EXPO2000 with CDROM ^ Rydell, Robert W. (1987). All the World's a Fair: Visions of Empire at American International Expositions Archived 2014-08-24 at the Wayback Machine , p. 53. University of Chicago. ISBN 0-226-73240-1 . ^ ^ ^ ^ ^ ^ ^ ^ Buel, James William. The Magic City: A Massive Portfolio of Original Photographic Views of the Great World's Fair , Historical Publishing Company, St. Louis MO, 1894 reprinted by Arno Press, NY, 1974 ^ Ca\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 11 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[0,0,0,0,0,0,0,0,0,0,0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2991, + "osl": 666, + "total_tokens": 3657, + "latency_ms": 3853.1, + "tokens_per_second": 172.85 + }, + "context": { + "num_documents_evaluated": 11, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "f84a7065-6dee-4dbe-8643-ff88222f9c6a", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:52:22.983750Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: How many years after the founding of the 50th most populous US city, based on 2023 estimate population data, did Frank Fox receive UK Patent (1344259)?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: - Query: 'US cities population 2023' → 2 docs\n- Query: 'Frank Fox patent 1344259' → 5 docs\n- Query: 'founding year of city' → 4 docs\n\nFEEDBACK: Need the 50th most populous US city (2023 estimates) and its founding year, plus the grant date of UK Patent 1344259 held by Frank Fox. First query finds the city rank list, second retrieves patent details, third will be refined with the specific city name once identified.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"50th most populous US city 2023\",\n \"List US cities population 2023\",\n \"UK patent 1344259 grant date\"\n ],\n \"feedback\": \"Need to identify the city that ranked 50th in 2023 US population estimates and its founding year, plus the grant date of Frank Fox's UK Patent No. 1344259.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 569, + "osl": 477, + "total_tokens": 1046, + "latency_ms": 7454.73, + "tokens_per_second": 63.99 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "25c8f5b9-f49b-4939-aa77-a83109a4b99a", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T02:52:25.258480Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: How many years after the founding of the 50th most populous US city, based on 2023 estimate population data, did Frank Fox receive UK Patent (1344259)?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] 2 2023 White 6,136 (80.3%) 6,258 (81.3%) 6,196 (79.8%) 5,763 (78.0%) 5,426 (78.6%) 5,078 (77.4%) 5,158 (78.6%) 4,762 (77.7%) 4,882 (78.3%) 4,622 (76.4%) 4,553 (76.0%) Native American 305 (4.0%) 294 (3.8%) 294 (3.8%) 200 (2.7%) 206 (3.0%) 219 (3.3%) 198 (3.0%) 176 (2.9%) 179 (2.9%) 178 (2.9%) 150 (2.5%) Asian 124 (1.6%) 108 (1.4%) 135 (1.7%) 100 (1.3%) 79 (1.1%) 72 (1.1%) 73 (1.1%) 58 (0.9%) 67 (1.1%) 64 (1.1%) 68 (1.1%) Black 125 (1.6%) 116 (1.5%) 119 (1.5%) 63 (0.9%) 45 (0.7%) 57 (0.9%) 61 (0.9%) 55 (0.9%) 48 (0.8%) 46 (0.7%) 38 (0.6%) Hispanic (any race) 926 (12.1%) 895 (11.6%) 963 (12.4%) 973 (13.2%) 892 (12.9%) 851 (13.0%) 839 (12.8%) 818 (13.3%) 749 (12.0%) 835 (13.8%) 858 (14.3%) Total 7,644 (100%) 7,696 (100%) 7,765 (100%) 7,386 (100%) 6,903 (100%) 6,\n\n[NEW 2] Facts Archived 15 August 2018 at the Wayback Machine , Nobel Foundation, 2014. (accessed 29 October 2014) ^ ^ Nobel Prize in Chemistry Archived 23 May 2020 at the Wayback Machine Nobel Foundation 2018 (accessed 3 October 2018) ^ Nobel Prize in Physiology or Medicine Archived 23 May 2020 at the Wayback Machine Nobel Foundation 2018. (accessed 3 October 2018) ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b ^ (Subscription, Wikipedia Library access or UK public library membership required.) ^ ^ ^ ^ ^ ^ ^ ^ Monument of the Planet of Alfred Nobel Archived 9 August 2017 at the Wayback Machine . Panoramio. com. Retrieved on 28 July 2013. ^ {{ cite magazine }} : CS1 maint: deprecated archival service ( link ) ^ ^ ^ ^ Sources This article incorporates text from a free content work. Licen\n\n[NEW 3] p ) {{ cite book }} : ISBN / Date incompatibility ( help ) {{ cite book }} : ISBN / Date incompatibility ( help ) {{ cite book }} : ISBN / Date incompatibility ( help ) {{ cite book }} : ISBN / Date incompatibility ( help ) External links England at Wikipedia's sister projects Media from Commons News from Wikinews Quotations from Wikiquote Travel information from Wikivoyage English Heritage – national body protecting English heritage Natural England – wildlife and the natural world of England VisitEngland – English tourist board BBC News – England – news items from BBC News relating to England GOV. UK – official website of the British Government Geographic data related to England at OpenStreetMap 53°08′N 1°23′W  /  53.13°N 1.38°W  / 53.13; -1.38\n\n[NEW 4] p ) {{ cite book }} : ISBN / Date incompatibility ( help ) {{ cite book }} : ISBN / Date incompatibility ( help ) {{ cite book }} : ISBN / Date incompatibility ( help ) {{ cite book }} : ISBN / Date incompatibility ( help ) External links England at Wikipedia's sister projects Media from Commons News from Wikinews Quotations from Wikiquote Travel information from Wikivoyage English Heritage – national body protecting English heritage Natural England – wildlife and the natural world of England VisitEngland – English tourist board BBC News – England – news items from BBC News relating to England GOV. UK – official website of the British Government Geographic data related to England at OpenStreetMap 53°08′N 1°23′W  /  53.13°N 1.38°W  / 53.13; -1.38\n\n[NEW 5] 1989 , p. 32. ^ Norwich 1989 , p. 34. ^ a b c Adkins & Adkins 1998 , p. 34. ^ Hebblewhite 2016 , p. 20. ^ a b Errington 2006 , pp. 36–37. ^ a b Birley 2005 , p. 450. ^ Grant 1997 , pp. 270–274. ^ a b c Adkins & Adkins 1998 , p. 35. ^ Grant 1997 , pp. 282–285. ^ Grant 1997 , pp. 298–302. ^ Drinkwater & Elton 2002 , p. 116. ^ Burns & Jensen 2014 , p. 64. ^ Collins 2010 , p. 88. ^ Bagnall et al. 1987 , p. 446. ^ Grant 1997 , pp. 310–312. ^ Barnes 1983 . ^ Grant 1997 , pp. 315–317. ^ Grant 1997 , pp. 317–319. ^ Grant 1997 , pp. 319–321. ^ Grant 1997 , pp. 322–323. ^ Norwich 1989 , p. 171. ^ Bury 1923 , p. 274. ^ a b Norwich 1989 , p. 385. ^ Gibbons & Womersley 1994 , p. 402. ^ Burns 1991 , p. 74. Sources Web sources Further reading Library resources about Wester\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 5 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1648, + "osl": 401, + "total_tokens": 2049, + "latency_ms": 1528.18, + "tokens_per_second": 262.4 + }, + "context": { + "num_documents_evaluated": 5, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "44f7a989-68d8-40d2-a67f-aa62d347b1d6", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T02:52:29.192457Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: How many years after the founding of the 50th most populous US city, based on 2023 estimate population data, did Frank Fox receive UK Patent (1344259)?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: - Query: 'US cities population 2023' → 2 docs\n- Query: 'Frank Fox patent 1344259' → 5 docs\n- Query: 'founding year of city' → 4 docs\n- Query: '50th most populous US city 2023' → 0 docs\n- Query: 'List US cities population 2023' → 1 docs\n- Query: 'UK patent 1344259 grant date' → 4 docs\n\nFEEDBACK: Need the 50th most populous US city (2023 estimates) and its founding year, plus the grant date of UK Patent 1344259 held by Frank Fox. First query finds the city rank list, second retrieves patent details, third will be refined with the specific city name once identified.\nNeed to identify the city that ranked 50th in 2023 US population estimates and its founding year, plus the grant date of Frank Fox's UK Patent No. 1344259.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"United States cities population 2023\",\n \"UK patent 1344259 Frank Fox\",\n \"US cities founded list\"\n ],\n \"feedback\": \"We still need the 50th most populous US city from the 2023 estimates and its specific founding year, as well as the exact grant date of UK Patent 1344259 held by Frank Fox. The first query should retrieve a ranked list of city populations for 2023, the second should provide the patent’s grant details, and the third aims to obtain a reference list of US city founding years to later match the identified city.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 671, + "osl": 839, + "total_tokens": 1510, + "latency_ms": 3933.21, + "tokens_per_second": 213.31 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "30b677bf-a31e-473d-a194-a55d0c9ca9f7", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T02:52:33.459700Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: How many years after the founding of the 50th most populous US city, based on 2023 estimate population data, did Frank Fox receive UK Patent (1344259)?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] list Fox S Samuel Fox UK left back Leces Louis Celeste Lecesne UK left back Grevi Robert Greville UK far left Botanist Pease Jo Joseph Pease UK left Minister Tatum William Tatum UK right Webb Richard D. Webb Ireland right Publisher Scales Rev. Thomas Scales UK right front Minister James W William James UK right Minister Wilson William Wilson UK right Swan T Rev. Thomas Swan UK right Baptist Minister Steane Rev. Edward Steane UK right Minister Miller J Colonel Jonathon Miller US right front United States Army Officer Stuart Captain Charles Stuart Jamaica right Royal Navy Officer Jerem Sir John Jeremie Colonies right of centre Judge Stow Charles Stovel UK far right front Minister Peek Richard Peek UK far right front Sheriff of London Sturge John Sturge UK far\n\n[NEW 2] & Gilbert 1994 , pp. 46–47. ^ McCarthy & Gilbert 1994 , pp. 48–49. ^ McCarthy & Gilbert 1994 , pp. 62–63. ^ McCarthy & Gilbert 1994 , p. 55. ^ McCarthy & Gilbert 1994 , pp. 54–55. ^ McCarthy & Gilbert 1994 , pp. 10–11. ^ McCarthy & Gilbert 1994 , pp. 66–67. ^ ^ McCarthy & Gilbert 1994 , pp. 50–51. ^ McCarthy & Gilbert 1994 , pp. 60–61. ^ McCarthy & Gilbert 1994 , pp. 76–77. ^ McCarthy & Gilbert 1994 , pp. 52–53. ^ McCarthy & Gilbert 1994 , pp. 70, 74–75. ^ Witton & Michel 2022 , pp. 96–103 ^ ^ McCarthy & Gilbert 1994 , pp. 58–59. ^ McCarthy & Gilbert 1994 , p. 79. ^ a b c d Witton & Michel 2022 , pp. 68–91 ^ McCarthy & Gilbert 1994 , pp. 82–83. ^ McCarthy & Gilbert 1994 , pp. 80–81. ^ ^ McCarthy & Gilbert 1994 , pp. 12, 78, citing the Illustrated London News\n\n[NEW 3] 8 36,420 94,326 9 Iowa IA Des Moines Dec 28, 1846 3,190,369 56,273 145,746 4 Kansas KS Topeka Wichita Jan 29, 1861 2,937,880 82,278 213,100 4 Kentucky KY Frankfort Louisville Jun 1, 1792 4,505,836 40,408 104,656 6 Louisiana LA Baton Rouge New Orleans Apr 30, 1812 4,657,757 52,378 135,659 6 Maine ME Augusta Portland Mar 15, 1820 1,362,359 35,380 91,633 2 Maryland MD Annapolis Baltimore Apr 28, 1788 6,177,224 12,406 32,131 8 Massachusetts MA Boston Feb 6, 1788 7,029,917 10,554 27,336 9 Michigan MI Lansing Detroit Jan 26, 1837 10,077,331 96,714 250,487 13 Minnesota MN Saint Paul Minneapolis May 11, 1858 5,706,494 86,936 225,163 8 Mississippi MS Jackson Dec 10, 1817 2,961,279 48,432 125,438 4 Missouri MO Jefferson City Kansas City Aug 10, 1821 6,154,913 69,707 1\n\n[NEW 4] ugust 10, 1821 (admitted) Missouri Territory (part) 25 Arkansas June 15, 1836 (admitted) Arkansas Territory 26 Michigan January 26, 1837 (admitted) Michigan Territory 27 Florida March 3, 1845 (admitted) Florida Territory 28 Texas December 29, 1845 (admitted) Republic of Texas 29 Iowa December 28, 1846 (admitted) Iowa Territory (part) 30 Wisconsin May 29, 1848 (admitted) Wisconsin Territory (part) 31 California September 9, 1850 (admitted) Unorganized territory / Mexican Cession (part) 32 Minnesota May 11, 1858 (admitted) Minnesota Territory (part) 33 Oregon February 14, 1859 (admitted) Oregon Territory (part) 34 Kansas January 29, 1861 (admitted) Kansas Territory (part) 35 West Virginia June 20, 1863 (admitted) Virginia ( 50 Trans-Allegheny region counties )\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 4 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 0, 1, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1300, + "osl": 450, + "total_tokens": 1750, + "latency_ms": 3491.33, + "tokens_per_second": 128.89 + }, + "context": { + "num_documents_evaluated": 4, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "8d18c921-1b35-4c46-83ce-b2f8b03b0b6d", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T02:52:38.188809Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: How many years after the founding of the 50th most populous US city, based on 2023 estimate population data, did Frank Fox receive UK Patent (1344259)?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] list Fox S Samuel Fox UK left back Leces Louis Celeste Lecesne UK left back Grevi Robert Greville UK far left Botanist Pease Jo Joseph Pease UK left Minister Tatum William Tatum UK right Webb Richard D. Webb Ireland right Publisher Scales Rev. Thomas Scales UK right front Minister James W William James UK right Minister Wilson William Wilson UK right Swan T Rev. Thomas Swan UK right Baptist Minister Steane Rev. Edward Steane UK right Minister Miller J Colonel Jonathon Miller US right front United States Army Officer Stuart Captain Charles Stuart Jamaica right Royal Navy Officer Jerem Sir John Jeremie Colonies right of centre Judge Stow Charles Stovel UK far right front Minister Peek Richard Peek UK far right front Sheriff of London Sturge John Sturge UK far\n\n[DOC 2] 8 36,420 94,326 9 Iowa IA Des Moines Dec 28, 1846 3,190,369 56,273 145,746 4 Kansas KS Topeka Wichita Jan 29, 1861 2,937,880 82,278 213,100 4 Kentucky KY Frankfort Louisville Jun 1, 1792 4,505,836 40,408 104,656 6 Louisiana LA Baton Rouge New Orleans Apr 30, 1812 4,657,757 52,378 135,659 6 Maine ME Augusta Portland Mar 15, 1820 1,362,359 35,380 91,633 2 Maryland MD Annapolis Baltimore Apr 28, 1788 6,177,224 12,406 32,131 8 Massachusetts MA Boston Feb 6, 1788 7,029,917 10,554 27,336 9 Michigan MI Lansing Detroit Jan 26, 1837 10,077,331 96,714 250,487 13 Minnesota MN Saint Paul Minneapolis May 11, 1858 5,706,494 86,936 225,163 8 Mississippi MS Jackson Dec 10, 1817 2,961,279 48,432 125,438 4 Missouri MO Jefferson City Kansas City Aug 10, 1821 6,154,913 69,707 1\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The provided documents do not contain any information about Frank Fox receiving UK Patent 1344259, nor do they identify the 50th most populous US city based on 2023 population estimates and its founding year. Without these critical data points, the required calculation cannot be performed.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 735, + "osl": 71, + "total_tokens": 806, + "latency_ms": 4728.34, + "tokens_per_second": 15.02 + }, + "context": { + "kept_docs_count": 2, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "f56d783b-cec7-4fb8-8b91-bdfc5532f76f", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T02:52:44.190235Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: How many years after the founding of the 50th most populous US city, based on 2023 estimate population data, did Frank Fox receive UK Patent (1344259)?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] list Fox S Samuel Fox UK left back Leces Louis Celeste Lecesne UK left back Grevi Robert Greville UK far left Botanist Pease Jo Joseph Pease UK left Minister Tatum William Tatum UK right Webb Richard D. Webb Ireland right Publisher Scales Rev. Thomas Scales UK right front Minister James W William James UK right Minister Wilson William Wilson UK right Swan T Rev. Thomas Swan UK right Baptist Minister Steane Rev. Edward Steane UK right Minister Miller J Colonel Jonathon Miller US right front United States Army Officer Stuart Captain Charles Stuart Jamaica right Royal Navy Officer Jerem Sir John Jeremie Colonies right of centre Judge Stow Charles Stovel UK far right front Minister Peek Richard Peek UK far right front Sheriff of London Sturge John Sturge UK far\n\n[DOC 2] 8 36,420 94,326 9 Iowa IA Des Moines Dec 28, 1846 3,190,369 56,273 145,746 4 Kansas KS Topeka Wichita Jan 29, 1861 2,937,880 82,278 213,100 4 Kentucky KY Frankfort Louisville Jun 1, 1792 4,505,836 40,408 104,656 6 Louisiana LA Baton Rouge New Orleans Apr 30, 1812 4,657,757 52,378 135,659 6 Maine ME Augusta Portland Mar 15, 1820 1,362,359 35,380 91,633 2 Maryland MD Annapolis Baltimore Apr 28, 1788 6,177,224 12,406 32,131 8 Massachusetts MA Boston Feb 6, 1788 7,029,917 10,554 27,336 9 Michigan MI Lansing Detroit Jan 26, 1837 10,077,331 96,714 250,487 13 Minnesota MN Saint Paul Minneapolis May 11, 1858 5,706,494 86,936 225,163 8 Mississippi MS Jackson Dec 10, 1817 2,961,279 48,432 125,438 4 Missouri MO Jefferson City Kansas City Aug 10, 1821 6,154,913 69,707 1\n\n\nSEARCH HISTORY: - Query: 'US cities population 2023' → 2 docs\n- Query: 'Frank Fox patent 1344259' → 5 docs\n- Query: 'founding year of city' → 4 docs\n- Query: '50th most populous US city 2023' → 0 docs\n- Query: 'List US cities population 2023' → 1 docs\n- Query: 'UK patent 1344259 grant date' → 4 docs\n- Query: 'United States cities population 2023' → 0 docs\n- Query: 'UK patent 1344259 Frank Fox' → 2 docs\n- Query: 'US cities founded list' → 2 docs\n\nFEEDBACK: Need the 50th most populous US city (2023 estimates) and its founding year, plus the grant date of UK Patent 1344259 held by Frank Fox. First query finds the city rank list, second retrieves patent details, third will be refined with the specific city name once identified.\nNeed to identify the city that ranked 50th in 2023 US population estimates and its founding year, plus the grant date of Frank Fox's UK Patent No. 1344259.\nWe still need the 50th most populous US city from the 2023 estimates and its specific founding year, as well as the exact grant date of UK Patent 1344259 held by Frank Fox. The first query should retrieve a ranked list of city populations for 2023, the second should provide the patent’s grant details, and the third aims to obtain a reference list of US city founding years to later match the identified city.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"2023 US city population list\",\n \"UK patent 1344259 Frank Fox\",\n \"US city founding dates list\"\n ],\n \"feedback\": \"Need the 50th most populous US city from the 2023 estimates (ranked list), the grant date of UK Patent 1344259 held by Frank Fox, and a source of US city founding years to later locate the founding year of the identified city.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1223, + "osl": 96, + "total_tokens": 1319, + "latency_ms": 6000.86, + "tokens_per_second": 16.0 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "f955a4bf-9f05-4d17-b827-a6674d0cf93e", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T02:52:50.158728Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: How many years after the founding of the 50th most populous US city, based on 2023 estimate population data, did Frank Fox receive UK Patent (1344259)?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] hicago – Loop (2026) Illinois 12. Detroit Michigan 13. Irvine California 14. Kirkland Washington 15. Kitchener Canada 16. Los Angeles California 17. Madison Wisconsin 18. Miami Florida 19. Montreal Canada 20. Mountain View – HQ California 21. New York New York 22. Pittsburgh Pennsylvania 23. Playa Vista California 24. Portland Oregon 25. Redwood City California 26. Reston Virginia 27. San Bruno California 28. San Diego California 29. San Francisco California 30. Seattle Washington 31. Sunnyvale California 32. Toronto Canada 33. Washington DC District of Columbia Latin America SN City Country 1. Belo Horizonte Brazil 2. Bogotá Colombia 3. Buenos Aires Argentina 4. Mexico City Mexico 5. San Salvador El Salvador 6. Santiago Chile 7. São Paulo Brazil Europe SN C\n\n[NEW 2] herefore did not designate a national capital. The Second Continental Congress encompassed the period during which the United States declared independence but had not yet established a permanent national capital. Congress City Building Start date End date Duration Ref Albany Congress Albany, New York Stadt Huys June 19, 1754 July 11, 1754 22 days Stamp Act Congress New York, New York City Hall October 7, 1765 October 25, 1765 23 days First Continental Congress Philadelphia, Pennsylvania Carpenters' Hall September 5, 1774 October 26, 1774 1 month and 21 days Second Continental Congress Philadelphia, Pennsylvania Independence Hall May 10, 1775 July 4, 1776 ‡ 1 year, 1 month and 24 days ‡ (continuing after independence until December 12, 1776) State of Franklin\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] list Fox S Samuel Fox UK left back Leces Louis Celeste Lecesne UK left back Grevi Robert Greville UK far left Botanist Pease Jo Joseph Pease UK left Minister Tatum William Tatum UK right Webb Richard D. Webb Ireland right Publisher Scales Rev. Thomas Scales UK right front Minister James W William Ja...\n[KEPT 2] 8 36,420 94,326 9 Iowa IA Des Moines Dec 28, 1846 3,190,369 56,273 145,746 4 Kansas KS Topeka Wichita Jan 29, 1861 2,937,880 82,278 213,100 4 Kentucky KY Frankfort Louisville Jun 1, 1792 4,505,836 40,408 104,656 6 Louisiana LA Baton Rouge New Orleans Apr 30, 1812 4,657,757 52,378 135,659 6 Maine ME ...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 2 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 866, + "osl": 842, + "total_tokens": 1708, + "latency_ms": 5204.31, + "tokens_per_second": 161.79 + }, + "context": { + "num_documents_evaluated": 2, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "f8118960-a2a7-43f5-84fe-06e91401a5ab", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T02:52:54.387762Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: How many years after the founding of the 50th most populous US city, based on 2023 estimate population data, did Frank Fox receive UK Patent (1344259)?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] list Fox S Samuel Fox UK left back Leces Louis Celeste Lecesne UK left back Grevi Robert Greville UK far left Botanist Pease Jo Joseph Pease UK left Minister Tatum William Tatum UK right Webb Richard D. Webb Ireland right Publisher Scales Rev. Thomas Scales UK right front Minister James W William James UK right Minister Wilson William Wilson UK right Swan T Rev. Thomas Swan UK right Baptist Minister Steane Rev. Edward Steane UK right Minister Miller J Colonel Jonathon Miller US right front United States Army Officer Stuart Captain Charles Stuart Jamaica right Royal Navy Officer Jerem Sir John Jeremie Colonies right of centre Judge Stow Charles Stovel UK far right front Minister Peek Richard Peek UK far right front Sheriff of London Sturge John Sturge UK far\n\n[DOC 2] 8 36,420 94,326 9 Iowa IA Des Moines Dec 28, 1846 3,190,369 56,273 145,746 4 Kansas KS Topeka Wichita Jan 29, 1861 2,937,880 82,278 213,100 4 Kentucky KY Frankfort Louisville Jun 1, 1792 4,505,836 40,408 104,656 6 Louisiana LA Baton Rouge New Orleans Apr 30, 1812 4,657,757 52,378 135,659 6 Maine ME Augusta Portland Mar 15, 1820 1,362,359 35,380 91,633 2 Maryland MD Annapolis Baltimore Apr 28, 1788 6,177,224 12,406 32,131 8 Massachusetts MA Boston Feb 6, 1788 7,029,917 10,554 27,336 9 Michigan MI Lansing Detroit Jan 26, 1837 10,077,331 96,714 250,487 13 Minnesota MN Saint Paul Minneapolis May 11, 1858 5,706,494 86,936 225,163 8 Mississippi MS Jackson Dec 10, 1817 2,961,279 48,432 125,438 4 Missouri MO Jefferson City Kansas City Aug 10, 1821 6,154,913 69,707 1\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents do not provide the founding year of the 50th most populous US city (2023 estimates) nor the year Frank Fox received UK Patent 1344259. Document 1 lists names without dates, and Document 2 contains limited city founding dates for a few states, insufficient to identify the 50th most populous city or the patent date. Therefore, the required information to compute the year difference is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 735, + "osl": 101, + "total_tokens": 836, + "latency_ms": 4228.16, + "tokens_per_second": 23.89 + }, + "context": { + "kept_docs_count": 2, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "f4d19a99-3937-4363-9881-e52f56ea97fe", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T02:52:57.832676Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: How many years after the founding of the 50th most populous US city, based on 2023 estimate population data, did Frank Fox receive UK Patent (1344259)?\n\nDOCUMENTS:\n\n[DOC 1] list Fox S Samuel Fox UK left back Leces Louis Celeste Lecesne UK left back Grevi Robert Greville UK far left Botanist Pease Jo Joseph Pease UK left Minister Tatum William Tatum UK right Webb Richard D. Webb Ireland right Publisher Scales Rev. Thomas Scales UK right front Minister James W William James UK right Minister Wilson William Wilson UK right Swan T Rev. Thomas Swan UK right Baptist Minister Steane Rev. Edward Steane UK right Minister Miller J Colonel Jonathon Miller US right front United States Army Officer Stuart Captain Charles Stuart Jamaica right Royal Navy Officer Jerem Sir John Jeremie Colonies right of centre Judge Stow Charles Stovel UK far right front Minister Peek Richard Peek UK far right front Sheriff of London Sturge John Sturge UK far\n\n[DOC 2] 8 36,420 94,326 9 Iowa IA Des Moines Dec 28, 1846 3,190,369 56,273 145,746 4 Kansas KS Topeka Wichita Jan 29, 1861 2,937,880 82,278 213,100 4 Kentucky KY Frankfort Louisville Jun 1, 1792 4,505,836 40,408 104,656 6 Louisiana LA Baton Rouge New Orleans Apr 30, 1812 4,657,757 52,378 135,659 6 Maine ME Augusta Portland Mar 15, 1820 1,362,359 35,380 91,633 2 Maryland MD Annapolis Baltimore Apr 28, 1788 6,177,224 12,406 32,131 8 Massachusetts MA Boston Feb 6, 1788 7,029,917 10,554 27,336 9 Michigan MI Lansing Detroit Jan 26, 1837 10,077,331 96,714 250,487 13 Minnesota MN Saint Paul Minneapolis May 11, 1858 5,706,494 86,936 225,163 8 Mississippi MS Jackson Dec 10, 1817 2,961,279 48,432 125,438 4 Missouri MO Jefferson City Kansas City Aug 10, 1821 6,154,913 69,707 1\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 590, + "osl": 1, + "total_tokens": 591, + "latency_ms": 3444.35, + "tokens_per_second": 0.29 + }, + "context": { + "num_documents_used": 2, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:52:57.833336Z", + "summary": { + "total_llm_calls": 11, + "total_hops": 5, + "total_input_tokens": 11801, + "total_output_tokens": 4785, + "total_tokens": 16586, + "total_latency_ms": 56044.58, + "average_tokens_per_second": 102.5, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/List_of_United_States_cities_by_population", + "https://en.wikipedia.org/wiki/Arlington,_Texas", + "https://en.wikipedia.org/wiki/Rubik%27s_Cube" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "98 Years (Arlington, TX & Rubik's Cube)" + } + }, + { + "query_id": "19", + "original_query": "As of August 4, 2024, in what state was the first secretary of the latest United States federal executive department born?", + "timestamp_start": "2026-05-16T02:52:57.850112Z", + "llm_calls": [ + { + "call_id": "f53d0e55-a3dd-46e6-ba0f-bed6fdf5f413", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:53:00.536561Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 4, 2024, in what state was the first secretary of the latest United States federal executive department born?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"latest U.S. federal executive department\",\n \"first secretary Department of Homeland Security\",\n \"Tom Ridge birthplace state\"\n ],\n \"feedback\": \"Need to identify the most recently created U.S. executive department, its inaugural secretary, and the state of that secretary's birth.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 462, + "osl": 496, + "total_tokens": 958, + "latency_ms": 2686.31, + "tokens_per_second": 184.64 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "27a7367e-623f-45c2-98c1-1d1c62f91d62", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:53:21.746612Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of August 4, 2024, in what state was the first secretary of the latest United States federal executive department born?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] er of precedence of the United States as Former U. S. Cabinet Member Succeeded by John W. Snow as Former U. S. Cabinet Member\n\n[NEW 2] chist Objectivism Progressivism Socialism Abolitionism Constitutionalism Environmentalism Feminism Anarchism Capitalist Individualist Monarchism Nationalism Nativism Populism Protectionism Republicanism Political culture Political polarization Federalism State and territorial government Governors Legislatures ( list ) Courts Local government District of Columbia government District Council Mayor Superior Court Foreign relations Department of State Secretary of State : Marco Rubio Diplomatic missions of / in the United States Nationality law Passports Visa requirements Visa policy United States and the United Nations United States–Gulf Cooperation Council relations United States portal Politics portal v t e The United States federal executive departments are\n\n[NEW 3] r Becerra 2021–2025 Secretary of Housing and Urban Development Marcia Fudge 2021–2024 Adrianne Todman (acting) 2024–2025 Secretary of Transportation Pete Buttigieg 2021–2025 Secretary of Energy Jennifer Granholm 2021–2025 Secretary of Education Miguel Cardona 2021–2025 Secretary of Veterans Affairs Denis McDonough 2021–2025 Secretary of Homeland Security Alejandro Mayorkas 2021–2025 Administrator of the Environmental Protection Agency Michael S. Regan 2021–2024 Jane Nishida (acting) 2025–2025 Director of the Office of Management and Budget Shalanda Young 2021–2025 Director of National Intelligence Avril Haines 2021–2025 Director of the Central Intelligence Agency William J. Burns * 2021–2025 United States Trade Representative Katherine Tai 2021–2025 Ambassad\n\n[NEW 4] xecutive Schedule, Level I Website dhs. gov The United States secretary of homeland security is the head of the United States Department of Homeland Security , the federal department tasked with border control , counterterrorism and other aspects of public safety in the United States . The secretary is a member of the Cabinet of the United States . The position was created by the Homeland Security Act following the terrorist attacks of September 11, 2001 . The new department consisted primarily of components transferred from other Cabinet departments because of their role in homeland security , such as the Coast Guard , the Federal Protective Service , U. S. Customs and Border Protection (which includes the United States Border Patrol ), U. S. Immigration an\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 4 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 1, 1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 779, + "osl": 1170, + "total_tokens": 1949, + "latency_ms": 20705.9, + "tokens_per_second": 56.51 + }, + "context": { + "num_documents_evaluated": 4, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "7a53d3a2-6106-40f9-993c-312f2f938d14", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:53:24.599964Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 4, 2024, in what state was the first secretary of the latest United States federal executive department born?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] r Becerra 2021–2025 Secretary of Housing and Urban Development Marcia Fudge 2021–2024 Adrianne Todman (acting) 2024–2025 Secretary of Transportation Pete Buttigieg 2021–2025 Secretary of Energy Jennifer Granholm 2021–2025 Secretary of Education Miguel Cardona 2021–2025 Secretary of Veterans Affairs Denis McDonough 2021–2025 Secretary of Homeland Security Alejandro Mayorkas 2021–2025 Administrator of the Environmental Protection Agency Michael S. Regan 2021–2024 Jane Nishida (acting) 2025–2025 Director of the Office of Management and Budget Shalanda Young 2021–2025 Director of National Intelligence Avril Haines 2021–2025 Director of the Central Intelligence Agency William J. Burns * 2021–2025 United States Trade Representative Katherine Tai 2021–2025 Ambassad\n\n[DOC 2] xecutive Schedule, Level I Website dhs. gov The United States secretary of homeland security is the head of the United States Department of Homeland Security , the federal department tasked with border control , counterterrorism and other aspects of public safety in the United States . The secretary is a member of the Cabinet of the United States . The position was created by the Homeland Security Act following the terrorist attacks of September 11, 2001 . The new department consisted primarily of components transferred from other Cabinet departments because of their role in homeland security , such as the Coast Guard , the Federal Protective Service , U. S. Customs and Border Protection (which includes the United States Border Patrol ), U. S. Immigration an\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents list current secretaries and describe the role of the Secretary of Homeland Security, but they do not provide the name of the first secretary of the newest department nor any information about that person's birthplace. Therefore the required fact (state of birth of the first secretary of the latest department) is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 660, + "osl": 356, + "total_tokens": 1016, + "latency_ms": 2852.41, + "tokens_per_second": 124.81 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "ae5e9a5a-4d8c-492d-a8e6-c6c4e4ba2ac5", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:53:28.532728Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 4, 2024, in what state was the first secretary of the latest United States federal executive department born?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] r Becerra 2021–2025 Secretary of Housing and Urban Development Marcia Fudge 2021–2024 Adrianne Todman (acting) 2024–2025 Secretary of Transportation Pete Buttigieg 2021–2025 Secretary of Energy Jennifer Granholm 2021–2025 Secretary of Education Miguel Cardona 2021–2025 Secretary of Veterans Affairs Denis McDonough 2021–2025 Secretary of Homeland Security Alejandro Mayorkas 2021–2025 Administrator of the Environmental Protection Agency Michael S. Regan 2021–2024 Jane Nishida (acting) 2025–2025 Director of the Office of Management and Budget Shalanda Young 2021–2025 Director of National Intelligence Avril Haines 2021–2025 Director of the Central Intelligence Agency William J. Burns * 2021–2025 United States Trade Representative Katherine Tai 2021–2025 Ambassad\n\n[DOC 2] xecutive Schedule, Level I Website dhs. gov The United States secretary of homeland security is the head of the United States Department of Homeland Security , the federal department tasked with border control , counterterrorism and other aspects of public safety in the United States . The secretary is a member of the Cabinet of the United States . The position was created by the Homeland Security Act following the terrorist attacks of September 11, 2001 . The new department consisted primarily of components transferred from other Cabinet departments because of their role in homeland security , such as the Coast Guard , the Federal Protective Service , U. S. Customs and Border Protection (which includes the United States Border Patrol ), U. S. Immigration an\n\n\nSEARCH HISTORY: - Query: 'latest U.S. federal executive department' → 3 docs\n- Query: 'first secretary Department of Homeland Security' → 1 docs\n- Query: 'Tom Ridge birthplace state' → 0 docs\n\nFEEDBACK: Need to identify the most recently created U.S. executive department, its inaugural secretary, and the state of that secretary's birth.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Tom Ridge birthplace\",\n \"Tom Ridge born\",\n \"Department of Homeland Security first secretary\"\n ],\n \"feedback\": \"Need to confirm the newest cabinet-level executive department (likely DHS), identify its inaugural secretary (Tom Ridge), and retrieve his birth state.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 811, + "osl": 63, + "total_tokens": 874, + "latency_ms": 3932.22, + "tokens_per_second": 16.02 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "2612ae42-b6f5-42a3-87f9-e4d925ae343c", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T02:53:31.210230Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 4, 2024, in what state was the first secretary of the latest United States federal executive department born?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] r Becerra 2021–2025 Secretary of Housing and Urban Development Marcia Fudge 2021–2024 Adrianne Todman (acting) 2024–2025 Secretary of Transportation Pete Buttigieg 2021–2025 Secretary of Energy Jennifer Granholm 2021–2025 Secretary of Education Miguel Cardona 2021–2025 Secretary of Veterans Affairs Denis McDonough 2021–2025 Secretary of Homeland Security Alejandro Mayorkas 2021–2025 Administrator of the Environmental Protection Agency Michael S. Regan 2021–2024 Jane Nishida (acting) 2025–2025 Director of the Office of Management and Budget Shalanda Young 2021–2025 Director of National Intelligence Avril Haines 2021–2025 Director of the Central Intelligence Agency William J. Burns * 2021–2025 United States Trade Representative Katherine Tai 2021–2025 Ambassad\n\n[DOC 2] xecutive Schedule, Level I Website dhs. gov The United States secretary of homeland security is the head of the United States Department of Homeland Security , the federal department tasked with border control , counterterrorism and other aspects of public safety in the United States . The secretary is a member of the Cabinet of the United States . The position was created by the Homeland Security Act following the terrorist attacks of September 11, 2001 . The new department consisted primarily of components transferred from other Cabinet departments because of their role in homeland security , such as the Coast Guard , the Federal Protective Service , U. S. Customs and Border Protection (which includes the United States Border Patrol ), U. S. Immigration an\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents list current secretaries and describe the Department of Homeland Security, but they do not provide the name of the first secretary (Tom Ridge) nor any information about his birthplace. Therefore, the required fact— the state in which the first secretary of the latest federal executive department was born— is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 660, + "osl": 307, + "total_tokens": 967, + "latency_ms": 2152.02, + "tokens_per_second": 142.66 + }, + "context": { + "kept_docs_count": 2, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "ff892e49-2608-4561-b31d-bcbddf0462c6", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T02:53:34.042507Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 4, 2024, in what state was the first secretary of the latest United States federal executive department born?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] r Becerra 2021–2025 Secretary of Housing and Urban Development Marcia Fudge 2021–2024 Adrianne Todman (acting) 2024–2025 Secretary of Transportation Pete Buttigieg 2021–2025 Secretary of Energy Jennifer Granholm 2021–2025 Secretary of Education Miguel Cardona 2021–2025 Secretary of Veterans Affairs Denis McDonough 2021–2025 Secretary of Homeland Security Alejandro Mayorkas 2021–2025 Administrator of the Environmental Protection Agency Michael S. Regan 2021–2024 Jane Nishida (acting) 2025–2025 Director of the Office of Management and Budget Shalanda Young 2021–2025 Director of National Intelligence Avril Haines 2021–2025 Director of the Central Intelligence Agency William J. Burns * 2021–2025 United States Trade Representative Katherine Tai 2021–2025 Ambassad\n\n[DOC 2] xecutive Schedule, Level I Website dhs. gov The United States secretary of homeland security is the head of the United States Department of Homeland Security , the federal department tasked with border control , counterterrorism and other aspects of public safety in the United States . The secretary is a member of the Cabinet of the United States . The position was created by the Homeland Security Act following the terrorist attacks of September 11, 2001 . The new department consisted primarily of components transferred from other Cabinet departments because of their role in homeland security , such as the Coast Guard , the Federal Protective Service , U. S. Customs and Border Protection (which includes the United States Border Patrol ), U. S. Immigration an\n\n\nSEARCH HISTORY: - Query: 'latest U.S. federal executive department' → 3 docs\n- Query: 'first secretary Department of Homeland Security' → 1 docs\n- Query: 'Tom Ridge birthplace state' → 0 docs\n- Query: 'Tom Ridge birthplace' → 0 docs\n- Query: 'Tom Ridge born' → 0 docs\n- Query: 'Department of Homeland Security first secretary' → 0 docs\n\nFEEDBACK: Need to identify the most recently created U.S. executive department, its inaugural secretary, and the state of that secretary's birth.\nNeed to confirm the newest cabinet-level executive department (likely DHS), identify its inaugural secretary (Tom Ridge), and retrieve his birth state.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Tom Ridge\",\n \"Tom Ridge biography\",\n \"Thomas Ridge birthplace\"\n ],\n \"feedback\": \"Needed: information on the inaugural secretary of the most recently created U.S. cabinet department and his birth state. Prior attempts using 'Tom Ridge birthplace' failed; switching to broader entity searches and using his full legal name should retrieve the Wikipedia infobox with birthplace details.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 881, + "osl": 87, + "total_tokens": 968, + "latency_ms": 2831.45, + "tokens_per_second": 30.73 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "da82c854-42b1-4960-8ef2-1bbdc22dba4f", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T02:53:39.044224Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of August 4, 2024, in what state was the first secretary of the latest United States federal executive department born?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] n English school at age five. In 1752, at age nine, he attended a local school run by a Presbyterian minister and also began studying the natural world, which he grew to love. He studied Latin , Greek , and French , and began learning to ride horses. Thomas read books from his father's modest library. He was taught from 1758 to 1760 by the Reverend James Maury near Gordonsville, Virginia , where he studied history, science, and the classics while boarding with Maury's family. Jefferson came to know various American Indians , including Cherokee chief Ostenaco , who often stopped at Shadwell to visit on their way to Williamsburg to trade. In Williamsburg, the young Jefferson met and came to admire Patrick Henry . Thomas's father died in 1757, and his estate wa\n\n[NEW 2] was born on February 4, 1903, near Loma Vista, Texas , the daughter of Rosa ( née Ross ) and Thomas Holdsworth. She was the fourth of seven children. Her father, the son of an English schoolteacher, emigrated from England to Texas with his parents in 1880. Her mother was a schoolteacher who grew up and worked in Kerrville, Texas . Both her parents were devout, tithing Baptists . In 1924, she married Howard Edward Butt . Howard owned a small grocery store in Kerrville. Over time, her husband's business prospered and expanded, enabling Mary Elizabeth to take an active role in supporting numerous and varied charitable causes. Her husband stated: \"I make the money, and Mary spends it (on worthy causes). And I am glad she does.\" In 1940, they moved to Corpus Chr\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] r Becerra 2021–2025 Secretary of Housing and Urban Development Marcia Fudge 2021–2024 Adrianne Todman (acting) 2024–2025 Secretary of Transportation Pete Buttigieg 2021–2025 Secretary of Energy Jennifer Granholm 2021–2025 Secretary of Education Miguel Cardona 2021–2025 Secretary of Veterans Affairs ...\n[KEPT 2] xecutive Schedule, Level I Website dhs. gov The United States secretary of homeland security is the head of the United States Department of Homeland Security , the federal department tasked with border control , counterterrorism and other aspects of public safety in the United States . The secretary...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 2 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 770, + "osl": 908, + "total_tokens": 1678, + "latency_ms": 4414.35, + "tokens_per_second": 205.69 + }, + "context": { + "num_documents_evaluated": 2, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "5cbbdf71-c84a-4ef4-836d-48b09ab91020", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T02:53:41.067997Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 4, 2024, in what state was the first secretary of the latest United States federal executive department born?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] r Becerra 2021–2025 Secretary of Housing and Urban Development Marcia Fudge 2021–2024 Adrianne Todman (acting) 2024–2025 Secretary of Transportation Pete Buttigieg 2021–2025 Secretary of Energy Jennifer Granholm 2021–2025 Secretary of Education Miguel Cardona 2021–2025 Secretary of Veterans Affairs Denis McDonough 2021–2025 Secretary of Homeland Security Alejandro Mayorkas 2021–2025 Administrator of the Environmental Protection Agency Michael S. Regan 2021–2024 Jane Nishida (acting) 2025–2025 Director of the Office of Management and Budget Shalanda Young 2021–2025 Director of National Intelligence Avril Haines 2021–2025 Director of the Central Intelligence Agency William J. Burns * 2021–2025 United States Trade Representative Katherine Tai 2021–2025 Ambassad\n\n[DOC 2] xecutive Schedule, Level I Website dhs. gov The United States secretary of homeland security is the head of the United States Department of Homeland Security , the federal department tasked with border control , counterterrorism and other aspects of public safety in the United States . The secretary is a member of the Cabinet of the United States . The position was created by the Homeland Security Act following the terrorist attacks of September 11, 2001 . The new department consisted primarily of components transferred from other Cabinet departments because of their role in homeland security , such as the Coast Guard , the Federal Protective Service , U. S. Customs and Border Protection (which includes the United States Border Patrol ), U. S. Immigration an\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents identify the Department of Homeland Security as a federal executive department and list its current secretary (Alejandro Mayorkas), but they do not provide the name of the first secretary of that department nor the state where that individual was born. Without the first secretary's identity and birthplace, the question cannot be answered.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 660, + "osl": 415, + "total_tokens": 1075, + "latency_ms": 2022.94, + "tokens_per_second": 205.15 + }, + "context": { + "kept_docs_count": 2, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "a89adaee-98f4-4b21-848c-22baf56374a6", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T02:53:45.037681Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 4, 2024, in what state was the first secretary of the latest United States federal executive department born?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] r Becerra 2021–2025 Secretary of Housing and Urban Development Marcia Fudge 2021–2024 Adrianne Todman (acting) 2024–2025 Secretary of Transportation Pete Buttigieg 2021–2025 Secretary of Energy Jennifer Granholm 2021–2025 Secretary of Education Miguel Cardona 2021–2025 Secretary of Veterans Affairs Denis McDonough 2021–2025 Secretary of Homeland Security Alejandro Mayorkas 2021–2025 Administrator of the Environmental Protection Agency Michael S. Regan 2021–2024 Jane Nishida (acting) 2025–2025 Director of the Office of Management and Budget Shalanda Young 2021–2025 Director of National Intelligence Avril Haines 2021–2025 Director of the Central Intelligence Agency William J. Burns * 2021–2025 United States Trade Representative Katherine Tai 2021–2025 Ambassad\n\n[DOC 2] xecutive Schedule, Level I Website dhs. gov The United States secretary of homeland security is the head of the United States Department of Homeland Security , the federal department tasked with border control , counterterrorism and other aspects of public safety in the United States . The secretary is a member of the Cabinet of the United States . The position was created by the Homeland Security Act following the terrorist attacks of September 11, 2001 . The new department consisted primarily of components transferred from other Cabinet departments because of their role in homeland security , such as the Coast Guard , the Federal Protective Service , U. S. Customs and Border Protection (which includes the United States Border Patrol ), U. S. Immigration an\n\n\nSEARCH HISTORY: - Query: 'latest U.S. federal executive department' → 3 docs\n- Query: 'first secretary Department of Homeland Security' → 1 docs\n- Query: 'Tom Ridge birthplace state' → 0 docs\n- Query: 'Tom Ridge birthplace' → 0 docs\n- Query: 'Tom Ridge born' → 0 docs\n- Query: 'Department of Homeland Security first secretary' → 0 docs\n- Query: 'Tom Ridge' → 0 docs\n- Query: 'Tom Ridge biography' → 0 docs\n- Query: 'Thomas Ridge birthplace' → 2 docs\n\nFEEDBACK: Need to identify the most recently created U.S. executive department, its inaugural secretary, and the state of that secretary's birth.\nNeed to confirm the newest cabinet-level executive department (likely DHS), identify its inaugural secretary (Tom Ridge), and retrieve his birth state.\nNeeded: information on the inaugural secretary of the most recently created U.S. cabinet department and his birth state. Prior attempts using 'Tom Ridge birthplace' failed; switching to broader entity searches and using his full legal name should retrieve the Wikipedia infobox with birthplace details.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Tom Ridge\",\n \"Thomas Ridge birthplace\",\n \"Tom Ridge biography\"\n ],\n \"feedback\": \"Need the Wikipedia/biography page for Tom Ridge, the inaugural Secretary of Homeland Security, to locate his birth state (Pennsylvania).\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1033, + "osl": 262, + "total_tokens": 1295, + "latency_ms": 3969.06, + "tokens_per_second": 66.01 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "f61fc2b8-89f5-48aa-9341-c202c9116594", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T02:53:49.706254Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 4, 2024, in what state was the first secretary of the latest United States federal executive department born?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] r Becerra 2021–2025 Secretary of Housing and Urban Development Marcia Fudge 2021–2024 Adrianne Todman (acting) 2024–2025 Secretary of Transportation Pete Buttigieg 2021–2025 Secretary of Energy Jennifer Granholm 2021–2025 Secretary of Education Miguel Cardona 2021–2025 Secretary of Veterans Affairs Denis McDonough 2021–2025 Secretary of Homeland Security Alejandro Mayorkas 2021–2025 Administrator of the Environmental Protection Agency Michael S. Regan 2021–2024 Jane Nishida (acting) 2025–2025 Director of the Office of Management and Budget Shalanda Young 2021–2025 Director of National Intelligence Avril Haines 2021–2025 Director of the Central Intelligence Agency William J. Burns * 2021–2025 United States Trade Representative Katherine Tai 2021–2025 Ambassad\n\n[DOC 2] xecutive Schedule, Level I Website dhs. gov The United States secretary of homeland security is the head of the United States Department of Homeland Security , the federal department tasked with border control , counterterrorism and other aspects of public safety in the United States . The secretary is a member of the Cabinet of the United States . The position was created by the Homeland Security Act following the terrorist attacks of September 11, 2001 . The new department consisted primarily of components transferred from other Cabinet departments because of their role in homeland security , such as the Coast Guard , the Federal Protective Service , U. S. Customs and Border Protection (which includes the United States Border Patrol ), U. S. Immigration an\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents list current secretaries and describe the Department of Homeland Security, but they do not provide the name of the first secretary of the latest department nor the state of that secretary's birth. Therefore, the required facts to answer the question are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 660, + "osl": 694, + "total_tokens": 1354, + "latency_ms": 4080.96, + "tokens_per_second": 170.06 + }, + "context": { + "kept_docs_count": 2, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "cb8c88bf-0608-46a5-b820-72862df2eee0", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T02:53:51.040220Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: As of August 4, 2024, in what state was the first secretary of the latest United States federal executive department born?\n\nDOCUMENTS:\n\n[DOC 1] r Becerra 2021–2025 Secretary of Housing and Urban Development Marcia Fudge 2021–2024 Adrianne Todman (acting) 2024–2025 Secretary of Transportation Pete Buttigieg 2021–2025 Secretary of Energy Jennifer Granholm 2021–2025 Secretary of Education Miguel Cardona 2021–2025 Secretary of Veterans Affairs Denis McDonough 2021–2025 Secretary of Homeland Security Alejandro Mayorkas 2021–2025 Administrator of the Environmental Protection Agency Michael S. Regan 2021–2024 Jane Nishida (acting) 2025–2025 Director of the Office of Management and Budget Shalanda Young 2021–2025 Director of National Intelligence Avril Haines 2021–2025 Director of the Central Intelligence Agency William J. Burns * 2021–2025 United States Trade Representative Katherine Tai 2021–2025 Ambassad\n\n[DOC 2] xecutive Schedule, Level I Website dhs. gov The United States secretary of homeland security is the head of the United States Department of Homeland Security , the federal department tasked with border control , counterterrorism and other aspects of public safety in the United States . The secretary is a member of the Cabinet of the United States . The position was created by the Homeland Security Act following the terrorist attacks of September 11, 2001 . The new department consisted primarily of components transferred from other Cabinet departments because of their role in homeland security , such as the Coast Guard , the Federal Protective Service , U. S. Customs and Border Protection (which includes the United States Border Patrol ), U. S. Immigration an\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 514, + "osl": 313, + "total_tokens": 827, + "latency_ms": 1333.14, + "tokens_per_second": 234.78 + }, + "context": { + "num_documents_used": 2, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:53:51.040959Z", + "summary": { + "total_llm_calls": 11, + "total_hops": 5, + "total_input_tokens": 7890, + "total_output_tokens": 5071, + "total_tokens": 12961, + "total_latency_ms": 50980.76, + "average_tokens_per_second": 130.64, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/United_States_federal_executive_departments#Former_departments", + "https://en.wikipedia.org/wiki/United_States_Secretary_of_Homeland_Security", + "https://en.wikipedia.org/wiki/Tom_Ridge" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "Pennsylvania" + } + }, + { + "query_id": "20", + "original_query": "As of August 1 2024, what is the most recently described genus of Colosteidae?", + "timestamp_start": "2026-05-16T02:53:51.059498Z", + "llm_calls": [ + { + "call_id": "8edd9d70-1530-4079-83ff-71eedb862b49", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:53:54.021896Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 1 2024, what is the most recently described genus of Colosteidae?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Colosteidae genus\",\n \"new colosteidae genus 2023\",\n \"Colosteidae recent genus\"\n ],\n \"feedback\": \"Need to identify all genera within Colosteidae and determine which was most recently described (likely a 2022‑2024 taxonomic paper). First query gets the baseline list; second narrows to recent publications; third checks broader recent mentions.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 458, + "osl": 303, + "total_tokens": 761, + "latency_ms": 2962.23, + "tokens_per_second": 102.29 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "162d3efc-dc2c-44c6-8b18-1494a4b12281", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:54:01.240070Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of August 1 2024, what is the most recently described genus of Colosteidae?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Extinct family of tetrapodomorphs Colosteidae Temporal range: Carboniferous ( Viséan –late Moscovian ) PreꞒ Ꞓ O S D C P T J K Pg N Life restoration and size comparation of three members of Colosteidae Scientific classification Kingdom: Animalia Phylum: Chordata Clade : Stegocephali Family: † Colosteidae Cope , 1875 Type genus † Colosteus Cope, 1871 Genera † Colosteus † Deltaherpeton † Greererpeton † Pholidogaster Colosteidae is a family of stegocephalians ( stem-group tetrapods ) that lived in the Carboniferous period. They possessed a variety of characteristics from different tetrapod or stem-tetrapod groups, which made them historically difficult to classify. They are now considered to be part of a lineage intermediate between the earliest Devonian terrest\n\n[NEW 2] 12 species) Chernetidae Menge, 1855 (120 genera, 728 species) Chthoniidae Daday, 1888 (54 genera, 909 species) Feaellidae Ellingsen, 1906 (8 genus, 37 species) Garypidae Simon, 1879 (11 genera, 110 species) Garypinidae Daday, 1888 (21 genera, 94 species) Geogarypidae Chamberlin, 1930 (2 genera, 81 species) Gymnobisiidae Beier, 1947 (4 genera, 17 species) Hyidae Chamberlin, 1930 (2 genera, 41 species) Ideoroncidae Chamberlin, 1930 (15 genera, 86 species) Larcidae Harvey, 1992 (1 genus, 15 species) Menthidae Chamberlin, 1930 (5 genera, 12 species) Neobisiidae Chamberlin, 1930 (34 genera, 748 species) Olpiidae Banks, 1895 (24 genera, 211 species) Parahyidae Harvey, 1992 (1 genus, 1 species) Pseudochiridiidae Chamberlin, 1923 (2 genera, 13 species) Pseudogarypid\n\n[NEW 3] Extinct genus of tetrapodomorphs Deltaherpeton Temporal range: 330 Ma PreꞒ Ꞓ O S D C P T J K Pg N ↓ Life restoration Scientific classification Kingdom: Animalia Phylum: Chordata Clade : Stegocephali Family: † Colosteidae Genus: † Deltaherpeton Bolt & Lombard, 2010 Species D. hiemstrae Bolt & Lombard, 2010 ( type ) Deltaherpeton is an extinct genus of colosteid from middle Mississippian (late Viséan age) deposits of Delta , Iowa , United States . It was first named by John R. Bolt and R. Eric Lombard in 2010 and the type species is Deltaherpeton hiemstrae . Deltaherpeton can be differentiated from other colosteids due to possessing several unique bones along the midline of the skull, separating paired skull bones which typically contact each other along the m\n\n[NEW 4] on Schlesinger, 1917 †Family Amebelodontidae Barbour, 1927 † Afromastodon Pickford, 2003 † Progomphotherium Pickford, 2003 † Eurybelodon Lambert, 2016 † Serbelodon Frick, 1933 † Archaeobelodon Tassy, 1984 † Protanancus Arambourg, 1945 † Amebelodon Barbour, 1927 † Konobelodon Lambert, 1990 † Torynobelodon Barbour, 1929 † Aphanobelodon Wang et al. , 2016 † Platybelodon Borissiak, 1928 †Family Gomphotheriidae Hay, 1922 ( paraphyletic ) † Gomphotherium Burmeister, 1837 † Blancotherium May, 2019 † Gnathabelodon Barbour & Sternberg, 1935 † Eubelodon Barbour, 1914 † Megabelodon Barbour, 1914 † Stegomastodon Pohlig, 1912 † Sinomastodon Tobien et al. , 1986 † Notiomastodon Cabrera, 1929 † Rhynchotherium Falconer, 1868 † Cuvieronius Osborn, 1923 Superfamily Elephantoi\n\n[NEW 5] Genus of fishes Moxostoma Robust redhorse ( M. robustum ) Scientific classification Kingdom: Animalia Phylum: Chordata Class: Actinopterygii Order: Cypriniformes Suborder: Catostomoidei Family: Catostomidae Subfamily: Catostominae Genus: Moxostoma Rafinesque , 1820 Type species Catostomus anisurus Rafinesque 1820 Species 25, see text . Synonyms Lagochila D. S. Jordan & Brayton , 1877 Megapharynx Legendre , 1942 Placopharynx Cope , 1870 Ptychostomus Agassiz , 1855 Quassilabia D. S. Jordan & Brayton, 1878 Scartomyzon Fowler , 1913 Teretulus Rafinesque , 1820 Moxostoma , the redhorses or jumprocks , is a genus of North American ray-finned fish in the family Catostomidae . Redhorses are variable in size, geographic location, and other ecological traits such as s\n\n[NEW 6] 2023 Synsphyronus lathrius Harvey, 1987 Synsphyronus leo Harvey, 1987 Synsphyronus lineatus Beier, 1966 Synsphyronus magnus Hoff, 1947 Synsphyronus marinae Cullen and Harvey, 2021 Synsphyronus meganennus Harvey, 1987 Synsphyronus melanochelatus (Chamberlin, 1930) Synsphyronus mimetus Chamberlin, 1943 Synsphyronus mimulus Chamberlin, 1943 Synsphyronus niger Hoff, 1947 Synsphyronus nullarborensis Beier, 1969 Synsphyronus paradoxus Chamberlin, 1930 Synsphyronus patricki Cullen and Harvey, 2021 Synsphyronus pharangites Cullen and Harvey, 2021 Synsphyronus platnicki Harvey, 2020 Synsphyronus samueli Cullen and Harvey, 2021 Synsphyronus sertus Cullen and Harvey, 2021 Synsphyronus silveirai Harvey, 1987 Synsphyronus spatiosus Harvey, 2022 Synsphyronus tenuis Harvey\n\n[NEW 7] ed August 22, 2023. ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ [ permanent dead link ] ^ ^ ^ (in Spanish) Instituto Geográfico Nacional Archived 30 October 2008 at the Wayback Machine ^ ^ a b c ^ ^ ^ a b c d e f g h i j k l m n o p q ^ ^ ^ ^ [ permanent dead link ] ^ ^ Estadísticas del Gobierno de Canarias Archived 23 March 2010 at the Wayback Machine ^ a b ^ ^ ^ ^ ^ The Recently Extinct Plants and Animals Database Extinct Reptiles: Geochelone burchardi . Archived 19 June 2016 at the Wayback Machine Consultado el 17 de junio de 2016. ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ Estimate of 1 January 2023: from Instituto Nacional de Estadística, Madrid. ^ Census of 1 November 2001: from Instituto Nacional de Estadística, Madrid. ^ Census of 1 November 2011: from Instituto Nacional de Estadística, Mad\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 7 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 0, 1, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1974, + "osl": 673, + "total_tokens": 2647, + "latency_ms": 6577.93, + "tokens_per_second": 102.31 + }, + "context": { + "num_documents_evaluated": 7, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "44508b1e-07f7-4361-8525-1ea58f89b05c", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:54:07.562236Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 1 2024, what is the most recently described genus of Colosteidae?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Extinct family of tetrapodomorphs Colosteidae Temporal range: Carboniferous ( Viséan –late Moscovian ) PreꞒ Ꞓ O S D C P T J K Pg N Life restoration and size comparation of three members of Colosteidae Scientific classification Kingdom: Animalia Phylum: Chordata Clade : Stegocephali Family: † Colosteidae Cope , 1875 Type genus † Colosteus Cope, 1871 Genera † Colosteus † Deltaherpeton † Greererpeton † Pholidogaster Colosteidae is a family of stegocephalians ( stem-group tetrapods ) that lived in the Carboniferous period. They possessed a variety of characteristics from different tetrapod or stem-tetrapod groups, which made them historically difficult to classify. They are now considered to be part of a lineage intermediate between the earliest Devonian terrest\n\n[DOC 2] Extinct genus of tetrapodomorphs Deltaherpeton Temporal range: 330 Ma PreꞒ Ꞓ O S D C P T J K Pg N ↓ Life restoration Scientific classification Kingdom: Animalia Phylum: Chordata Clade : Stegocephali Family: † Colosteidae Genus: † Deltaherpeton Bolt & Lombard, 2010 Species D. hiemstrae Bolt & Lombard, 2010 ( type ) Deltaherpeton is an extinct genus of colosteid from middle Mississippian (late Viséan age) deposits of Delta , Iowa , United States . It was first named by John R. Bolt and R. Eric Lombard in 2010 and the type species is Deltaherpeton hiemstrae . Deltaherpeton can be differentiated from other colosteids due to possessing several unique bones along the midline of the skull, separating paired skull bones which typically contact each other along the m\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": true,\n \"reasoning\": \"Document 2 specifies that the genus Deltaherpeton (a member of Colosteidae) was named by Bolt & Lombard in 2010. Document 1 lists the known genera of Colosteidae (Colosteus, Deltaherpeton, Greererpeton, Pholidogaster) but provides no later description dates. Since 2010 is later than the known description dates for the other listed genera, the docs contain enough information to determine that Deltaherpeton is the most recently described genus of Colosteidae as of August 1 2024.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 711, + "osl": 429, + "total_tokens": 1140, + "latency_ms": 6321.29, + "tokens_per_second": 67.87 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "623ae685-50cd-4475-9924-c0e2938edb91", + "component": "answer_generator", + "hop_count": 2, + "timestamp": "2026-05-16T02:54:08.324250Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: As of August 1 2024, what is the most recently described genus of Colosteidae?\n\nDOCUMENTS:\n\n[DOC 1] Extinct family of tetrapodomorphs Colosteidae Temporal range: Carboniferous ( Viséan –late Moscovian ) PreꞒ Ꞓ O S D C P T J K Pg N Life restoration and size comparation of three members of Colosteidae Scientific classification Kingdom: Animalia Phylum: Chordata Clade : Stegocephali Family: † Colosteidae Cope , 1875 Type genus † Colosteus Cope, 1871 Genera † Colosteus † Deltaherpeton † Greererpeton † Pholidogaster Colosteidae is a family of stegocephalians ( stem-group tetrapods ) that lived in the Carboniferous period. They possessed a variety of characteristics from different tetrapod or stem-tetrapod groups, which made them historically difficult to classify. They are now considered to be part of a lineage intermediate between the earliest Devonian terrest\n\n[DOC 2] Extinct genus of tetrapodomorphs Deltaherpeton Temporal range: 330 Ma PreꞒ Ꞓ O S D C P T J K Pg N ↓ Life restoration Scientific classification Kingdom: Animalia Phylum: Chordata Clade : Stegocephali Family: † Colosteidae Genus: † Deltaherpeton Bolt & Lombard, 2010 Species D. hiemstrae Bolt & Lombard, 2010 ( type ) Deltaherpeton is an extinct genus of colosteid from middle Mississippian (late Viséan age) deposits of Delta , Iowa , United States . It was first named by John R. Bolt and R. Eric Lombard in 2010 and the type species is Deltaherpeton hiemstrae . Deltaherpeton can be differentiated from other colosteids due to possessing several unique bones along the midline of the skull, separating paired skull bones which typically contact each other along the m\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "The most recently described genus of Colosteidae is **† Deltaherpeton** (named in 2010).", + "finish_reason": "stop" + }, + "metrics": { + "isl": 563, + "osl": 155, + "total_tokens": 718, + "latency_ms": 761.37, + "tokens_per_second": 203.58 + }, + "context": { + "num_documents_used": 2, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:54:08.324966Z", + "summary": { + "total_llm_calls": 4, + "total_hops": 2, + "total_input_tokens": 3706, + "total_output_tokens": 1560, + "total_tokens": 5266, + "total_latency_ms": 16622.82, + "average_tokens_per_second": 119.01, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Colosteidae", + "https://en.wikipedia.org/wiki/Deltaherpeton", + "https://en.wikipedia.org/wiki/Greererpeton", + "https://en.wikipedia.org/wiki/Pholidogaster" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "The most recently described genus of Colosteidae is **† Deltaherpeton** (named in 2010).", + "ground_truth_answer": "Deltaherpeton, first described in 2010" + } + }, + { + "query_id": "21", + "original_query": "Małgorzata Rożniecka is a model who won the title of Miss International. What is the difference in title years from when she won and the pageant winner who was murdered by her stalker?", + "timestamp_start": "2026-05-16T02:54:08.342313Z", + "llm_calls": [ + { + "call_id": "40580272-cf83-48cf-a755-27e046fb9175", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:54:31.733498Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: Małgorzata Rożniecka is a model who won the title of Miss International. What is the difference in title years from when she won and the pageant winner who was murdered by her stalker?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Małgorzata Rożniecka Miss International year\",\n \"beauty queen murdered by stalker pageant winner\",\n \"Miss International winner murdered stalker year\"\n ],\n \"feedback\": \"First query aims to locate the year Małgorzata Rożniecka won Miss International. The second and third queries target the identity and title year of the pageant winner who was later murdered by her stalker, enabling calculation of the difference in title years.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 466, + "osl": 1050, + "total_tokens": 1516, + "latency_ms": 23391.03, + "tokens_per_second": 44.89 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "2e91e268-676e-48ac-b468-3e0de343b85a", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:54:36.216543Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: Małgorzata Rożniecka is a model who won the title of Miss International. What is the difference in title years from when she won and the pageant winner who was murdered by her stalker?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Polish model and beauty queen (born 1978) Małgorzata Rożniecka (born 1978, in Szczecin ) is a Polish model and beauty queen who won Miss International 2001 . Her 2001 victory broke the four-year monopoly of Latin American countries of the crown. She is 1.78 m tall. References ^ Retrieved on 11 Apr 2008. External links Małgorzata Rożniecka profile Awards and achievements Preceded by Vivian Urdaneta Miss International 2001 Succeeded by Christina Sawaya This Polish biographical article is a stub . You can help Wikipedia by adding missing information . v t e This biography article about a European contestant in a beauty pageant is a stub . You can help Wikipedia by adding missing information . v t e\n\n[NEW 2] Beauty pageant competition, Organization Miss International Type International women's beauty pageant Headquarters Tokyo, Japan First edition 1960 Most recent edition 2025 Current titleholder Catalina Duque Colombia President Akemi Shimomura Language English Website www . miss-international . org Miss International ( Miss International Beauty or The International Beauty Pageant ) is a Japan-based international major beauty pageant organized by the International Culture Association. First held in 1960, it is the fourth largest pageant in the world in terms of the number of national winners participating in the international contest. Along with Miss World , Miss Universe , and Miss Earth , the pageant is one of the Big Four beauty pageants . The Miss Internati\n\n[NEW 3] 10 2004: María Gabriela Pérez Della Pía – 2nd runner-up (Dreamgirl of the Year International) 2005: Winnela Álvarez – Unplaced 2006: Yariagny Quintero Santiago – Unplaced 2008: Estefanía del Carmen Di Filippo Brazon – Unplaced 2009: Jéssica Ibarra – 1st runner-up (Miss Tourism Queen of the Year International) 2010: Stephany Andreína González Socorro – 2nd runner-up (Miss Tourism Global; Miss Photogenic) 2011: Did not compete 2012: Marielis Alejandra Castellanos Pérez – 2nd runner-up (Miss Tourism Global) 2013: María Luisa Valencia Lera – Did not compete 2014: Faddya Ysabel Halabi Troisi – Miss Tourism International (Miss Elegant) 2016: Thea Cleo Nice Sichini Comunian – Unplaced 2017: Diana Silva – Did not compete 2018: Michel Gerardine Vivas López – Top 10\n\n[NEW 4] killer\" Archived 30 December 2016 at the Wayback Machine . The Guardian . ^ [2002] EWCA Crim 2912 , 22. ^ a b [2002] EWCA Crim 2912 , 392–404; Kennedy 2002 . ^ [2002] EWCA Crim 2912 , 370 ^ [2002] EWCA Crim 2912 , 373–377. ^ Lee 2015 , 156, 412. ^ Lee 2015 , 157. ^ [2002] EWCA Crim 2912 , 23. ^ [2002] EWCA Crim 2912 , 21, 30. ^ Lee 2015 , 218. ^ [2002] EWCA Crim 2912 , 146. ^ [2002] EWCA Crim 2912 , 12. ^ [2002] EWCA Crim 2912 , 13; Lee 2015 , 104. ^ [2002] EWCA Crim 2912 , 69; Lee 2015 , 318. ^ [2002] EWCA Crim 2912 , 13. ^ [2002] EWCA Crim 2912 , 21. ^ a b [2002] EWCA Crim 2912 , 66–67. ^ Lee 2015 , 249–250. ^ [2002] EWCA Crim 2912 , 430. ^ a b c [2002] EWCA Crim 2912 , 24; for \"gone berserk\", \"Jury told of Bamber's 'fatal mistake'\", The Guardian , 22 Octo\n\n[NEW 5] U Bitch\" inflicted emotional distress, were slanderous, and invaded her privacy. The case was later dismissed. Murder and aftermath East Flamingo Road and Koval Lane, where the murder occurred On the night of September 7, 1996, Shakur was in Paradise, Nevada , to attend the Bruce Seldon vs. Mike Tyson boxing match with Suge Knight at the MGM Grand . Afterward in the lobby one of Knight's associates spotted Orlando Anderson , a South Side Compton Crip , and told Shakur he had tried to rob them earlier that year. The hotel's surveillance footage shows the ensuing assault on Anderson. Shakur soon stopped by his hotel room and then headed with Knight to his Death Row nightclub, Club 662, in a black BMW 750iL sedan, part of a larger convoy. At about 11 p. m. on L\n\n[NEW 6] the massacre which included \"Go to the outside hill, wait. When first bombs go off, attack.\" ^ All times are in Mountain Daylight Time , UTC-6 . ^ On the corner of South Wadsworth Boulevard and Ken Caryl Avenue. ^ Hochhalter would die of her injuries on February 16, 2025, at the age of 43. Her death certificate lists her cause of death as sepsis due to Streptococcus pyogenes . Her death was officially ruled a homicide. ^ Gardner was not wearing his prescription eyeglasses. ^ Prior to her murder, Bernall had her hands on the sides of her head. ^ The report notes that such a profile should not be construed as a direct psychiatric diagnosis, which is based on face-to-face interviews, formal psychological testing , and collection of collateral information. ^ In\n\n[NEW 7] his period as an addict and burglar, he accidentally kills M. DuPlessis, a leader of one of the many separatist Québécois organizations featured in the novel. Gately is visited by the ghosts of James O. Incandenza and Lyle. Joelle van Dyne , also known as \"Madame Psychosis\" (cf. metempsychosis ), a stage name she received from James Incandenza when she starred in his films (and later her on-air name in her radio show \"60+/−\"). She became acquainted with James through her college relationship with Orin Incandenza, who referred to her as \"The Prettiest Girl of All Time\", or P. G. O. A. T. She appears in the lethally addictive Entertainment, reaching down toward a wobbly \"neonatal\" lens as if it were in a bassinet and apologizing profusely, her face blurred bey\n\n[NEW 8] ng and murder 3 0 Philadelphia, Mississippi The murders of Chaney, Goodman, and Schwerner , three civil rights workers, in Philadelphia, Mississippi by the Ku Klux Klan. March 25, 1965 Shooting 1 0 Selma, Alabama The Ku Klux Klan murdered Viola Liuzzo , a Southern-raised white mother of five who was visiting Alabama from her home in Detroit to attend a civil rights march. At the time of her murder, Liuzzo was transporting civil rights marchers. January 10, 1966 Firebombing 1 0 Hattiesburg, Mississippi Vernon Dahmer died in the firebombing of his home in Mississippi at the hands of the Ku Klux Klan . November 3, 1979 Shooting 5 5 Greensboro, North Carolina Greensboro massacre : Members of the Ku Klux Klan and the American Nazi Party jumped out of vehicles and\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 8 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 1, 0, 0, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1951, + "osl": 1153, + "total_tokens": 3104, + "latency_ms": 3895.89, + "tokens_per_second": 295.95 + }, + "context": { + "num_documents_evaluated": 8, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "924838ec-75fc-4489-8d96-8b1e346eaf30", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:54:46.470283Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: Małgorzata Rożniecka is a model who won the title of Miss International. What is the difference in title years from when she won and the pageant winner who was murdered by her stalker?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Polish model and beauty queen (born 1978) Małgorzata Rożniecka (born 1978, in Szczecin ) is a Polish model and beauty queen who won Miss International 2001 . Her 2001 victory broke the four-year monopoly of Latin American countries of the crown. She is 1.78 m tall. References ^ Retrieved on 11 Apr 2008. External links Małgorzata Rożniecka profile Awards and achievements Preceded by Vivian Urdaneta Miss International 2001 Succeeded by Christina Sawaya This Polish biographical article is a stub . You can help Wikipedia by adding missing information . v t e This biography article about a European contestant in a beauty pageant is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 2] Beauty pageant competition, Organization Miss International Type International women's beauty pageant Headquarters Tokyo, Japan First edition 1960 Most recent edition 2025 Current titleholder Catalina Duque Colombia President Akemi Shimomura Language English Website www . miss-international . org Miss International ( Miss International Beauty or The International Beauty Pageant ) is a Japan-based international major beauty pageant organized by the International Culture Association. First held in 1960, it is the fourth largest pageant in the world in terms of the number of national winners participating in the international contest. Along with Miss World , Miss Universe , and Miss Earth , the pageant is one of the Big Four beauty pageants . The Miss Internati\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents provide the year Małgorzata Rożniecka won Miss International (2001) but contain no information about a pageant winner who was murdered by her stalker, including the year that winner held her title. Without the second winner's title year, the difference in years cannot be calculated.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 581, + "osl": 79, + "total_tokens": 660, + "latency_ms": 10253.01, + "tokens_per_second": 7.71 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "5b2c0ec0-f6df-447a-b30f-17aebe020470", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:54:51.774941Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: Małgorzata Rożniecka is a model who won the title of Miss International. What is the difference in title years from when she won and the pageant winner who was murdered by her stalker?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Polish model and beauty queen (born 1978) Małgorzata Rożniecka (born 1978, in Szczecin ) is a Polish model and beauty queen who won Miss International 2001 . Her 2001 victory broke the four-year monopoly of Latin American countries of the crown. She is 1.78 m tall. References ^ Retrieved on 11 Apr 2008. External links Małgorzata Rożniecka profile Awards and achievements Preceded by Vivian Urdaneta Miss International 2001 Succeeded by Christina Sawaya This Polish biographical article is a stub . You can help Wikipedia by adding missing information . v t e This biography article about a European contestant in a beauty pageant is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 2] Beauty pageant competition, Organization Miss International Type International women's beauty pageant Headquarters Tokyo, Japan First edition 1960 Most recent edition 2025 Current titleholder Catalina Duque Colombia President Akemi Shimomura Language English Website www . miss-international . org Miss International ( Miss International Beauty or The International Beauty Pageant ) is a Japan-based international major beauty pageant organized by the International Culture Association. First held in 1960, it is the fourth largest pageant in the world in terms of the number of national winners participating in the international contest. Along with Miss World , Miss Universe , and Miss Earth , the pageant is one of the Big Four beauty pageants . The Miss Internati\n\n\nSEARCH HISTORY: - Query: 'Małgorzata Rożniecka Miss International year' → 3 docs\n- Query: 'beauty queen murdered by stalker pageant winner' → 5 docs\n- Query: 'Miss International winner murdered stalker year' → 0 docs\n\nFEEDBACK: First query aims to locate the year Małgorzata Rożniecka won Miss International. The second and third queries target the identity and title year of the pageant winner who was later murdered by her stalker, enabling calculation of the difference in title years.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"beauty queen murdered stalker\",\n \"pageant winner killed by stalker\",\n \"beauty pageant titleholder murder stalker\"\n ],\n \"feedback\": \"First query seeks the specific case of a pageant winner who was murdered by her stalker, without limiting to a particular pageant. Second and third queries broaden the search to capture any pageant titleholder involved in a stalker‑related murder, allowing us to identify the victim and her title year.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 843, + "osl": 106, + "total_tokens": 949, + "latency_ms": 5303.97, + "tokens_per_second": 19.99 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "381afbde-cc78-4dfd-b2f3-c45c5c2a04ad", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T02:54:56.675943Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: Małgorzata Rożniecka is a model who won the title of Miss International. What is the difference in title years from when she won and the pageant winner who was murdered by her stalker?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] e her relationship with her boyfriend, Billy Loomis . As her father Neil is traveling for work, Sidney waits at home alone for her best friend, Tatum Riley . She receives a call taunting her about Maureen's death and is attacked by Ghostface, who disappears just before Billy climbs through her window. Sidney immediately grows suspicious when he drops a cell phone, and Billy is arrested by Deputy Sheriff Dewey , Tatum's brother. Outside the police station, Sidney gets into a physical altercation with investigative journalist Gale Weathers . Gale had written a book claiming that Maureen had multiple extramarital affairs, including one with Cotton, who Gale believes was falsely accused of Maureen's homicide. Sidney stays at Tatum's home but receives another tau\n\n[NEW 2] 's stepfather Dan Guynes married and divorced Virginia twice. On October 20, 1980, a year after their second divorce from each other, Guynes died by suicide. Her biological father Harmon died in 1997 from liver cancer in Brazoria, Texas . Moore's mother had a long arrest record which included drunk driving and arson. Moore broke off contact with her mother in 1989, when she left halfway through a rehab stay Moore had financed at the Hazelden Foundation in Minnesota. Virginia Guynes posed nude for the magazine High Society in 1993, where she spoofed Moore's Vanity Fair pregnancy and bodypaint covers and parodied her clay scene from Ghost . Moore and Guynes reconciled shortly before Guynes died of a brain tumor on July 2, 1998. Moore spent her early childhood\n\n[NEW 3] r the title Evil Angels . References ^ Hollinger 2006 , pp. 94–95. ^ ^ a b ^ ^ ^ ^ ^ ^ ^ Probst 2012 , p. 7. ^ a b c d ^ a b c d ^ ^ ^ ^ a b c d Longworth 2013 , p. 7. ^ ^ ^ a b c d Longworth 2013 , p. 8. ^ ^ ^ Gussow 1998 , p. 265. ^ ^ ^ a b c d e Longworth 2013 , p. 10. ^ Pfaff & Emerson 1987 , p. 16. \"Her second year, the rage was \"emotional recall\" by a teacher who \"delved into personal lives in a way that I found obnoxious.\" ^ ^ a b ^ - - ^ ^ ^ a b Fisher 2011 , p. 772. ^ Longworth 2013 , p. 21. ^ ^ Longworth 2013 , pp. 19–21. ^ ^ a b Hollinger 2006 , p. 81. ^ Longworth 2013 , p. 32. ^ ^ citing ^ Longworth 2013 , p. 26. ^ ^ a b Longworth 2013 , p. 44. ^ ^ ^ [ dead link ] ^ Hollinger 2006 , p. 71. ^ ^ a b c Longworth 2013 , p. 41. ^ Hollinger 2006 , p. 7\n\n[NEW 4] 25 Newberg, Oregon College Admissions Runner-Up 2 Gia Allemand 26 Manorville, New York Swimsuit Model Week 7 3 Ali Fedotowsky 25 Williamstown, Massachusetts Advertising Account Manager Week 6 4 (quit) Corrie Adamson 23 Kissimmee, Florida Wardrobe Consultant Week 5 5 Ashleigh Hunt 25 North Potomac, Maryland Account Manager Week 4 6–7 Jessica \"Jessie\" Sulidis 25 Oakville, Ontario Cosmetic Sales Manager Kathryn Sherlock 25 Lexington, Kentucky Corporate Flight Attendant 8 Ella Nolan 30 La Follette, Tennessee Hair Stylist 9 Elizabeth Kitt (NE) 29 Imperial, Nebraska Nanny Week 3 10–11 Valishia Savage 32 San Bernardino, California Homemaker Michelle Kujawa 26 Anaheim, California Office Manager 12 Ashley Elmore 29 Pittsburgh, Pennsylvania Teacher Week 2 13–14 Chris\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] Polish model and beauty queen (born 1978) Małgorzata Rożniecka (born 1978, in Szczecin ) is a Polish model and beauty queen who won Miss International 2001 . Her 2001 victory broke the four-year monopoly of Latin American countries of the crown. She is 1.78 m tall. References ^ Retrieved on 11 Apr 2...\n[KEPT 2] Beauty pageant competition, Organization Miss International Type International women's beauty pageant Headquarters Tokyo, Japan First edition 1960 Most recent edition 2025 Current titleholder Catalina Duque Colombia President Akemi Shimomura Language English Website www . miss-international . org Mi...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 4 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1298, + "osl": 1234, + "total_tokens": 2532, + "latency_ms": 4233.11, + "tokens_per_second": 291.51 + }, + "context": { + "num_documents_evaluated": 4, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "1be4c643-c3a4-48ad-988b-1c509e53753b", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T02:55:01.282222Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: Małgorzata Rożniecka is a model who won the title of Miss International. What is the difference in title years from when she won and the pageant winner who was murdered by her stalker?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Polish model and beauty queen (born 1978) Małgorzata Rożniecka (born 1978, in Szczecin ) is a Polish model and beauty queen who won Miss International 2001 . Her 2001 victory broke the four-year monopoly of Latin American countries of the crown. She is 1.78 m tall. References ^ Retrieved on 11 Apr 2008. External links Małgorzata Rożniecka profile Awards and achievements Preceded by Vivian Urdaneta Miss International 2001 Succeeded by Christina Sawaya This Polish biographical article is a stub . You can help Wikipedia by adding missing information . v t e This biography article about a European contestant in a beauty pageant is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 2] Beauty pageant competition, Organization Miss International Type International women's beauty pageant Headquarters Tokyo, Japan First edition 1960 Most recent edition 2025 Current titleholder Catalina Duque Colombia President Akemi Shimomura Language English Website www . miss-international . org Miss International ( Miss International Beauty or The International Beauty Pageant ) is a Japan-based international major beauty pageant organized by the International Culture Association. First held in 1960, it is the fourth largest pageant in the world in terms of the number of national winners participating in the international contest. Along with Miss World , Miss Universe , and Miss Earth , the pageant is one of the Big Four beauty pageants . The Miss Internati\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents provide Małgorzata Rożniecka's title year (2001) but contain no information about any pageant winner who was murdered by her stalker, nor the year that winner held the title. Therefore the required comparison cannot be performed.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 581, + "osl": 69, + "total_tokens": 650, + "latency_ms": 4605.54, + "tokens_per_second": 14.98 + }, + "context": { + "kept_docs_count": 2, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "381e5754-cd65-4804-8a11-b623721263be", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T02:55:11.921707Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: Małgorzata Rożniecka is a model who won the title of Miss International. What is the difference in title years from when she won and the pageant winner who was murdered by her stalker?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Polish model and beauty queen (born 1978) Małgorzata Rożniecka (born 1978, in Szczecin ) is a Polish model and beauty queen who won Miss International 2001 . Her 2001 victory broke the four-year monopoly of Latin American countries of the crown. She is 1.78 m tall. References ^ Retrieved on 11 Apr 2008. External links Małgorzata Rożniecka profile Awards and achievements Preceded by Vivian Urdaneta Miss International 2001 Succeeded by Christina Sawaya This Polish biographical article is a stub . You can help Wikipedia by adding missing information . v t e This biography article about a European contestant in a beauty pageant is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 2] Beauty pageant competition, Organization Miss International Type International women's beauty pageant Headquarters Tokyo, Japan First edition 1960 Most recent edition 2025 Current titleholder Catalina Duque Colombia President Akemi Shimomura Language English Website www . miss-international . org Miss International ( Miss International Beauty or The International Beauty Pageant ) is a Japan-based international major beauty pageant organized by the International Culture Association. First held in 1960, it is the fourth largest pageant in the world in terms of the number of national winners participating in the international contest. Along with Miss World , Miss Universe , and Miss Earth , the pageant is one of the Big Four beauty pageants . The Miss Internati\n\n\nSEARCH HISTORY: - Query: 'Małgorzata Rożniecka Miss International year' → 3 docs\n- Query: 'beauty queen murdered by stalker pageant winner' → 5 docs\n- Query: 'Miss International winner murdered stalker year' → 0 docs\n- Query: 'beauty queen murdered stalker' → 1 docs\n- Query: 'pageant winner killed by stalker' → 1 docs\n- Query: 'beauty pageant titleholder murder stalker' → 2 docs\n\nFEEDBACK: First query aims to locate the year Małgorzata Rożniecka won Miss International. The second and third queries target the identity and title year of the pageant winner who was later murdered by her stalker, enabling calculation of the difference in title years.\nFirst query seeks the specific case of a pageant winner who was murdered by her stalker, without limiting to a particular pageant. Second and third queries broaden the search to capture any pageant titleholder involved in a stalker‑related murder, allowing us to identify the victim and her title year.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"beauty pageant murders\",\n \"Miss Universe stalker murder\",\n \"Miss World stalker murder\"\n ],\n \"feedback\": \"Need to identify the pageant titleholder who was murdered by her stalker and the year she held the title, so we broaden to list articles and focus on the major pageants where such an incident is most likely documented.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1014, + "osl": 725, + "total_tokens": 1739, + "latency_ms": 10638.89, + "tokens_per_second": 68.15 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "f7786764-095f-4476-953b-66a780f74d57", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T02:55:32.648084Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: Małgorzata Rożniecka is a model who won the title of Miss International. What is the difference in title years from when she won and the pageant winner who was murdered by her stalker?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] dman's friend and fellow Mezzaluna waiter List of unsolved murders (1980–1999) Trial of Yolanda Saldívar – the \"Hispanic O. J. Simpson trial\" Robert Blake and Bonny Lee Bakley 1995 Okinawa rape incident High profile trial from the same time Lizzie Borden References ^ a b ^ a b c d e f g ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c d e ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c ^ ^ ^ ^ ^ ^ ^ ^ [ full citation needed ] ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c ^ ^ Schuetz & Lilley 1999 , pp. 22–23. ^ ^ ^ a b ^ FRONTLINE: the o. j. verdict: interviews: Peter arenella Archived September 8, 2017, at the Wayback Machine . PBS. Retrieved December 30, 2010. ^ a b Dershowitz 2004 . ^ ^ ^ a b ^ ^ ^ O. J.'s Darkened Mug Shot Archived January 5, 2010, at the Wayback Machine . Museumofhoaxes. com. Retrieved December 30, 2\n\n[NEW 2] scars appears in an alley the same way and evades police. The first man robs a gun shop, looks up addresses of women named Sarah Connor, and begins murdering them. A waitress named Sarah Connor sees news reports about the murders and notices the second man following her. As she shelters in a nightclub , the first man kills her roommate Ginger and Ginger's boyfriend. He tracks her to the nightclub, where the second man preemptively fires on him. After a chaotic gunfight, the second man escapes with Sarah in a stolen car, while the first man gets up and pursues them on foot and in a police car . The second man identifies himself as Sgt. Reese . He tells Sarah that the other man is a Terminator , a cyborg assassin from the future, and he has also come from the\n\n[NEW 3] graph the body as evidence, but, shortly after he arrives, her body is gone. Wanting to see the latest Videodrome broadcast, Max meets Harlan at his studio. Harlan reveals that he has been working with Convex to recruit Max to their cause. They aim to end North America's cultural decay by using Videodrome to kill anyone too obsessed with sex and violence. Convex then inserts a brainwashing Betamax tape into Max's torso. Under Convex's influence, Max kills his partners at CIVIC-TV. He attempts to kill Bianca, who stops him by showing him a videotape of Nicki's murder on the Videodrome set. Bianca then \"reprograms\" Max to her father's cause: \"Death to Videodrome . Long live the new flesh.\" Under her orders, he kills Harlan, who inserts his hand into Max's tors\n\n[NEW 4] workshop in Southport , United Kingdom On July 18, 2024, a suspected stalker of Swift, who was accused of threatening her and her boyfriend, American football player Travis Kelce , on social media was detained and arrested as he attempted to enter the second of Swift's shows in Gelsenkirchen . The same month, three children were killed at a mass stabbing attack at a Swiftie workshop in Southport, Merseyside , UK. The incident caused public unrest in Southport, and over the next few days, escalated into nationwide protests and riots . A week later, authorities thwarted an Islamic State (ISIS) plot to attack Swift's three shows in Vienna . Police arrested three teenagers who were \"radicalized on the Internet\" and plotted to kill \"tens of thousands\" at the con\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] Polish model and beauty queen (born 1978) Małgorzata Rożniecka (born 1978, in Szczecin ) is a Polish model and beauty queen who won Miss International 2001 . Her 2001 victory broke the four-year monopoly of Latin American countries of the crown. She is 1.78 m tall. References ^ Retrieved on 11 Apr 2...\n[KEPT 2] Beauty pageant competition, Organization Miss International Type International women's beauty pageant Headquarters Tokyo, Japan First edition 1960 Most recent edition 2025 Current titleholder Catalina Duque Colombia President Akemi Shimomura Language English Website www . miss-international . org Mi...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 4 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[0,0,0,0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1224, + "osl": 1762, + "total_tokens": 2986, + "latency_ms": 19989.4, + "tokens_per_second": 88.15 + }, + "context": { + "num_documents_evaluated": 4, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "25335b69-3331-4fcf-85ed-1d6f0112a6c7", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T02:55:38.666990Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: Małgorzata Rożniecka is a model who won the title of Miss International. What is the difference in title years from when she won and the pageant winner who was murdered by her stalker?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Polish model and beauty queen (born 1978) Małgorzata Rożniecka (born 1978, in Szczecin ) is a Polish model and beauty queen who won Miss International 2001 . Her 2001 victory broke the four-year monopoly of Latin American countries of the crown. She is 1.78 m tall. References ^ Retrieved on 11 Apr 2008. External links Małgorzata Rożniecka profile Awards and achievements Preceded by Vivian Urdaneta Miss International 2001 Succeeded by Christina Sawaya This Polish biographical article is a stub . You can help Wikipedia by adding missing information . v t e This biography article about a European contestant in a beauty pageant is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 2] Beauty pageant competition, Organization Miss International Type International women's beauty pageant Headquarters Tokyo, Japan First edition 1960 Most recent edition 2025 Current titleholder Catalina Duque Colombia President Akemi Shimomura Language English Website www . miss-international . org Miss International ( Miss International Beauty or The International Beauty Pageant ) is a Japan-based international major beauty pageant organized by the International Culture Association. First held in 1960, it is the fourth largest pageant in the world in terms of the number of national winners participating in the international contest. Along with Miss World , Miss Universe , and Miss Earth , the pageant is one of the Big Four beauty pageants . The Miss Internati\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents provide Małgorzata Rożniecka's title year (2001) but contain no information about a pageant winner who was murdered by her stalker, so the required comparison year is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 639, + "osl": 440, + "total_tokens": 1079, + "latency_ms": 6018.11, + "tokens_per_second": 73.11 + }, + "context": { + "kept_docs_count": 2, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "aaaa5cd0-c588-4430-a4f1-5eebf2165041", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T02:55:42.252081Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: Małgorzata Rożniecka is a model who won the title of Miss International. What is the difference in title years from when she won and the pageant winner who was murdered by her stalker?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Polish model and beauty queen (born 1978) Małgorzata Rożniecka (born 1978, in Szczecin ) is a Polish model and beauty queen who won Miss International 2001 . Her 2001 victory broke the four-year monopoly of Latin American countries of the crown. She is 1.78 m tall. References ^ Retrieved on 11 Apr 2008. External links Małgorzata Rożniecka profile Awards and achievements Preceded by Vivian Urdaneta Miss International 2001 Succeeded by Christina Sawaya This Polish biographical article is a stub . You can help Wikipedia by adding missing information . v t e This biography article about a European contestant in a beauty pageant is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 2] Beauty pageant competition, Organization Miss International Type International women's beauty pageant Headquarters Tokyo, Japan First edition 1960 Most recent edition 2025 Current titleholder Catalina Duque Colombia President Akemi Shimomura Language English Website www . miss-international . org Miss International ( Miss International Beauty or The International Beauty Pageant ) is a Japan-based international major beauty pageant organized by the International Culture Association. First held in 1960, it is the fourth largest pageant in the world in terms of the number of national winners participating in the international contest. Along with Miss World , Miss Universe , and Miss Earth , the pageant is one of the Big Four beauty pageants . The Miss Internati\n\n\nSEARCH HISTORY: - Query: 'Małgorzata Rożniecka Miss International year' → 3 docs\n- Query: 'beauty queen murdered by stalker pageant winner' → 5 docs\n- Query: 'Miss International winner murdered stalker year' → 0 docs\n- Query: 'beauty queen murdered stalker' → 1 docs\n- Query: 'pageant winner killed by stalker' → 1 docs\n- Query: 'beauty pageant titleholder murder stalker' → 2 docs\n- Query: 'beauty pageant murders' → 1 docs\n- Query: 'Miss Universe stalker murder' → 1 docs\n- Query: 'Miss World stalker murder' → 2 docs\n\nFEEDBACK: First query aims to locate the year Małgorzata Rożniecka won Miss International. The second and third queries target the identity and title year of the pageant winner who was later murdered by her stalker, enabling calculation of the difference in title years.\nFirst query seeks the specific case of a pageant winner who was murdered by her stalker, without limiting to a particular pageant. Second and third queries broaden the search to capture any pageant titleholder involved in a stalker‑related murder, allowing us to identify the victim and her title year.\nNeed to identify the pageant titleholder who was murdered by her stalker and the year she held the title, so we broaden to list articles and focus on the major pageants where such an incident is most likely documented.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"beauty queen stalker murder\",\n \"pageant winner stalker murder\",\n \"Miss USA stalker murder\"\n ],\n \"feedback\": \"The first query broadens the search to any beauty queen killed by a stalker. The second focuses on pageant titleholders in general, using a concise pattern not previously tried. The third narrows to the Miss USA pageant, a likely source of widely reported stalker-related crimes.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1101, + "osl": 991, + "total_tokens": 2092, + "latency_ms": 3584.49, + "tokens_per_second": 276.47 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "15e0fa3b-e09e-4a1c-847a-9257237ff23e", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T02:55:57.341110Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: Małgorzata Rożniecka is a model who won the title of Miss International. What is the difference in title years from when she won and the pageant winner who was murdered by her stalker?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] covered harassing a female cyclist in the vicinity, but charges against him were dropped and the case remained unsolved until 2024. In May 2019, Oklahoman Ronald S. Sanchez Jr., 43 years old, was murdered at a campsite near Mount Rogers National Recreation Area in western Virginia Jefferson National Forest. A female hiker who has not been identified was also injured by an assailant there. James L. Jordan, 30, of West Yarmouth, Massachusetts was found not guilty of the crimes by reason of insanity. Human error can lead to casualties as well. In July 2013, 66-year-old lone hiker Geraldine Largay disappeared on the trail in Maine. Largay became lost and survived 26 days before dying. Her remains were found two years later in October 2015. In October 2015, a hik\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] Polish model and beauty queen (born 1978) Małgorzata Rożniecka (born 1978, in Szczecin ) is a Polish model and beauty queen who won Miss International 2001 . Her 2001 victory broke the four-year monopoly of Latin American countries of the crown. She is 1.78 m tall. References ^ Retrieved on 11 Apr 2...\n[KEPT 2] Beauty pageant competition, Organization Miss International Type International women's beauty pageant Headquarters Tokyo, Japan First edition 1960 Most recent edition 2025 Current titleholder Catalina Duque Colombia President Akemi Shimomura Language English Website www . miss-international . org Mi...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 619, + "osl": 1470, + "total_tokens": 2089, + "latency_ms": 14395.1, + "tokens_per_second": 102.12 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "05698689-1a49-47a5-83a7-0d3f7be95fb5", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T02:56:03.791346Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: Małgorzata Rożniecka is a model who won the title of Miss International. What is the difference in title years from when she won and the pageant winner who was murdered by her stalker?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Polish model and beauty queen (born 1978) Małgorzata Rożniecka (born 1978, in Szczecin ) is a Polish model and beauty queen who won Miss International 2001 . Her 2001 victory broke the four-year monopoly of Latin American countries of the crown. She is 1.78 m tall. References ^ Retrieved on 11 Apr 2008. External links Małgorzata Rożniecka profile Awards and achievements Preceded by Vivian Urdaneta Miss International 2001 Succeeded by Christina Sawaya This Polish biographical article is a stub . You can help Wikipedia by adding missing information . v t e This biography article about a European contestant in a beauty pageant is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 2] Beauty pageant competition, Organization Miss International Type International women's beauty pageant Headquarters Tokyo, Japan First edition 1960 Most recent edition 2025 Current titleholder Catalina Duque Colombia President Akemi Shimomura Language English Website www . miss-international . org Miss International ( Miss International Beauty or The International Beauty Pageant ) is a Japan-based international major beauty pageant organized by the International Culture Association. First held in 1960, it is the fourth largest pageant in the world in terms of the number of national winners participating in the international contest. Along with Miss World , Miss Universe , and Miss Earth , the pageant is one of the Big Four beauty pageants . The Miss Internati\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Document 1 provides Małgorzata Rożniecka's win year (2001). However, none of the kept documents mention the pageant winner who was murdered by her stalker or the year that winner held the title. Without that second year, the difference in title years cannot be calculated.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 624, + "osl": 395, + "total_tokens": 1019, + "latency_ms": 6449.29, + "tokens_per_second": 61.25 + }, + "context": { + "kept_docs_count": 2, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "64a54c37-6b6c-4ae3-804f-00a955dd39ef", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T02:56:04.383326Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: Małgorzata Rożniecka is a model who won the title of Miss International. What is the difference in title years from when she won and the pageant winner who was murdered by her stalker?\n\nDOCUMENTS:\n\n[DOC 1] Polish model and beauty queen (born 1978) Małgorzata Rożniecka (born 1978, in Szczecin ) is a Polish model and beauty queen who won Miss International 2001 . Her 2001 victory broke the four-year monopoly of Latin American countries of the crown. She is 1.78 m tall. References ^ Retrieved on 11 Apr 2008. External links Małgorzata Rożniecka profile Awards and achievements Preceded by Vivian Urdaneta Miss International 2001 Succeeded by Christina Sawaya This Polish biographical article is a stub . You can help Wikipedia by adding missing information . v t e This biography article about a European contestant in a beauty pageant is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 2] Beauty pageant competition, Organization Miss International Type International women's beauty pageant Headquarters Tokyo, Japan First edition 1960 Most recent edition 2025 Current titleholder Catalina Duque Colombia President Akemi Shimomura Language English Website www . miss-international . org Miss International ( Miss International Beauty or The International Beauty Pageant ) is a Japan-based international major beauty pageant organized by the International Culture Association. First held in 1960, it is the fourth largest pageant in the world in terms of the number of national winners participating in the international contest. Along with Miss World , Miss Universe , and Miss Earth , the pageant is one of the Big Four beauty pageants . The Miss Internati\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 495, + "osl": 126, + "total_tokens": 621, + "latency_ms": 591.25, + "tokens_per_second": 213.11 + }, + "context": { + "num_documents_used": 2, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:56:04.384128Z", + "summary": { + "total_llm_calls": 13, + "total_hops": 5, + "total_input_tokens": 11436, + "total_output_tokens": 9600, + "total_tokens": 21036, + "total_latency_ms": 113349.08, + "average_tokens_per_second": 119.8, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Ma%C5%82gorzata_Ro%C5%BCniecka", + "https://en.wikipedia.org/wiki/Miss_International", + "https://en.wikipedia.org/wiki/Agnieszka_Kotlarska" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "10 years" + } + }, + { + "query_id": "22", + "original_query": "According to the 1990 United States census, what was the total population of the cities in Oklahoma that had at least 100,000 residents according to the 2020 United States census?", + "timestamp_start": "2026-05-16T02:56:04.403129Z", + "llm_calls": [ + { + "call_id": "5a4cb098-0514-4e36-aab8-b43d9e60c808", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:56:07.584751Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: According to the 1990 United States census, what was the total population of the cities in Oklahoma that had at least 100,000 residents according to the 2020 United States census?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Oklahoma cities 2020 census 100000\",\n \"Oklahoma City 1990 census population\",\n \"Tulsa 1990 census population\"\n ],\n \"feedback\": \"Need to identify Oklahoma cities with ≥100,000 residents in 2020 (likely Oklahoma City and Tulsa) and retrieve their 1990 census populations to sum them.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 414, + "osl": 85, + "total_tokens": 499, + "latency_ms": 3181.47, + "tokens_per_second": 26.72 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "b5d1a48f-3830-4958-806d-bb7931e4617b", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:56:11.409256Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: According to the 1990 United States census, what was the total population of the cities in Oklahoma that had at least 100,000 residents according to the 2020 United States census?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] rated July 15, 1890 Government • Type Council–manager • Body Oklahoma City Council • Mayor David Holt ( R ) • City manager Craig Freeman Area • City 620.79 sq mi (1,607.83 km 2 ) • Land 606.48 sq mi (1,570.77 km 2 ) • Water 14.31 sq mi (37.06 km 2 ) • Urban 421.7 sq mi (1,092.3 km 2 ) Elevation 1,198 ft (365 m) Population ( 2020 ) • City 681,054 • Estimate (2024) 712,919 • Rank 62nd in North America 20th in the United States 1st in Oklahoma • Density 1,123.0/sq mi (433.58/km 2 ) • Urban 982,276 ( US: 46th ) • Urban density 2,329/sq mi (899.3/km 2 ) • Metro 1,512,242 ( US: 42nd ) Demonyms Oklahoma Cityan Oklahoma Citian GDP • Metro $100.054 billion (2023) Time zone UTC−6 ( Central (CST) ) • Summer ( DST ) UTC−5 ( CDT ) ZIP Codes 73101–73132, 73134–73137, 7313\n\n[NEW 2] storical population Census Pop. Note %± 1900 1,390 — 1910 18,182 1,208.1% 1920 72,075 296.4% 1930 141,258 96.0% 1940 142,157 0.6% 1950 182,740 28.5% 1960 261,685 43.2% 1970 331,638 26.7% 1980 360,919 8.8% 1990 367,302 1.8% 2000 393,049 7.0% 2010 391,906 −0.3% 2020 413,066 5.4% 2024 (est.) 415,154 0.5% U. S. Decennial Census Map of racial distribution in Tulsa, 2010 U. S. Census. Each dot is 25 people: ⬤ White ⬤ Black ⬤ Asian ⬤ Hispanic ⬤ Other Racial and ethnic composition Tulsa, Oklahoma – Racial and ethnic composition Note: the US Census treats Hispanic/Latino as an ethnic category. This table excludes Latinos from the racial categories and assigns them to a separate category. Hispanics/Latinos may be of any race. Race / Ethnicity ( NH = Non-Hispanic ) Pop\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 2 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 865, + "osl": 411, + "total_tokens": 1276, + "latency_ms": 3175.94, + "tokens_per_second": 129.41 + }, + "context": { + "num_documents_evaluated": 2, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "4b146df2-077e-4693-aa06-cd09c323fba6", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:56:15.687962Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: According to the 1990 United States census, what was the total population of the cities in Oklahoma that had at least 100,000 residents according to the 2020 United States census?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] rated July 15, 1890 Government • Type Council–manager • Body Oklahoma City Council • Mayor David Holt ( R ) • City manager Craig Freeman Area • City 620.79 sq mi (1,607.83 km 2 ) • Land 606.48 sq mi (1,570.77 km 2 ) • Water 14.31 sq mi (37.06 km 2 ) • Urban 421.7 sq mi (1,092.3 km 2 ) Elevation 1,198 ft (365 m) Population ( 2020 ) • City 681,054 • Estimate (2024) 712,919 • Rank 62nd in North America 20th in the United States 1st in Oklahoma • Density 1,123.0/sq mi (433.58/km 2 ) • Urban 982,276 ( US: 46th ) • Urban density 2,329/sq mi (899.3/km 2 ) • Metro 1,512,242 ( US: 42nd ) Demonyms Oklahoma Cityan Oklahoma Citian GDP • Metro $100.054 billion (2023) Time zone UTC−6 ( Central (CST) ) • Summer ( DST ) UTC−5 ( CDT ) ZIP Codes 73101–73132, 73134–73137, 7313\n\n[DOC 2] storical population Census Pop. Note %± 1900 1,390 — 1910 18,182 1,208.1% 1920 72,075 296.4% 1930 141,258 96.0% 1940 142,157 0.6% 1950 182,740 28.5% 1960 261,685 43.2% 1970 331,638 26.7% 1980 360,919 8.8% 1990 367,302 1.8% 2000 393,049 7.0% 2010 391,906 −0.3% 2020 413,066 5.4% 2024 (est.) 415,154 0.5% U. S. Decennial Census Map of racial distribution in Tulsa, 2010 U. S. Census. Each dot is 25 people: ⬤ White ⬤ Black ⬤ Asian ⬤ Hispanic ⬤ Other Racial and ethnic composition Tulsa, Oklahoma – Racial and ethnic composition Note: the US Census treats Hispanic/Latino as an ethnic category. This table excludes Latinos from the racial categories and assigns them to a separate category. Hispanics/Latinos may be of any race. Race / Ethnicity ( NH = Non-Hispanic ) Pop\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"We have the 2020 populations for Oklahoma City (681,054) and Tulsa (413,066) from the kept documents, satisfying the condition of cities with at least 100,000 residents. We also have Tulsa's 1990 population (367,302). However, the 1990 population for Oklahoma City is not present in any of the kept documents, so we cannot compute the total 1990 population for the qualifying cities.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 844, + "osl": 105, + "total_tokens": 949, + "latency_ms": 4277.92, + "tokens_per_second": 24.54 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "b4d153b7-410b-4cb2-83e1-b9a8a21e621a", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:56:22.867437Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: According to the 1990 United States census, what was the total population of the cities in Oklahoma that had at least 100,000 residents according to the 2020 United States census?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] rated July 15, 1890 Government • Type Council–manager • Body Oklahoma City Council • Mayor David Holt ( R ) • City manager Craig Freeman Area • City 620.79 sq mi (1,607.83 km 2 ) • Land 606.48 sq mi (1,570.77 km 2 ) • Water 14.31 sq mi (37.06 km 2 ) • Urban 421.7 sq mi (1,092.3 km 2 ) Elevation 1,198 ft (365 m) Population ( 2020 ) • City 681,054 • Estimate (2024) 712,919 • Rank 62nd in North America 20th in the United States 1st in Oklahoma • Density 1,123.0/sq mi (433.58/km 2 ) • Urban 982,276 ( US: 46th ) • Urban density 2,329/sq mi (899.3/km 2 ) • Metro 1,512,242 ( US: 42nd ) Demonyms Oklahoma Cityan Oklahoma Citian GDP • Metro $100.054 billion (2023) Time zone UTC−6 ( Central (CST) ) • Summer ( DST ) UTC−5 ( CDT ) ZIP Codes 73101–73132, 73134–73137, 7313\n\n[DOC 2] storical population Census Pop. Note %± 1900 1,390 — 1910 18,182 1,208.1% 1920 72,075 296.4% 1930 141,258 96.0% 1940 142,157 0.6% 1950 182,740 28.5% 1960 261,685 43.2% 1970 331,638 26.7% 1980 360,919 8.8% 1990 367,302 1.8% 2000 393,049 7.0% 2010 391,906 −0.3% 2020 413,066 5.4% 2024 (est.) 415,154 0.5% U. S. Decennial Census Map of racial distribution in Tulsa, 2010 U. S. Census. Each dot is 25 people: ⬤ White ⬤ Black ⬤ Asian ⬤ Hispanic ⬤ Other Racial and ethnic composition Tulsa, Oklahoma – Racial and ethnic composition Note: the US Census treats Hispanic/Latino as an ethnic category. This table excludes Latinos from the racial categories and assigns them to a separate category. Hispanics/Latinos may be of any race. Race / Ethnicity ( NH = Non-Hispanic ) Pop\n\n\nSEARCH HISTORY: - Query: 'Oklahoma cities 2020 census 100000' → 2 docs\n- Query: 'Oklahoma City 1990 census population' → 0 docs\n- Query: 'Tulsa 1990 census population' → 0 docs\n\nFEEDBACK: Need to identify Oklahoma cities with ≥100,000 residents in 2020 (likely Oklahoma City and Tulsa) and retrieve their 1990 census populations to sum them.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Oklahoma cities 2020 census 100000\",\n \"Oklahoma City population 1990 census\",\n \"Norman Oklahoma population 2020 census\"\n ],\n \"feedback\": \"Need to identify all Oklahoma cities with ≥100,000 residents in the 2020 Census (likely Oklahoma City, Tulsa, Norman) and obtain each of their 1990 Census populations to sum them.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1143, + "osl": 513, + "total_tokens": 1656, + "latency_ms": 7178.95, + "tokens_per_second": 71.46 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "9933c55f-effb-4bf3-856f-7c94d41e627d", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T02:56:27.767016Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: According to the 1990 United States census, what was the total population of the cities in Oklahoma that had at least 100,000 residents according to the 2020 United States census?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] rated July 15, 1890 Government • Type Council–manager • Body Oklahoma City Council • Mayor David Holt ( R ) • City manager Craig Freeman Area • City 620.79 sq mi (1,607.83 km 2 ) • Land 606.48 sq mi (1,570.77 km 2 ) • Water 14.31 sq mi (37.06 km 2 ) • Urban 421.7 sq mi (1,092.3 km 2 ) Elevation 1,198 ft (365 m) Population ( 2020 ) • City 681,054 • Estimate (2024) 712,919 • Rank 62nd in North America 20th in the United States 1st in Oklahoma • Density 1,123.0/sq mi (433.58/km 2 ) • Urban 982,276 ( US: 46th ) • Urban density 2,329/sq mi (899.3/km 2 ) • Metro 1,512,242 ( US: 42nd ) Demonyms Oklahoma Cityan Oklahoma Citian GDP • Metro $100.054 billion (2023) Time zone UTC−6 ( Central (CST) ) • Summer ( DST ) UTC−5 ( CDT ) ZIP Codes 73101–73132, 73134–73137, 7313\n\n[DOC 2] storical population Census Pop. Note %± 1900 1,390 — 1910 18,182 1,208.1% 1920 72,075 296.4% 1930 141,258 96.0% 1940 142,157 0.6% 1950 182,740 28.5% 1960 261,685 43.2% 1970 331,638 26.7% 1980 360,919 8.8% 1990 367,302 1.8% 2000 393,049 7.0% 2010 391,906 −0.3% 2020 413,066 5.4% 2024 (est.) 415,154 0.5% U. S. Decennial Census Map of racial distribution in Tulsa, 2010 U. S. Census. Each dot is 25 people: ⬤ White ⬤ Black ⬤ Asian ⬤ Hispanic ⬤ Other Racial and ethnic composition Tulsa, Oklahoma – Racial and ethnic composition Note: the US Census treats Hispanic/Latino as an ethnic category. This table excludes Latinos from the racial categories and assigns them to a separate category. Hispanics/Latinos may be of any race. Race / Ethnicity ( NH = Non-Hispanic ) Pop\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide the 2020 populations for Oklahoma City (681,054) and Tulsa (413,066) and the 1990 population for Tulsa (367,302). However, the 1990 population for Oklahoma City is not present, and no data is given for any other Oklahoma cities that might have had 100,000+ residents in 2020. Therefore, the required total 1990 population cannot be calculated from the available information.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 844, + "osl": 107, + "total_tokens": 951, + "latency_ms": 4237.28, + "tokens_per_second": 25.25 + }, + "context": { + "kept_docs_count": 2, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "f4fa5258-9871-43db-ae9e-02ab066c5aeb", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T02:56:37.180785Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: According to the 1990 United States census, what was the total population of the cities in Oklahoma that had at least 100,000 residents according to the 2020 United States census?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] rated July 15, 1890 Government • Type Council–manager • Body Oklahoma City Council • Mayor David Holt ( R ) • City manager Craig Freeman Area • City 620.79 sq mi (1,607.83 km 2 ) • Land 606.48 sq mi (1,570.77 km 2 ) • Water 14.31 sq mi (37.06 km 2 ) • Urban 421.7 sq mi (1,092.3 km 2 ) Elevation 1,198 ft (365 m) Population ( 2020 ) • City 681,054 • Estimate (2024) 712,919 • Rank 62nd in North America 20th in the United States 1st in Oklahoma • Density 1,123.0/sq mi (433.58/km 2 ) • Urban 982,276 ( US: 46th ) • Urban density 2,329/sq mi (899.3/km 2 ) • Metro 1,512,242 ( US: 42nd ) Demonyms Oklahoma Cityan Oklahoma Citian GDP • Metro $100.054 billion (2023) Time zone UTC−6 ( Central (CST) ) • Summer ( DST ) UTC−5 ( CDT ) ZIP Codes 73101–73132, 73134–73137, 7313\n\n[DOC 2] storical population Census Pop. Note %± 1900 1,390 — 1910 18,182 1,208.1% 1920 72,075 296.4% 1930 141,258 96.0% 1940 142,157 0.6% 1950 182,740 28.5% 1960 261,685 43.2% 1970 331,638 26.7% 1980 360,919 8.8% 1990 367,302 1.8% 2000 393,049 7.0% 2010 391,906 −0.3% 2020 413,066 5.4% 2024 (est.) 415,154 0.5% U. S. Decennial Census Map of racial distribution in Tulsa, 2010 U. S. Census. Each dot is 25 people: ⬤ White ⬤ Black ⬤ Asian ⬤ Hispanic ⬤ Other Racial and ethnic composition Tulsa, Oklahoma – Racial and ethnic composition Note: the US Census treats Hispanic/Latino as an ethnic category. This table excludes Latinos from the racial categories and assigns them to a separate category. Hispanics/Latinos may be of any race. Race / Ethnicity ( NH = Non-Hispanic ) Pop\n\n\nSEARCH HISTORY: - Query: 'Oklahoma cities 2020 census 100000' → 2 docs\n- Query: 'Oklahoma City 1990 census population' → 0 docs\n- Query: 'Tulsa 1990 census population' → 0 docs\n- Query: 'Oklahoma cities 2020 census 100000' → 0 docs\n- Query: 'Oklahoma City population 1990 census' → 0 docs\n- Query: 'Norman Oklahoma population 2020 census' → 0 docs\n\nFEEDBACK: Need to identify Oklahoma cities with ≥100,000 residents in 2020 (likely Oklahoma City and Tulsa) and retrieve their 1990 census populations to sum them.\nNeed to identify all Oklahoma cities with ≥100,000 residents in the 2020 Census (likely Oklahoma City, Tulsa, Norman) and obtain each of their 1990 Census populations to sum them.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"List of cities in Oklahoma\",\n \"1990 census Oklahoma City\",\n \"1990 census Tulsa\"\n ],\n \"feedback\": \"We need the 2020 Census list of Oklahoma cities with populations >=100,000 to identify the relevant cities (likely Oklahoma City, Tulsa, and possibly Norman). Then we need each of those cities' 1990 Census populations to sum them. The first query gets the full list; the second and third retrieve the 1990 population figures for the two largest qualifying cities.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1225, + "osl": 733, + "total_tokens": 1958, + "latency_ms": 9412.95, + "tokens_per_second": 77.87 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "499d851c-d75b-49cf-a1c6-c352a314dfc0", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T02:56:47.160250Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: According to the 1990 United States census, what was the total population of the cities in Oklahoma that had at least 100,000 residents according to the 2020 United States census?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] rated July 15, 1890 Government • Type Council–manager • Body Oklahoma City Council • Mayor David Holt ( R ) • City manager Craig Freeman Area • City 620.79 sq mi (1,607.83 km 2 ) • Land 606.48 sq mi (1,570.77 km 2 ) • Water 14.31 sq mi (37.06 km 2 ) • Urban 421.7 sq mi (1,092.3 km 2 ) Elevation 1,198 ft (365 m) Population ( 2020 ) • City 681,054 • Estimate (2024) 712,919 • Rank 62nd in North America 20th in the United States 1st in Oklahoma • Density 1,123.0/sq mi (433.58/km 2 ) • Urban 982,276 ( US: 46th ) • Urban density 2,329/sq mi (899.3/km 2 ) • Metro 1,512,242 ( US: 42nd ) Demonyms Oklahoma Cityan Oklahoma Citian GDP • Metro $100.054 billion (2023) Time zone UTC−6 ( Central (CST) ) • Summer ( DST ) UTC−5 ( CDT ) ZIP Codes 73101–73132, 73134–73137, 7313\n\n[DOC 2] storical population Census Pop. Note %± 1900 1,390 — 1910 18,182 1,208.1% 1920 72,075 296.4% 1930 141,258 96.0% 1940 142,157 0.6% 1950 182,740 28.5% 1960 261,685 43.2% 1970 331,638 26.7% 1980 360,919 8.8% 1990 367,302 1.8% 2000 393,049 7.0% 2010 391,906 −0.3% 2020 413,066 5.4% 2024 (est.) 415,154 0.5% U. S. Decennial Census Map of racial distribution in Tulsa, 2010 U. S. Census. Each dot is 25 people: ⬤ White ⬤ Black ⬤ Asian ⬤ Hispanic ⬤ Other Racial and ethnic composition Tulsa, Oklahoma – Racial and ethnic composition Note: the US Census treats Hispanic/Latino as an ethnic category. This table excludes Latinos from the racial categories and assigns them to a separate category. Hispanics/Latinos may be of any race. Race / Ethnicity ( NH = Non-Hispanic ) Pop\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide the 2020 population for Oklahoma City (681,054) and the 1990 population for Tulsa (367,302), but they do not give Oklahoma City's 1990 population nor Tulsa's 2020 population. Additionally, no other Oklahoma cities are identified with 2020 populations of at least 100,000. Therefore, we cannot determine the total 1990 population of all Oklahoma cities that met the 2020 100,000‑resident threshold.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 890, + "osl": 509, + "total_tokens": 1399, + "latency_ms": 9280.42, + "tokens_per_second": 54.85 + }, + "context": { + "kept_docs_count": 2, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "f53c7146-9955-4476-8fc4-21b8b6368886", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T02:56:53.593410Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: According to the 1990 United States census, what was the total population of the cities in Oklahoma that had at least 100,000 residents according to the 2020 United States census?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] rated July 15, 1890 Government • Type Council–manager • Body Oklahoma City Council • Mayor David Holt ( R ) • City manager Craig Freeman Area • City 620.79 sq mi (1,607.83 km 2 ) • Land 606.48 sq mi (1,570.77 km 2 ) • Water 14.31 sq mi (37.06 km 2 ) • Urban 421.7 sq mi (1,092.3 km 2 ) Elevation 1,198 ft (365 m) Population ( 2020 ) • City 681,054 • Estimate (2024) 712,919 • Rank 62nd in North America 20th in the United States 1st in Oklahoma • Density 1,123.0/sq mi (433.58/km 2 ) • Urban 982,276 ( US: 46th ) • Urban density 2,329/sq mi (899.3/km 2 ) • Metro 1,512,242 ( US: 42nd ) Demonyms Oklahoma Cityan Oklahoma Citian GDP • Metro $100.054 billion (2023) Time zone UTC−6 ( Central (CST) ) • Summer ( DST ) UTC−5 ( CDT ) ZIP Codes 73101–73132, 73134–73137, 7313\n\n[DOC 2] storical population Census Pop. Note %± 1900 1,390 — 1910 18,182 1,208.1% 1920 72,075 296.4% 1930 141,258 96.0% 1940 142,157 0.6% 1950 182,740 28.5% 1960 261,685 43.2% 1970 331,638 26.7% 1980 360,919 8.8% 1990 367,302 1.8% 2000 393,049 7.0% 2010 391,906 −0.3% 2020 413,066 5.4% 2024 (est.) 415,154 0.5% U. S. Decennial Census Map of racial distribution in Tulsa, 2010 U. S. Census. Each dot is 25 people: ⬤ White ⬤ Black ⬤ Asian ⬤ Hispanic ⬤ Other Racial and ethnic composition Tulsa, Oklahoma – Racial and ethnic composition Note: the US Census treats Hispanic/Latino as an ethnic category. This table excludes Latinos from the racial categories and assigns them to a separate category. Hispanics/Latinos may be of any race. Race / Ethnicity ( NH = Non-Hispanic ) Pop\n\n\nSEARCH HISTORY: - Query: 'Oklahoma cities 2020 census 100000' → 2 docs\n- Query: 'Oklahoma City 1990 census population' → 0 docs\n- Query: 'Tulsa 1990 census population' → 0 docs\n- Query: 'Oklahoma cities 2020 census 100000' → 0 docs\n- Query: 'Oklahoma City population 1990 census' → 0 docs\n- Query: 'Norman Oklahoma population 2020 census' → 0 docs\n- Query: 'List of cities in Oklahoma' → 0 docs\n- Query: '1990 census Oklahoma City' → 0 docs\n- Query: '1990 census Tulsa' → 0 docs\n\nFEEDBACK: Need to identify Oklahoma cities with ≥100,000 residents in 2020 (likely Oklahoma City and Tulsa) and retrieve their 1990 census populations to sum them.\nNeed to identify all Oklahoma cities with ≥100,000 residents in the 2020 Census (likely Oklahoma City, Tulsa, Norman) and obtain each of their 1990 Census populations to sum them.\nWe need the 2020 Census list of Oklahoma cities with populations >=100,000 to identify the relevant cities (likely Oklahoma City, Tulsa, and possibly Norman). Then we need each of those cities' 1990 Census populations to sum them. The first query gets the full list; the second and third retrieve the 1990 population figures for the two largest qualifying cities.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Oklahoma cities 2020 census population list\",\n \"1990 census population Oklahoma City\",\n \"1990 census population Tulsa\"\n ],\n \"feedback\": \"Need the 2020 list of Oklahoma cities with 100,000+ residents and the 1990 census populations for those cities (Oklahoma City, Tulsa, and any others such as Norman).\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1304, + "osl": 86, + "total_tokens": 1390, + "latency_ms": 6432.21, + "tokens_per_second": 13.37 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "a1f8409c-ebb5-421f-be66-32d41fd5d259", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T02:56:56.712840Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: According to the 1990 United States census, what was the total population of the cities in Oklahoma that had at least 100,000 residents according to the 2020 United States census?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] rated July 15, 1890 Government • Type Council–manager • Body Oklahoma City Council • Mayor David Holt ( R ) • City manager Craig Freeman Area • City 620.79 sq mi (1,607.83 km 2 ) • Land 606.48 sq mi (1,570.77 km 2 ) • Water 14.31 sq mi (37.06 km 2 ) • Urban 421.7 sq mi (1,092.3 km 2 ) Elevation 1,198 ft (365 m) Population ( 2020 ) • City 681,054 • Estimate (2024) 712,919 • Rank 62nd in North America 20th in the United States 1st in Oklahoma • Density 1,123.0/sq mi (433.58/km 2 ) • Urban 982,276 ( US: 46th ) • Urban density 2,329/sq mi (899.3/km 2 ) • Metro 1,512,242 ( US: 42nd ) Demonyms Oklahoma Cityan Oklahoma Citian GDP • Metro $100.054 billion (2023) Time zone UTC−6 ( Central (CST) ) • Summer ( DST ) UTC−5 ( CDT ) ZIP Codes 73101–73132, 73134–73137, 7313\n\n[DOC 2] storical population Census Pop. Note %± 1900 1,390 — 1910 18,182 1,208.1% 1920 72,075 296.4% 1930 141,258 96.0% 1940 142,157 0.6% 1950 182,740 28.5% 1960 261,685 43.2% 1970 331,638 26.7% 1980 360,919 8.8% 1990 367,302 1.8% 2000 393,049 7.0% 2010 391,906 −0.3% 2020 413,066 5.4% 2024 (est.) 415,154 0.5% U. S. Decennial Census Map of racial distribution in Tulsa, 2010 U. S. Census. Each dot is 25 people: ⬤ White ⬤ Black ⬤ Asian ⬤ Hispanic ⬤ Other Racial and ethnic composition Tulsa, Oklahoma – Racial and ethnic composition Note: the US Census treats Hispanic/Latino as an ethnic category. This table excludes Latinos from the racial categories and assigns them to a separate category. Hispanics/Latinos may be of any race. Race / Ethnicity ( NH = Non-Hispanic ) Pop\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"We have the 1990 population for Tulsa (367,302) and the 2020 populations for Oklahoma City (681,054) and Tulsa (413,066). However, the 1990 census population for Oklahoma City is not provided in the kept documents, and we lack information about any other Oklahoma cities that reached 100,000 residents by 2020. Therefore, we cannot compute the total 1990 population of all qualifying cities.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 902, + "osl": 682, + "total_tokens": 1584, + "latency_ms": 2467.63, + "tokens_per_second": 276.38 + }, + "context": { + "kept_docs_count": 2, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "89354138-553c-48db-85f9-848290733142", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T02:56:58.849005Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: According to the 1990 United States census, what was the total population of the cities in Oklahoma that had at least 100,000 residents according to the 2020 United States census?\n\nDOCUMENTS:\n\n[DOC 1] rated July 15, 1890 Government • Type Council–manager • Body Oklahoma City Council • Mayor David Holt ( R ) • City manager Craig Freeman Area • City 620.79 sq mi (1,607.83 km 2 ) • Land 606.48 sq mi (1,570.77 km 2 ) • Water 14.31 sq mi (37.06 km 2 ) • Urban 421.7 sq mi (1,092.3 km 2 ) Elevation 1,198 ft (365 m) Population ( 2020 ) • City 681,054 • Estimate (2024) 712,919 • Rank 62nd in North America 20th in the United States 1st in Oklahoma • Density 1,123.0/sq mi (433.58/km 2 ) • Urban 982,276 ( US: 46th ) • Urban density 2,329/sq mi (899.3/km 2 ) • Metro 1,512,242 ( US: 42nd ) Demonyms Oklahoma Cityan Oklahoma Citian GDP • Metro $100.054 billion (2023) Time zone UTC−6 ( Central (CST) ) • Summer ( DST ) UTC−5 ( CDT ) ZIP Codes 73101–73132, 73134–73137, 7313\n\n[DOC 2] storical population Census Pop. Note %± 1900 1,390 — 1910 18,182 1,208.1% 1920 72,075 296.4% 1930 141,258 96.0% 1940 142,157 0.6% 1950 182,740 28.5% 1960 261,685 43.2% 1970 331,638 26.7% 1980 360,919 8.8% 1990 367,302 1.8% 2000 393,049 7.0% 2010 391,906 −0.3% 2020 413,066 5.4% 2024 (est.) 415,154 0.5% U. S. Decennial Census Map of racial distribution in Tulsa, 2010 U. S. Census. Each dot is 25 people: ⬤ White ⬤ Black ⬤ Asian ⬤ Hispanic ⬤ Other Racial and ethnic composition Tulsa, Oklahoma – Racial and ethnic composition Note: the US Census treats Hispanic/Latino as an ethnic category. This table excludes Latinos from the racial categories and assigns them to a separate category. Hispanics/Latinos may be of any race. Race / Ethnicity ( NH = Non-Hispanic ) Pop\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 768, + "osl": 344, + "total_tokens": 1112, + "latency_ms": 2135.4, + "tokens_per_second": 161.09 + }, + "context": { + "num_documents_used": 2, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:56:58.849747Z", + "summary": { + "total_llm_calls": 10, + "total_hops": 5, + "total_input_tokens": 9199, + "total_output_tokens": 3575, + "total_tokens": 12774, + "total_latency_ms": 51780.17, + "average_tokens_per_second": 86.09, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/List_of_United_States_cities_by_population", + "https://en.wikipedia.org/wiki/Oklahoma_City", + "https://en.wikipedia.org/wiki/Tulsa,_Oklahoma", + "https://en.wikipedia.org/wiki/Broken_Arrow,_Oklahoma", + "https://en.wikipedia.org/wiki/Norman,_Oklahoma" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "950135" + } + }, + { + "query_id": "23", + "original_query": "What was the political party of the person who advocated for the type of government used in Chikhali, Latur district to become the foundation of India's political system?", + "timestamp_start": "2026-05-16T02:56:58.870996Z", + "llm_calls": [ + { + "call_id": "b8814021-f185-46a9-bb5e-ad2b42b4e2ea", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:57:17.398251Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What was the political party of the person who advocated for the type of government used in Chikhali, Latur district to become the foundation of India's political system?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Chikhali Latur gram panchayat\",\n \"Mahatma Gandhi panchayat foundation India\",\n \"Mahatma Gandhi political party\"\n ],\n \"feedback\": \"First query confirms the local government type in Chikhali. Second finds the advocate linking that government type to India's political system. Third retrieves the advocate's party affiliation.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 466, + "osl": 869, + "total_tokens": 1335, + "latency_ms": 18527.11, + "tokens_per_second": 46.9 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "afbddb81-c7a5-4bdd-8d0f-87a2ffa60acf", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:57:23.077908Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: What was the political party of the person who advocated for the type of government used in Chikhali, Latur district to become the foundation of India's political system?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Chikhali may refer to several places in India: Chikhali, Rajasthan , a village in Dungarpur district, Rajasthan Maharashtra Chikhali Bk. , a village in Pune district Chikhali, Pune , a village in Pune district Chikhali, Latur district , a village in Latur district Chikhali, Ratnagiri district , a village in Ratnagiri district Chikhali (Vidhan Sabha constituency) , an Assembly constituency in Buldhana district See also Chikhli (disambiguation) Topics referred to by the same term This disambiguation page lists articles about distinct geographical locations with the same name. If an internal link led you here, you may wish to change the link to point directly to the intended article.\n\n[NEW 2] tio 1020 ♂ / ♀ Literacy 58.20% Lok Sabha constituency Kalahandi Vidhan Sabha constituency 2 071-Nuapada 072-Khariar Climate Aw ( Köppen ) Precipitation 1,230 millimetres (48 in) Website www . nuapada . nic . in Nuapada district is located in the Odisha state in India , with the town of Nuapada serving as the headquarters of the district. It has one subdivision: Nuapada , and five blocks: Khariar , Sinapali , Boden , Komna , and Nuapada . Nuapada District has three Notified Area Councils : Khariar , Khariar Road , and Nuapada , six tehsils in addition to villages, such as Gandabahali , Tukla, Hatibandha, Duajher, Bargaon , Tarbod, Udyanbandh, Singjhar, Brahmanpada , Karngamal, Sarabong, Dharambandha, Lakhna, Sunabeda, Beltukri, Bhela, Niljee, and Larka etc. H\n\n[NEW 3] entury CE The Colossal trimurti at the Elephanta Caves Painting of Padmapani Cave 1 at Ajanta The Shiva mukhalinga (faced-lingam ) from the Bhumara Temple Nalrajar Garh fortification wall in Chilapata Forests , West Bengal , is one of the last surviving fortification remains from the Gupta period, currently 5–7 m high Nalanda University was first established under Gupta Empire Bitargaon temple from the Gupta period provide one of the earliest examples of pointed arches anywhere in the world Ajanta caves from Gupta era Krishna fighting the horse demon Keshi , 5th century Family tree and list of rulers See also Portals : India History Part of a series on the History of India Timeline Prehistoric Paleolithic Masol c. 2.6 Ma Riwat c. 2.5 Ma Madrasian culture c.\n\n[NEW 4] governance and promote social justice. There have been several positive effects of this amendment, some of which are listed above. However, there is also evidence of deeply ingrained vote-trading structures maintained through extra-political means. This issue can potentially be attributed to the fact that Gram Sabhas have not been sufficiently empowered and strengthened to ensure greater public participation and transparency in the functioning of Panchayats, as envisaged in the Panchayat Act. See also Panchayat (Nepal) Khap panchayat References ^ Although it is also found in other places such as Trinidad and Tobago . ^ ^ ^ ^ Hathi Trust copy, search only Archived 5 June 2015 at the Wayback Machine ^ ^ ^ a b ^ a b ^ ^ ^ Jawaharlal Nehru , (1964), The Discover\n\n[NEW 5] ^ ^ ^ Gandhi (2008) , pp. 196–197 . ^ ^ ^ ^ ^ a b Minault (1982) , pp. 68–72, 78–82, 96–102, 108–109. ^ Minault (1982) , pp. 4–8. ^ ^ a b Ghose (1991) , p. 161–164. ^ ^ ^ ^ ^ Brown (1991) , pp. 140–147. ^ Minault (1982) , pp. 113–116. ^ ^ ^ ^ ^ ^ Markovits (2002) , p. 372. ^ ^ a b c d Wolpert (2002a) , pp. 99–103. ^ Also available at Wikisource . ^ ^ Gandhi (1990a) , p. 89. ^ Shashi (1996) , p. 9. ^ Gandhi (1990a) , p. 131. ^ Gandhi (1990) , p. 172. ^ Herman (2008) , pp. 419–420. ^ ^ ^ ^ ^ ^ a b c Dalton (2012) , pp. 8–14, 20–23, 30–35. ^ Dhiman (2016) , pp. 46–49. ^ ^ Herman (2008) , pp. 375–377 . ^ Herman (2008) , p. 359. ^ a b Herman (2008) , pp. 378–381. ^ a b Muldoon (2016) , pp. 92–99. ^ Gandhi (2008) , pp. 332–333. ^ Muldoon (2016) , p. 97. ^ Brown (\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 5 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1408, + "osl": 338, + "total_tokens": 1746, + "latency_ms": 4975.79, + "tokens_per_second": 67.93 + }, + "context": { + "num_documents_evaluated": 5, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "2092b95f-c7d9-4890-90c2-b367fde2e68d", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:57:32.122720Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What was the political party of the person who advocated for the type of government used in Chikhali, Latur district to become the foundation of India's political system?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: - Query: 'Chikhali Latur gram panchayat' → 4 docs\n- Query: 'Mahatma Gandhi panchayat foundation India' → 0 docs\n- Query: 'Mahatma Gandhi political party' → 1 docs\n\nFEEDBACK: First query confirms the local government type in Chikhali. Second finds the advocate linking that government type to India's political system. Third retrieves the advocate's party affiliation.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Mahatma Gandhi Gram Swaraj\",\n \"Mahatma Gandhi Panchayati Raj\",\n \"Mahatma Gandhi political party\"\n ],\n \"feedback\": \"First two queries aim to identify the advocate who linked the village self‑government (as in Chikhali) to India's political foundation. The third query retrieves the advocate's party affiliation.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 530, + "osl": 583, + "total_tokens": 1113, + "latency_ms": 9043.9, + "tokens_per_second": 64.46 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "7e523183-0bd9-40c7-bbde-8b83171ea045", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T02:57:56.851786Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: What was the political party of the person who advocated for the type of government used in Chikhali, Latur district to become the foundation of India's political system?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] yatirtha Jiva Goswami Jñāneśvar Kabir Kanaka Dasa Kumārila Bhaṭṭa Madhusūdana Sarasvatī Madhvacharya Matsyendranatha Morya Gosavi Mukundraj Namdev Narahari Tirtha Narasimha Saraswati Nayanars Nimbarkacharya Srinivasacharya Prabhākara Purandara Dasa Raghavendra Swami Raghunatha Siromani Raghuttama Tirtha Ram Charan Ramananda Ramanuja Ramprasad Sen Ravidas Rupa Goswami Samarth Ramdas Sankardev Satyanatha Tirtha Siddheshwar Sripada Srivallabha Sripadaraja Surdas Swaminarayan Syama Sastri Thiruvalluvar Tukaram Tulsidas Tyagaraja Vācaspati Miśra Vadiraja Tirtha Vallabha Vedanta Desika Vidyaranya Vyasaraja Modern Aurobindo Bhaktisiddhanta Sarasvati Bhaktivinoda Thakur Chandrashekarendra Saraswati Chinmayananda Dayananda Saraswati Jaggi Vasudev Krishnananda Saraswa\n\n[NEW 2] ngh 13 Shankar Dayal Sharma (1918–1999) 29 August 1984 26 November 1985 1 year, 89 days Madhya Pradesh President of the Indian National Congress 14 Kumudben Joshi (1934–2022) 26 November 1985 7 February 1990 4 years, 73 days Gujarat Deputy Minister of Health and Family Welfare 15 Krishan Kant (1927–2002) 7 February 1990 21 August 1997 7 years, 195 days Gujarat Member of Parliament, Lok Sabha R. Venkataraman Acting – Gopala Ramanujam (1915–2001) 22 August 1997 23 November 1997 93 days Tamil Nadu Governor of Odisha K. R. Narayanan 16 C. Rangarajan (1932–) 24 November 1997 3 January 2003 5 years, 40 days Tamil Nadu Governor of the Reserve Bank of India 17 Surjit Singh Barnala (1925–2017) 3 January 2003 3 November 2004 1 year, 305 days Punjab Governor of Uttarak\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 2 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 759, + "osl": 1528, + "total_tokens": 2287, + "latency_ms": 24133.49, + "tokens_per_second": 63.31 + }, + "context": { + "num_documents_evaluated": 2, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "bf392b96-4ea7-4819-9d74-6c71d03f7c6e", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T02:57:58.029353Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What was the political party of the person who advocated for the type of government used in Chikhali, Latur district to become the foundation of India's political system?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] ngh 13 Shankar Dayal Sharma (1918–1999) 29 August 1984 26 November 1985 1 year, 89 days Madhya Pradesh President of the Indian National Congress 14 Kumudben Joshi (1934–2022) 26 November 1985 7 February 1990 4 years, 73 days Gujarat Deputy Minister of Health and Family Welfare 15 Krishan Kant (1927–2002) 7 February 1990 21 August 1997 7 years, 195 days Gujarat Member of Parliament, Lok Sabha R. Venkataraman Acting – Gopala Ramanujam (1915–2001) 22 August 1997 23 November 1997 93 days Tamil Nadu Governor of Odisha K. R. Narayanan 16 C. Rangarajan (1932–) 24 November 1997 3 January 2003 5 years, 40 days Tamil Nadu Governor of the Reserve Bank of India 17 Surjit Singh Barnala (1925–2017) 3 January 2003 3 November 2004 1 year, 305 days Punjab Governor of Uttarak\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept document only lists Indian political figures and their offices; it contains no information about Chikhali, Latur district, the type of government used there, or the individual who advocated for it, nor that individual's political party.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 558, + "osl": 181, + "total_tokens": 739, + "latency_ms": 1176.66, + "tokens_per_second": 153.82 + }, + "context": { + "kept_docs_count": 1, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "3b9a168f-42ee-48ea-9fd8-9be7e7eb5f50", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T02:58:06.577190Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What was the political party of the person who advocated for the type of government used in Chikhali, Latur district to become the foundation of India's political system?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] ngh 13 Shankar Dayal Sharma (1918–1999) 29 August 1984 26 November 1985 1 year, 89 days Madhya Pradesh President of the Indian National Congress 14 Kumudben Joshi (1934–2022) 26 November 1985 7 February 1990 4 years, 73 days Gujarat Deputy Minister of Health and Family Welfare 15 Krishan Kant (1927–2002) 7 February 1990 21 August 1997 7 years, 195 days Gujarat Member of Parliament, Lok Sabha R. Venkataraman Acting – Gopala Ramanujam (1915–2001) 22 August 1997 23 November 1997 93 days Tamil Nadu Governor of Odisha K. R. Narayanan 16 C. Rangarajan (1932–) 24 November 1997 3 January 2003 5 years, 40 days Tamil Nadu Governor of the Reserve Bank of India 17 Surjit Singh Barnala (1925–2017) 3 January 2003 3 November 2004 1 year, 305 days Punjab Governor of Uttarak\n\n\nSEARCH HISTORY: - Query: 'Chikhali Latur gram panchayat' → 4 docs\n- Query: 'Mahatma Gandhi panchayat foundation India' → 0 docs\n- Query: 'Mahatma Gandhi political party' → 1 docs\n- Query: 'Mahatma Gandhi Gram Swaraj' → 2 docs\n- Query: 'Mahatma Gandhi Panchayati Raj' → 0 docs\n- Query: 'Mahatma Gandhi political party' → 0 docs\n\nFEEDBACK: First query confirms the local government type in Chikhali. Second finds the advocate linking that government type to India's political system. Third retrieves the advocate's party affiliation.\nFirst two queries aim to identify the advocate who linked the village self‑government (as in Chikhali) to India's political foundation. The third query retrieves the advocate's party affiliation.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Mahatma Gandhi affiliation\",\n \"Mahatma Gandhi party membership\",\n \"Mahatma Gandhi Indian National Congress\"\n ],\n \"feedback\": \"Need to obtain the political party of the advocate (Mahatma Gandhi) who linked the Gram Panchayat system used in Chikhali to the foundation of India's political system.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 870, + "osl": 555, + "total_tokens": 1425, + "latency_ms": 8547.18, + "tokens_per_second": 64.93 + }, + "context": { + "kept_docs_count": 1, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "50400dc4-e465-42e5-bb44-3fa1dbeb6d1f", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T02:58:19.418633Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: What was the political party of the person who advocated for the type of government used in Chikhali, Latur district to become the foundation of India's political system?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] being a member of the Communist Party, historical research published in 2011 strongly suggested that he had joined in the late 1950s or early 1960s. This was confirmed by both the SACP and the ANC after Mandela's death. According to the SACP, he was not only a member of the party, but also served on its Central Committee. We of Umkhonto have always sought to achieve liberation without bloodshed and civil clash. Even at this late hour, we hope that our first actions will awaken everyone to a realisation of the dangerous situation to which Nationalist policy is leading. We hope that we will bring the Government and its supporters to their senses before it is too late so that both government and its policies can be changed before matters reach the desperate st\n\n[NEW 2] being a member of the Communist Party, historical research published in 2011 strongly suggested that he had joined in the late 1950s or early 1960s. This was confirmed by both the SACP and the ANC after Mandela's death. According to the SACP, he was not only a member of the party, but also served on its Central Committee. We of Umkhonto have always sought to achieve liberation without bloodshed and civil clash. Even at this late hour, we hope that our first actions will awaken everyone to a realisation of the dangerous situation to which Nationalist policy is leading. We hope that we will bring the Government and its supporters to their senses before it is too late so that both government and its policies can be changed before matters reach the desperate st\n\n\nKEPT DOCUMENTS (context only):\n[1 documents already kept as relevant]\n[KEPT 1] ngh 13 Shankar Dayal Sharma (1918–1999) 29 August 1984 26 November 1985 1 year, 89 days Madhya Pradesh President of the Indian National Congress 14 Kumudben Joshi (1934–2022) 26 November 1985 7 February 1990 4 years, 73 days Gujarat Deputy Minister of Health and Family Welfare 15 Krishan Kant (1927–...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 2 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 706, + "osl": 1130, + "total_tokens": 1836, + "latency_ms": 12188.52, + "tokens_per_second": 92.71 + }, + "context": { + "num_documents_evaluated": 2, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 1 + } + }, + { + "call_id": "26023008-3a4e-4070-a563-27a096fbeca3", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T02:58:20.904522Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What was the political party of the person who advocated for the type of government used in Chikhali, Latur district to become the foundation of India's political system?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] ngh 13 Shankar Dayal Sharma (1918–1999) 29 August 1984 26 November 1985 1 year, 89 days Madhya Pradesh President of the Indian National Congress 14 Kumudben Joshi (1934–2022) 26 November 1985 7 February 1990 4 years, 73 days Gujarat Deputy Minister of Health and Family Welfare 15 Krishan Kant (1927–2002) 7 February 1990 21 August 1997 7 years, 195 days Gujarat Member of Parliament, Lok Sabha R. Venkataraman Acting – Gopala Ramanujam (1915–2001) 22 August 1997 23 November 1997 93 days Tamil Nadu Governor of Odisha K. R. Narayanan 16 C. Rangarajan (1932–) 24 November 1997 3 January 2003 5 years, 40 days Tamil Nadu Governor of the Reserve Bank of India 17 Surjit Singh Barnala (1925–2017) 3 January 2003 3 November 2004 1 year, 305 days Punjab Governor of Uttarak\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"sufficient\": false, \"reasoning\": \"Missing: Information about the person who advocated for the type of government used in Chikhali, Latur district and their political party. The kept document only lists officeholders and their positions, with no reference to Chikhali, Latur district or any advocacy.\"}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 558, + "osl": 230, + "total_tokens": 788, + "latency_ms": 1484.96, + "tokens_per_second": 154.89 + }, + "context": { + "kept_docs_count": 1, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "8165b25a-7912-41e7-b1a2-9d9cc3c8d914", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T02:58:23.153057Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What was the political party of the person who advocated for the type of government used in Chikhali, Latur district to become the foundation of India's political system?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] ngh 13 Shankar Dayal Sharma (1918–1999) 29 August 1984 26 November 1985 1 year, 89 days Madhya Pradesh President of the Indian National Congress 14 Kumudben Joshi (1934–2022) 26 November 1985 7 February 1990 4 years, 73 days Gujarat Deputy Minister of Health and Family Welfare 15 Krishan Kant (1927–2002) 7 February 1990 21 August 1997 7 years, 195 days Gujarat Member of Parliament, Lok Sabha R. Venkataraman Acting – Gopala Ramanujam (1915–2001) 22 August 1997 23 November 1997 93 days Tamil Nadu Governor of Odisha K. R. Narayanan 16 C. Rangarajan (1932–) 24 November 1997 3 January 2003 5 years, 40 days Tamil Nadu Governor of the Reserve Bank of India 17 Surjit Singh Barnala (1925–2017) 3 January 2003 3 November 2004 1 year, 305 days Punjab Governor of Uttarak\n\n\nSEARCH HISTORY: - Query: 'Chikhali Latur gram panchayat' → 4 docs\n- Query: 'Mahatma Gandhi panchayat foundation India' → 0 docs\n- Query: 'Mahatma Gandhi political party' → 1 docs\n- Query: 'Mahatma Gandhi Gram Swaraj' → 2 docs\n- Query: 'Mahatma Gandhi Panchayati Raj' → 0 docs\n- Query: 'Mahatma Gandhi political party' → 0 docs\n- Query: 'Mahatma Gandhi affiliation' → 0 docs\n- Query: 'Mahatma Gandhi party membership' → 2 docs\n- Query: 'Mahatma Gandhi Indian National Congress' → 0 docs\n\nFEEDBACK: First query confirms the local government type in Chikhali. Second finds the advocate linking that government type to India's political system. Third retrieves the advocate's party affiliation.\nFirst two queries aim to identify the advocate who linked the village self‑government (as in Chikhali) to India's political foundation. The third query retrieves the advocate's party affiliation.\nNeed to obtain the political party of the advocate (Mahatma Gandhi) who linked the Gram Panchayat system used in Chikhali to the foundation of India's political system.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Mahatma Gandhi biography\",\n \"Mahatma Gandhi political affiliation\",\n \"Mahatma Gandhi congress membership\"\n ],\n \"feedback\": \"First query retrieves Gandhi's main article with party listed in the infobox. Second directly asks for his political affiliation. Third targets his membership in the Indian National Congress to confirm the party.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 977, + "osl": 362, + "total_tokens": 1339, + "latency_ms": 2247.87, + "tokens_per_second": 161.04 + }, + "context": { + "kept_docs_count": 1, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "5940bab4-b7b4-4729-9c70-c8e7fd93d9fc", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T02:58:27.345252Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What was the political party of the person who advocated for the type of government used in Chikhali, Latur district to become the foundation of India's political system?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] ngh 13 Shankar Dayal Sharma (1918–1999) 29 August 1984 26 November 1985 1 year, 89 days Madhya Pradesh President of the Indian National Congress 14 Kumudben Joshi (1934–2022) 26 November 1985 7 February 1990 4 years, 73 days Gujarat Deputy Minister of Health and Family Welfare 15 Krishan Kant (1927–2002) 7 February 1990 21 August 1997 7 years, 195 days Gujarat Member of Parliament, Lok Sabha R. Venkataraman Acting – Gopala Ramanujam (1915–2001) 22 August 1997 23 November 1997 93 days Tamil Nadu Governor of Odisha K. R. Narayanan 16 C. Rangarajan (1932–) 24 November 1997 3 January 2003 5 years, 40 days Tamil Nadu Governor of the Reserve Bank of India 17 Surjit Singh Barnala (1925–2017) 3 January 2003 3 November 2004 1 year, 305 days Punjab Governor of Uttarak\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept document only lists Indian political figures with their positions; it does not mention the individual who advocated for the type of government used in Chikhali, Latur district, nor does it provide any connection between that advocacy and a political party. Therefore, the required facts to answer the question are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 504, + "osl": 76, + "total_tokens": 580, + "latency_ms": 3516.62, + "tokens_per_second": 21.61 + }, + "context": { + "kept_docs_count": 1, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "e1935978-4b77-4e0d-8a7e-8d5e479a7015", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T02:58:29.186756Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: What was the political party of the person who advocated for the type of government used in Chikhali, Latur district to become the foundation of India's political system?\n\nDOCUMENTS:\n\n[DOC 1] ngh 13 Shankar Dayal Sharma (1918–1999) 29 August 1984 26 November 1985 1 year, 89 days Madhya Pradesh President of the Indian National Congress 14 Kumudben Joshi (1934–2022) 26 November 1985 7 February 1990 4 years, 73 days Gujarat Deputy Minister of Health and Family Welfare 15 Krishan Kant (1927–2002) 7 February 1990 21 August 1997 7 years, 195 days Gujarat Member of Parliament, Lok Sabha R. Venkataraman Acting – Gopala Ramanujam (1915–2001) 22 August 1997 23 November 1997 93 days Tamil Nadu Governor of Odisha K. R. Narayanan 16 C. Rangarajan (1932–) 24 November 1997 3 January 2003 5 years, 40 days Tamil Nadu Governor of the Reserve Bank of India 17 Surjit Singh Barnala (1925–2017) 3 January 2003 3 November 2004 1 year, 305 days Punjab Governor of Uttarak\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 415, + "osl": 113, + "total_tokens": 528, + "latency_ms": 1840.75, + "tokens_per_second": 61.39 + }, + "context": { + "num_documents_used": 1, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:58:29.187553Z", + "summary": { + "total_llm_calls": 11, + "total_hops": 5, + "total_input_tokens": 7751, + "total_output_tokens": 5965, + "total_tokens": 13716, + "total_latency_ms": 87682.85, + "average_tokens_per_second": 86.64, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Chikhali,_Latur_district", + "https://en.wikipedia.org/wiki/Panchayati_raj", + "https://en.wikipedia.org/wiki/Mahatma_Gandhi" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "Indian National Congress" + } + }, + { + "query_id": "24", + "original_query": "Giorgio Rognoni was an Italian professional footballer who played as a midfielder. 10 years after his death who was the midfielder who played in Milan that was born in Besana in Brianza,?", + "timestamp_start": "2026-05-16T02:58:29.208825Z", + "llm_calls": [ + { + "call_id": "f567baeb-eb85-42bd-bc11-939702b5853d", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:58:31.656581Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: Giorgio Rognoni was an Italian professional footballer who played as a midfielder. 10 years after his death who was the midfielder who played in Milan that was born in Besana in Brianza,?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Besana in Brianza footballer\",\n \"Milan midfielder birthplace Besana\",\n \"born Besana in Brianza AC Milan\"\n ],\n \"feedback\": \"We need to identify the midfielder born in Besana in Brianza who played for Milan, roughly 10 years after Giorgio Rognoni's 1994 death (circa 2004). These queries target the player's birthplace and club affiliation.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 463, + "osl": 395, + "total_tokens": 858, + "latency_ms": 2447.58, + "tokens_per_second": 161.38 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "2330cd4f-3ebe-40f8-9676-cb4d4351c6f4", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:58:42.089235Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: Giorgio Rognoni was an Italian professional footballer who played as a midfielder. 10 years after his death who was the midfielder who played in Milan that was born in Besana in Brianza,?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Jean Onana (on loan from Beşiktaş ) 15 DF ENG Brooke Norton-Cuffy 16 GK NED Justin Bijlow 17 MF UKR Ruslan Malinovskyi 18 FW GHA Caleb Ekuban No. Pos. Nation Player 20 DF ITA Stefano Sabelli 21 FW ITA Jeff Ekhator 22 DF MEX Johan Vásquez ( captain ) 27 DF ITA Alessandro Marcandalli 29 FW ITA Lorenzo Colombo (on loan from Milan ) 31 GK SUI Benjamin Siegrist (on loan from Rapid București ) 32 MF DEN Morten Frendrup 34 DF DEN Sebastian Otoa 39 GK ITA Daniele Sommariva 70 MF CIV Maxwel Cornet (on loan from West Ham United ) 73 MF ITA Patrizio Masini 77 MF ISL Mikael Egill Ellertsson Primavera Out on loan As of 2 February 2026 Note: Flags indicate national team as defined under FIFA eligibility rules ; some limited exceptions apply. Players may hold more than on\n\n[NEW 2] vić 11 DF ITA Alessandro Costacurta 13 DF ITA Francesco Coco 14 DF NED Michael Reiziger 15 MF ITA Massimo Ambrosini 16 MF ITA Tomas Locatelli 17 GK ITA Gabriele Aldegani 18 FW ITA Roberto Baggio 19 FW FRA Christophe Dugarry No. Pos. Nation Player 20 MF CRO Zvonimir Boban 21 DF ITA Mauro Tassotti (vice-captain) 22 MF NED Edgar Davids 23 FW ITA Marco Simone 24 MF ITA Stefano Eranio 25 GK ITA Angelo Pagotto 27 FW ITA Luca Saudati 28 MF ITA Matteo Pelatti 29 DF ITA Pietro Vierchowod 31 MF ITA Vincenzo Maiolo 32 DF ITA Daniele Daino 34 MF SWE Jesper Blomqvist 35 DF YUG Miodrag Vukotić 36 DF ITA Matteo Placida 38 DF ITA Alberto Comazzi 39 GK ITA Massimo Prete 40 MF ITA Massimiliano Grego 42 FW ITA Massimo Maccarone Transfers In Pos. Name from Type MF Edgar Davids\n\n[NEW 3] ames of 2000, 2004 and 2008. She won the silver medal in Beijing in 2008 in the team competition category. Jesé , born in Las Palmas de Gran Canaria in 1993, plays association football for Las Palmas . Christo Bezuidenhout , born in Tenerife in 1970, played rugby union for Gloucester and South Africa . Pedri , born in Tegueste in 2002, plays association football for Barcelona . Misa Rodríguez , born in Las Palmas de Gran Canaria in 1999, plays association football for Real Madrid Femenino . Member of the 2023 Women's World Cup winning Spain women's national football team . Nico Paz , born in Santa Cruz de Tenerife in 2004, plays association football for Como . See also Islands portal Spain portal History Battle of Santa Cruz de Tenerife (1797) First Battle o\n\n[NEW 4] noni began playing football with local side Modena. In 1967, he signed with Milan, where he would make his Serie A debut against Mantova on 11 February 1968. He played for 9 seasons (193 games, 13 goals) in the Serie A for A. C. Milan , U. S. Foggia , A. C. Cesena and A. C. Pistoiese . Honours Milan Serie A champion: 1967–68 . European Cup winner: 1968–69 . UEFA Cup Winners' Cup winner: 1967–68 . Intercontinental Cup winner: 1969 . References ^ This biographical article related to association football in Italy, about a midfielder born in the 1940s, is a stub . You can help Wikipedia by adding missing information . v t e\n\n[NEW 5] tiring that year. A vital member of the Italy national team , Albertini was part of the squads that competed at the World Cups of 1994 and 1998 , as well as the 1996 and 2000 European Championships , reaching the finals of the 1994 World Cup and Euro 2000. Club career A young Albertini during his time with Padova Albertini, born in Besana in Brianza , province of Monza e Brianza near Milan, emerged as a product of AC Milan's youth system , and went on to spend 14 highly successful years with the senior club after debuting in Serie A as a 17-year-old during the 1988–89 season under Arrigo Sacchi , on 15 January 1989, in a 4–0 home win over Como . He spent part of the 1990–91 season on loan at Padova Calcio in Serie B , collecting 28 appearances and 5 goals, i\n\n[NEW 6] lver Medal of Military Valor was then buried under the \"Gradinata Nord\" of the stadium, home of the Genoa ultras. References ^ ^ ^ {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ a b ^ a b ^ This biographical article related to association football in Italy, about a midfielder born in the 1880s, is a stub . You can help Wikipedia by adding missing information . v t e\n\n[NEW 7] n Jussi Roine Teemu Keisteri France France Télévisions Slimane \" Mon amour \" French Meïr Salah Slimane Nebchi Yaacov Salah Georgia GPB Nutsa Buzaladze \" Firefighter \" English Ada Skitka Darko Dimitrov Germany NDR Isaak \" Always on the Run \" English Greg Taro Isaak Guderian Kevin Lehr Leo Salminen Greece ERT Marina Satti \" Zari \" ( Ζάρι ) Greek Gino Borri Jay Lewitt Stolar Jordan Richard Palmer Konstantin Plamenov Beshkov Manolis Solidakis Marina Satti Nick Kodonas Oge Vlospa Iceland RÚV Hera Björk \" Scared of Heights \" English Ásdís María Viðarsdóttir Ferras Alqaisi Jaro Omar Michael Burek Ireland RTÉ Bambie Thug \" Doomsday Blue \" English Bambie Ray Robinson Olivia Cassy Brooking Sam Matlock Tyler Ryder Israel IPBC Eden Golan \" Hurricane \" English, Hebrew Av\n\n[NEW 8] Cuban American actress (b. 1991) Carmela Zumbado Born ( 1991-02-27 ) February 27, 1991 (age 35) Occupation Actress Years active 2012–present Relatives Gigi Zumbado (sister) Marisela Zumbado (sister) Carmela Zumbado (born February 27, 1991) is an American actress. Her breakthrough role was as Delilah Alves in the Netflix thriller series You (2019). She subsequently earned praise for playing Ximena Arista in the drama film The Wall of Mexico (2019), and has additionally played Jeny B in the action film Need for Speed (2014) and Denise Martinez on the musical series Crazy Ex-Girlfriend (2019). She appeared on season nine of Chicago P. D. in a recurring role. Early life Zumbado is a second-generation Cuban-American and was born and raised in Miami, Florida . She\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 8 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0, 1, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1851, + "osl": 509, + "total_tokens": 2360, + "latency_ms": 9682.36, + "tokens_per_second": 52.57 + }, + "context": { + "num_documents_evaluated": 8, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "89c29480-92b5-4805-bd47-913df06e927e", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:58:53.708197Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: Giorgio Rognoni was an Italian professional footballer who played as a midfielder. 10 years after his death who was the midfielder who played in Milan that was born in Besana in Brianza,?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] tiring that year. A vital member of the Italy national team , Albertini was part of the squads that competed at the World Cups of 1994 and 1998 , as well as the 1996 and 2000 European Championships , reaching the finals of the 1994 World Cup and Euro 2000. Club career A young Albertini during his time with Padova Albertini, born in Besana in Brianza , province of Monza e Brianza near Milan, emerged as a product of AC Milan's youth system , and went on to spend 14 highly successful years with the senior club after debuting in Serie A as a 17-year-old during the 1988–89 season under Arrigo Sacchi , on 15 January 1989, in a 4–0 home win over Como . He spent part of the 1990–91 season on loan at Padova Calcio in Serie B , collecting 28 appearances and 5 goals, i\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": true,\n \"reasoning\": \"Document 1 provides that Demetrio Albertini, a midfielder, was born in Besana in Brianza and spent his senior career with AC Milan. This matches the description sought for the midfielder who played in Milan and was born in Besana in Brianza, 10 years after Giorgio Rognoni's death.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 506, + "osl": 537, + "total_tokens": 1043, + "latency_ms": 11617.99, + "tokens_per_second": 46.22 + }, + "context": { + "kept_docs_count": 1, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "a130c6bf-dcb1-46a7-8cd0-e77f45457b47", + "component": "answer_generator", + "hop_count": 2, + "timestamp": "2026-05-16T02:58:56.390141Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: Giorgio Rognoni was an Italian professional footballer who played as a midfielder. 10 years after his death who was the midfielder who played in Milan that was born in Besana in Brianza,?\n\nDOCUMENTS:\n\n[DOC 1] tiring that year. A vital member of the Italy national team , Albertini was part of the squads that competed at the World Cups of 1994 and 1998 , as well as the 1996 and 2000 European Championships , reaching the finals of the 1994 World Cup and Euro 2000. Club career A young Albertini during his time with Padova Albertini, born in Besana in Brianza , province of Monza e Brianza near Milan, emerged as a product of AC Milan's youth system , and went on to spend 14 highly successful years with the senior club after debuting in Serie A as a 17-year-old during the 1988–89 season under Arrigo Sacchi , on 15 January 1989, in a 4–0 home win over Como . He spent part of the 1990–91 season on loan at Padova Calcio in Serie B , collecting 28 appearances and 5 goals, i\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "The midfielder is **Demetrio Albertini**.", + "finish_reason": "stop" + }, + "metrics": { + "isl": 375, + "osl": 164, + "total_tokens": 539, + "latency_ms": 2681.32, + "tokens_per_second": 61.16 + }, + "context": { + "num_documents_used": 1, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T02:58:56.390905Z", + "summary": { + "total_llm_calls": 4, + "total_hops": 2, + "total_input_tokens": 3195, + "total_output_tokens": 1605, + "total_tokens": 4800, + "total_latency_ms": 26429.25, + "average_tokens_per_second": 80.33, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Giorgio_Rognoni", + "https://en.wikipedia.org/wiki/1996%E2%80%9397_AC_Milan_season", + "https://en.wikipedia.org/wiki/Demetrio_Albertini" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "The midfielder is **Demetrio Albertini**.", + "ground_truth_answer": "Demetrio Albertini" + } + }, + { + "query_id": "25", + "original_query": "What was the age difference between Mike Tyson and Tyson Fury on the respective days on which they lost their first ever fights? Represent the figure in years only.", + "timestamp_start": "2026-05-16T02:58:56.411644Z", + "llm_calls": [ + { + "call_id": "4ad8ecb8-1322-43af-9b05-df28e16e8b8e", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T02:58:58.128408Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What was the age difference between Mike Tyson and Tyson Fury on the respective days on which they lost their first ever fights? Represent the figure in years only.\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Mike Tyson birthdate\",\n \"Mike Tyson first loss\",\n \"Tyson Fury first loss\"\n ],\n \"feedback\": \"Need birthdate and date of first professional loss for both boxers to compute ages on those days.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 469, + "osl": 545, + "total_tokens": 1014, + "latency_ms": 1716.64, + "tokens_per_second": 317.48 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "0e910c47-5ac5-4d57-983e-dadf74429f0c", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T02:59:01.394850Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: What was the age difference between Mike Tyson and Tyson Fury on the respective days on which they lost their first ever fights? Represent the figure in years only.\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] American boxer and media personality (born 1966) Mike Tyson Tyson at GalaxyCon , 2023 Personal information Nicknames Iron Kid Dynamite The Baddest Man on the Planet Born Michael Gerard Tyson ( 1966-06-30 ) June 30, 1966 (age 59) Brooklyn , New York City, U. S. Height 5 ft 10 in (178 cm) Weight Heavyweight Spouses Robin Givens ( m. 1988; div. 1989) Monica Turner ( m. 1997; div. 2003) Lakiha Spicer ( m. 2009) Children 7 Website miketyson . com Signature Boxing career Reach 71 in (180 cm) Stance Orthodox Boxing record Total fights 59 Wins 50 Win by KO 44 Losses 7 No contests 2 Medal record Men's amateur boxing National Junior Olympics 1981 North Carolina Heavyweight 1982 Tennessee Heavyweight Golden Gloves 1984 New York Heavyweight Michael Gerard Tyson (born Ju\n\n[NEW 2] hought of as a bum.\" Since his return from his hiatus, Fury has stated that he still feels bias against his community. Fury is a fan of Manchester United and attends football matches at their home ground Old Trafford . He also supports the England national team . Professional boxing record 37 fights 34 wins 2 losses By knockout 24 0 By decision 10 2 Draws 1 No. Result Record Opponent Type Round, time Date Location Notes 37 Loss 34–2–1 Oleksandr Usyk UD 12 21 Dec 2024 Kingdom Arena, Riyadh, Saudi Arabia For WBA (Super), WBC, WBO, IBO and The Ring heavyweight titles 36 Loss 34–1–1 Oleksandr Usyk SD 12 18 May 2024 Kingdom Arena, Riyadh, Saudi Arabia Lost WBC heavyweight title; For WBA (Super), IBF, WBO, IBO, and The Ring heavyweight titles 35 Win 34–0–1 Francis\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 2 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[1,1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 700, + "osl": 319, + "total_tokens": 1019, + "latency_ms": 2716.09, + "tokens_per_second": 117.45 + }, + "context": { + "num_documents_evaluated": 2, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "e78d3a7e-f876-408a-8d1d-23b8bcb07ac9", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T02:59:04.539005Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What was the age difference between Mike Tyson and Tyson Fury on the respective days on which they lost their first ever fights? Represent the figure in years only.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American boxer and media personality (born 1966) Mike Tyson Tyson at GalaxyCon , 2023 Personal information Nicknames Iron Kid Dynamite The Baddest Man on the Planet Born Michael Gerard Tyson ( 1966-06-30 ) June 30, 1966 (age 59) Brooklyn , New York City, U. S. Height 5 ft 10 in (178 cm) Weight Heavyweight Spouses Robin Givens ( m. 1988; div. 1989) Monica Turner ( m. 1997; div. 2003) Lakiha Spicer ( m. 2009) Children 7 Website miketyson . com Signature Boxing career Reach 71 in (180 cm) Stance Orthodox Boxing record Total fights 59 Wins 50 Win by KO 44 Losses 7 No contests 2 Medal record Men's amateur boxing National Junior Olympics 1981 North Carolina Heavyweight 1982 Tennessee Heavyweight Golden Gloves 1984 New York Heavyweight Michael Gerard Tyson (born Ju\n\n[DOC 2] hought of as a bum.\" Since his return from his hiatus, Fury has stated that he still feels bias against his community. Fury is a fan of Manchester United and attends football matches at their home ground Old Trafford . He also supports the England national team . Professional boxing record 37 fights 34 wins 2 losses By knockout 24 0 By decision 10 2 Draws 1 No. Result Record Opponent Type Round, time Date Location Notes 37 Loss 34–2–1 Oleksandr Usyk UD 12 21 Dec 2024 Kingdom Arena, Riyadh, Saudi Arabia For WBA (Super), WBC, WBO, IBO and The Ring heavyweight titles 36 Loss 34–1–1 Oleksandr Usyk SD 12 18 May 2024 Kingdom Arena, Riyadh, Saudi Arabia Lost WBC heavyweight title; For WBA (Super), IBF, WBO, IBO, and The Ring heavyweight titles 35 Win 34–0–1 Francis\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide Mike Tyson's birth date and general career record, and some information about Tyson Fury's recent fights, but they do not include the dates of each boxer's first professional loss nor Tyson Fury's birth date. Without the dates of their first losses (and Fury's birth date), we cannot calculate the ages on those days or the age difference.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 736, + "osl": 356, + "total_tokens": 1092, + "latency_ms": 3143.35, + "tokens_per_second": 113.26 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "cebbbdaa-71ff-42d2-99e5-a1be12632dfa", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T02:59:18.464697Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What was the age difference between Mike Tyson and Tyson Fury on the respective days on which they lost their first ever fights? Represent the figure in years only.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American boxer and media personality (born 1966) Mike Tyson Tyson at GalaxyCon , 2023 Personal information Nicknames Iron Kid Dynamite The Baddest Man on the Planet Born Michael Gerard Tyson ( 1966-06-30 ) June 30, 1966 (age 59) Brooklyn , New York City, U. S. Height 5 ft 10 in (178 cm) Weight Heavyweight Spouses Robin Givens ( m. 1988; div. 1989) Monica Turner ( m. 1997; div. 2003) Lakiha Spicer ( m. 2009) Children 7 Website miketyson . com Signature Boxing career Reach 71 in (180 cm) Stance Orthodox Boxing record Total fights 59 Wins 50 Win by KO 44 Losses 7 No contests 2 Medal record Men's amateur boxing National Junior Olympics 1981 North Carolina Heavyweight 1982 Tennessee Heavyweight Golden Gloves 1984 New York Heavyweight Michael Gerard Tyson (born Ju\n\n[DOC 2] hought of as a bum.\" Since his return from his hiatus, Fury has stated that he still feels bias against his community. Fury is a fan of Manchester United and attends football matches at their home ground Old Trafford . He also supports the England national team . Professional boxing record 37 fights 34 wins 2 losses By knockout 24 0 By decision 10 2 Draws 1 No. Result Record Opponent Type Round, time Date Location Notes 37 Loss 34–2–1 Oleksandr Usyk UD 12 21 Dec 2024 Kingdom Arena, Riyadh, Saudi Arabia For WBA (Super), WBC, WBO, IBO and The Ring heavyweight titles 36 Loss 34–1–1 Oleksandr Usyk SD 12 18 May 2024 Kingdom Arena, Riyadh, Saudi Arabia Lost WBC heavyweight title; For WBA (Super), IBF, WBO, IBO, and The Ring heavyweight titles 35 Win 34–0–1 Francis\n\n\nSEARCH HISTORY: - Query: 'Mike Tyson birthdate' → 1 docs\n- Query: 'Mike Tyson first loss' → 0 docs\n- Query: 'Tyson Fury first loss' → 1 docs\n\nFEEDBACK: Need birthdate and date of first professional loss for both boxers to compute ages on those days.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Mike Tyson vs Buster Douglas date\",\n \"Tyson Fury vs Wladimir Klitschko date\",\n \"Tyson Fury birthdate\"\n ],\n \"feedback\": \"Missing: exact dates of Mike Tyson's first professional loss and Tyson Fury's first professional loss, plus Tyson Fury's birthdate. The previous query for Mike Tyson's first loss returned no results, so the new query targets the specific bout. The Fury loss query targets his bout with Klitschko to obtain the date. The birthdate query will provide Fury's age for the calculation.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 937, + "osl": 941, + "total_tokens": 1878, + "latency_ms": 13925.07, + "tokens_per_second": 67.58 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "e8fd5ec9-f8c7-48a3-8834-d257e4222fc6", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T02:59:21.840667Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: What was the age difference between Mike Tyson and Tyson Fury on the respective days on which they lost their first ever fights? Represent the figure in years only.\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Boxing match Tyson is Back! Promotional poster Date February 11, 1990 Venue Tokyo Dome , Tokyo , Japan Title(s) on the line WBA , WBC and IBF undisputed heavyweight championship Tale of the tape Boxer Mike Tyson James Douglas Nickname Iron Buster Hometown Catskill , New York , US Columbus, Ohio , US Purse $6,000,000 $1,300,000 Pre-fight record 37–0 (33 KO) 29–4–1 (1) (19 KO) Age 23 years, 7 months 29 years, 10 months Height 5 ft 10 in (178 cm) 6 ft 4 in (193 cm) Weight 220 + 1 ⁄ 2 lb (100 kg) 231 + 1 ⁄ 2 lb (105 kg) Style Orthodox Orthodox Recognition WBA , WBC and IBF undisputed Heavyweight Champion The Ring No. 1 Ranked Heavyweight The Ring No. 1 ranked pound-for-pound fighter IBF No. 2 Ranked Heavyweight WBC No. 3 Ranked Heavyweight WBA No. 4 Ranked Heavy\n\n[NEW 2] out, with a date of 23 December being reported but not officially announced. It was also reported that Anthony Joshua vs. Deontay Wilder would serve as the co-feature. Then, on 28 October, after Fury defeated former UFC Heavyweight Champion Francis Ngannou , he demanded that the bout with Usyk must be postponed because of injuries he sustained in the fight, despite Usyk stating that they were contracted for 23 December 2023. On 29 October, it was announced that the bout would take place in early 2024. On 16 November, the bout was officially announced to take place on 17 February 2024 in Riyadh , Saudi Arabia , with a launch press conference in London . Again, the fight was officially postponed on 2 February 2024, with the idea of rescheduling for 2024 once a\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] American boxer and media personality (born 1966) Mike Tyson Tyson at GalaxyCon , 2023 Personal information Nicknames Iron Kid Dynamite The Baddest Man on the Planet Born Michael Gerard Tyson ( 1966-06-30 ) June 30, 1966 (age 59) Brooklyn , New York City, U. S. Height 5 ft 10 in (178 cm) Weight Heavy...\n[KEPT 2] hought of as a bum.\" Since his return from his hiatus, Fury has stated that he still feels bias against his community. Fury is a fan of Manchester United and attends football matches at their home ground Old Trafford . He also supports the England national team . Professional boxing record 37 fights...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 2 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[1,0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 852, + "osl": 193, + "total_tokens": 1045, + "latency_ms": 2824.48, + "tokens_per_second": 68.33 + }, + "context": { + "num_documents_evaluated": 2, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "c23d0253-8648-4e10-b64c-17a5efd739ec", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T02:59:27.388492Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What was the age difference between Mike Tyson and Tyson Fury on the respective days on which they lost their first ever fights? Represent the figure in years only.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American boxer and media personality (born 1966) Mike Tyson Tyson at GalaxyCon , 2023 Personal information Nicknames Iron Kid Dynamite The Baddest Man on the Planet Born Michael Gerard Tyson ( 1966-06-30 ) June 30, 1966 (age 59) Brooklyn , New York City, U. S. Height 5 ft 10 in (178 cm) Weight Heavyweight Spouses Robin Givens ( m. 1988; div. 1989) Monica Turner ( m. 1997; div. 2003) Lakiha Spicer ( m. 2009) Children 7 Website miketyson . com Signature Boxing career Reach 71 in (180 cm) Stance Orthodox Boxing record Total fights 59 Wins 50 Win by KO 44 Losses 7 No contests 2 Medal record Men's amateur boxing National Junior Olympics 1981 North Carolina Heavyweight 1982 Tennessee Heavyweight Golden Gloves 1984 New York Heavyweight Michael Gerard Tyson (born Ju\n\n[DOC 2] hought of as a bum.\" Since his return from his hiatus, Fury has stated that he still feels bias against his community. Fury is a fan of Manchester United and attends football matches at their home ground Old Trafford . He also supports the England national team . Professional boxing record 37 fights 34 wins 2 losses By knockout 24 0 By decision 10 2 Draws 1 No. Result Record Opponent Type Round, time Date Location Notes 37 Loss 34–2–1 Oleksandr Usyk UD 12 21 Dec 2024 Kingdom Arena, Riyadh, Saudi Arabia For WBA (Super), WBC, WBO, IBO and The Ring heavyweight titles 36 Loss 34–1–1 Oleksandr Usyk SD 12 18 May 2024 Kingdom Arena, Riyadh, Saudi Arabia Lost WBC heavyweight title; For WBA (Super), IBF, WBO, IBO, and The Ring heavyweight titles 35 Win 34–0–1 Francis\n\n[DOC 3] Boxing match Tyson is Back! Promotional poster Date February 11, 1990 Venue Tokyo Dome , Tokyo , Japan Title(s) on the line WBA , WBC and IBF undisputed heavyweight championship Tale of the tape Boxer Mike Tyson James Douglas Nickname Iron Buster Hometown Catskill , New York , US Columbus, Ohio , US Purse $6,000,000 $1,300,000 Pre-fight record 37–0 (33 KO) 29–4–1 (1) (19 KO) Age 23 years, 7 months 29 years, 10 months Height 5 ft 10 in (178 cm) 6 ft 4 in (193 cm) Weight 220 + 1 ⁄ 2 lb (100 kg) 231 + 1 ⁄ 2 lb (105 kg) Style Orthodox Orthodox Recognition WBA , WBC and IBF undisputed Heavyweight Champion The Ring No. 1 Ranked Heavyweight The Ring No. 1 ranked pound-for-pound fighter IBF No. 2 Ranked Heavyweight WBC No. 3 Ranked Heavyweight WBA No. 4 Ranked Heavy\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"We have Mike Tyson's birthdate and his age on February 11, 1990 (the date of his first loss) from DOC 1 and DOC 3. However, the documents do not provide Tyson Fury's birthdate or the date of his first loss, so we cannot determine his age on that day. Therefore, the required information to calculate the age difference is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 917, + "osl": 93, + "total_tokens": 1010, + "latency_ms": 5547.05, + "tokens_per_second": 16.77 + }, + "context": { + "kept_docs_count": 3, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "d9438ce1-e6d2-4c3b-8425-25db1538c0ff", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T02:59:32.679692Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What was the age difference between Mike Tyson and Tyson Fury on the respective days on which they lost their first ever fights? Represent the figure in years only.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American boxer and media personality (born 1966) Mike Tyson Tyson at GalaxyCon , 2023 Personal information Nicknames Iron Kid Dynamite The Baddest Man on the Planet Born Michael Gerard Tyson ( 1966-06-30 ) June 30, 1966 (age 59) Brooklyn , New York City, U. S. Height 5 ft 10 in (178 cm) Weight Heavyweight Spouses Robin Givens ( m. 1988; div. 1989) Monica Turner ( m. 1997; div. 2003) Lakiha Spicer ( m. 2009) Children 7 Website miketyson . com Signature Boxing career Reach 71 in (180 cm) Stance Orthodox Boxing record Total fights 59 Wins 50 Win by KO 44 Losses 7 No contests 2 Medal record Men's amateur boxing National Junior Olympics 1981 North Carolina Heavyweight 1982 Tennessee Heavyweight Golden Gloves 1984 New York Heavyweight Michael Gerard Tyson (born Ju\n\n[DOC 2] hought of as a bum.\" Since his return from his hiatus, Fury has stated that he still feels bias against his community. Fury is a fan of Manchester United and attends football matches at their home ground Old Trafford . He also supports the England national team . Professional boxing record 37 fights 34 wins 2 losses By knockout 24 0 By decision 10 2 Draws 1 No. Result Record Opponent Type Round, time Date Location Notes 37 Loss 34–2–1 Oleksandr Usyk UD 12 21 Dec 2024 Kingdom Arena, Riyadh, Saudi Arabia For WBA (Super), WBC, WBO, IBO and The Ring heavyweight titles 36 Loss 34–1–1 Oleksandr Usyk SD 12 18 May 2024 Kingdom Arena, Riyadh, Saudi Arabia Lost WBC heavyweight title; For WBA (Super), IBF, WBO, IBO, and The Ring heavyweight titles 35 Win 34–0–1 Francis\n\n[DOC 3] Boxing match Tyson is Back! Promotional poster Date February 11, 1990 Venue Tokyo Dome , Tokyo , Japan Title(s) on the line WBA , WBC and IBF undisputed heavyweight championship Tale of the tape Boxer Mike Tyson James Douglas Nickname Iron Buster Hometown Catskill , New York , US Columbus, Ohio , US Purse $6,000,000 $1,300,000 Pre-fight record 37–0 (33 KO) 29–4–1 (1) (19 KO) Age 23 years, 7 months 29 years, 10 months Height 5 ft 10 in (178 cm) 6 ft 4 in (193 cm) Weight 220 + 1 ⁄ 2 lb (100 kg) 231 + 1 ⁄ 2 lb (105 kg) Style Orthodox Orthodox Recognition WBA , WBC and IBF undisputed Heavyweight Champion The Ring No. 1 Ranked Heavyweight The Ring No. 1 ranked pound-for-pound fighter IBF No. 2 Ranked Heavyweight WBC No. 3 Ranked Heavyweight WBA No. 4 Ranked Heavy\n\n\nSEARCH HISTORY: - Query: 'Mike Tyson birthdate' → 1 docs\n- Query: 'Mike Tyson first loss' → 0 docs\n- Query: 'Tyson Fury first loss' → 1 docs\n- Query: 'Mike Tyson vs Buster Douglas date' → 1 docs\n- Query: 'Tyson Fury vs Wladimir Klitschko date' → 1 docs\n- Query: 'Tyson Fury birthdate' → 0 docs\n\nFEEDBACK: Need birthdate and date of first professional loss for both boxers to compute ages on those days.\nMissing: exact dates of Mike Tyson's first professional loss and Tyson Fury's first professional loss, plus Tyson Fury's birthdate. The previous query for Mike Tyson's first loss returned no results, so the new query targets the specific bout. The Fury loss query targets his bout with Klitschko to obtain the date. The birthdate query will provide Fury's age for the calculation.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Tyson Fury\",\n \"Tyson Fury vs Wladimir Klitschko date\",\n \"Mike Tyson vs Buster Douglas date\"\n ],\n \"feedback\": \"Missing: Tyson Fury's birthdate (to compute age on his first loss) and the exact dates of each boxer's first professional loss. The first query gets Fury's full biography (including birthdate). The second retrieves the date of Fury's first loss to Klitschko. The third ensures we have the date of Tyson's first loss to Buster Douglas.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1260, + "osl": 117, + "total_tokens": 1377, + "latency_ms": 5290.61, + "tokens_per_second": 22.11 + }, + "context": { + "kept_docs_count": 3, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "7f99d2ef-4be0-496c-b0cc-5c17f86aedac", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T02:59:48.204599Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What was the age difference between Mike Tyson and Tyson Fury on the respective days on which they lost their first ever fights? Represent the figure in years only.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American boxer and media personality (born 1966) Mike Tyson Tyson at GalaxyCon , 2023 Personal information Nicknames Iron Kid Dynamite The Baddest Man on the Planet Born Michael Gerard Tyson ( 1966-06-30 ) June 30, 1966 (age 59) Brooklyn , New York City, U. S. Height 5 ft 10 in (178 cm) Weight Heavyweight Spouses Robin Givens ( m. 1988; div. 1989) Monica Turner ( m. 1997; div. 2003) Lakiha Spicer ( m. 2009) Children 7 Website miketyson . com Signature Boxing career Reach 71 in (180 cm) Stance Orthodox Boxing record Total fights 59 Wins 50 Win by KO 44 Losses 7 No contests 2 Medal record Men's amateur boxing National Junior Olympics 1981 North Carolina Heavyweight 1982 Tennessee Heavyweight Golden Gloves 1984 New York Heavyweight Michael Gerard Tyson (born Ju\n\n[DOC 2] hought of as a bum.\" Since his return from his hiatus, Fury has stated that he still feels bias against his community. Fury is a fan of Manchester United and attends football matches at their home ground Old Trafford . He also supports the England national team . Professional boxing record 37 fights 34 wins 2 losses By knockout 24 0 By decision 10 2 Draws 1 No. Result Record Opponent Type Round, time Date Location Notes 37 Loss 34–2–1 Oleksandr Usyk UD 12 21 Dec 2024 Kingdom Arena, Riyadh, Saudi Arabia For WBA (Super), WBC, WBO, IBO and The Ring heavyweight titles 36 Loss 34–1–1 Oleksandr Usyk SD 12 18 May 2024 Kingdom Arena, Riyadh, Saudi Arabia Lost WBC heavyweight title; For WBA (Super), IBF, WBO, IBO, and The Ring heavyweight titles 35 Win 34–0–1 Francis\n\n[DOC 3] Boxing match Tyson is Back! Promotional poster Date February 11, 1990 Venue Tokyo Dome , Tokyo , Japan Title(s) on the line WBA , WBC and IBF undisputed heavyweight championship Tale of the tape Boxer Mike Tyson James Douglas Nickname Iron Buster Hometown Catskill , New York , US Columbus, Ohio , US Purse $6,000,000 $1,300,000 Pre-fight record 37–0 (33 KO) 29–4–1 (1) (19 KO) Age 23 years, 7 months 29 years, 10 months Height 5 ft 10 in (178 cm) 6 ft 4 in (193 cm) Weight 220 + 1 ⁄ 2 lb (100 kg) 231 + 1 ⁄ 2 lb (105 kg) Style Orthodox Orthodox Recognition WBA , WBC and IBF undisputed Heavyweight Champion The Ring No. 1 Ranked Heavyweight The Ring No. 1 ranked pound-for-pound fighter IBF No. 2 Ranked Heavyweight WBC No. 3 Ranked Heavyweight WBA No. 4 Ranked Heavy\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"We have Mike Tyson's birth date and the date (and age) of his first loss, but we lack Tyson Fury's birth date and the date of his first loss, which are necessary to compute his age on that day and thus the age difference.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 967, + "osl": 584, + "total_tokens": 1551, + "latency_ms": 14978.4, + "tokens_per_second": 38.99 + }, + "context": { + "kept_docs_count": 3, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "ddd3133d-dad6-4235-a431-8717977bac42", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T02:59:56.161841Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What was the age difference between Mike Tyson and Tyson Fury on the respective days on which they lost their first ever fights? Represent the figure in years only.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American boxer and media personality (born 1966) Mike Tyson Tyson at GalaxyCon , 2023 Personal information Nicknames Iron Kid Dynamite The Baddest Man on the Planet Born Michael Gerard Tyson ( 1966-06-30 ) June 30, 1966 (age 59) Brooklyn , New York City, U. S. Height 5 ft 10 in (178 cm) Weight Heavyweight Spouses Robin Givens ( m. 1988; div. 1989) Monica Turner ( m. 1997; div. 2003) Lakiha Spicer ( m. 2009) Children 7 Website miketyson . com Signature Boxing career Reach 71 in (180 cm) Stance Orthodox Boxing record Total fights 59 Wins 50 Win by KO 44 Losses 7 No contests 2 Medal record Men's amateur boxing National Junior Olympics 1981 North Carolina Heavyweight 1982 Tennessee Heavyweight Golden Gloves 1984 New York Heavyweight Michael Gerard Tyson (born Ju\n\n[DOC 2] hought of as a bum.\" Since his return from his hiatus, Fury has stated that he still feels bias against his community. Fury is a fan of Manchester United and attends football matches at their home ground Old Trafford . He also supports the England national team . Professional boxing record 37 fights 34 wins 2 losses By knockout 24 0 By decision 10 2 Draws 1 No. Result Record Opponent Type Round, time Date Location Notes 37 Loss 34–2–1 Oleksandr Usyk UD 12 21 Dec 2024 Kingdom Arena, Riyadh, Saudi Arabia For WBA (Super), WBC, WBO, IBO and The Ring heavyweight titles 36 Loss 34–1–1 Oleksandr Usyk SD 12 18 May 2024 Kingdom Arena, Riyadh, Saudi Arabia Lost WBC heavyweight title; For WBA (Super), IBF, WBO, IBO, and The Ring heavyweight titles 35 Win 34–0–1 Francis\n\n[DOC 3] Boxing match Tyson is Back! Promotional poster Date February 11, 1990 Venue Tokyo Dome , Tokyo , Japan Title(s) on the line WBA , WBC and IBF undisputed heavyweight championship Tale of the tape Boxer Mike Tyson James Douglas Nickname Iron Buster Hometown Catskill , New York , US Columbus, Ohio , US Purse $6,000,000 $1,300,000 Pre-fight record 37–0 (33 KO) 29–4–1 (1) (19 KO) Age 23 years, 7 months 29 years, 10 months Height 5 ft 10 in (178 cm) 6 ft 4 in (193 cm) Weight 220 + 1 ⁄ 2 lb (100 kg) 231 + 1 ⁄ 2 lb (105 kg) Style Orthodox Orthodox Recognition WBA , WBC and IBF undisputed Heavyweight Champion The Ring No. 1 Ranked Heavyweight The Ring No. 1 ranked pound-for-pound fighter IBF No. 2 Ranked Heavyweight WBC No. 3 Ranked Heavyweight WBA No. 4 Ranked Heavy\n\n\nSEARCH HISTORY: - Query: 'Mike Tyson birthdate' → 1 docs\n- Query: 'Mike Tyson first loss' → 0 docs\n- Query: 'Tyson Fury first loss' → 1 docs\n- Query: 'Mike Tyson vs Buster Douglas date' → 1 docs\n- Query: 'Tyson Fury vs Wladimir Klitschko date' → 1 docs\n- Query: 'Tyson Fury birthdate' → 0 docs\n- Query: 'Tyson Fury' → 0 docs\n- Query: 'Tyson Fury vs Wladimir Klitschko date' → 0 docs\n- Query: 'Mike Tyson vs Buster Douglas date' → 0 docs\n\nFEEDBACK: Need birthdate and date of first professional loss for both boxers to compute ages on those days.\nMissing: exact dates of Mike Tyson's first professional loss and Tyson Fury's first professional loss, plus Tyson Fury's birthdate. The previous query for Mike Tyson's first loss returned no results, so the new query targets the specific bout. The Fury loss query targets his bout with Klitschko to obtain the date. The birthdate query will provide Fury's age for the calculation.\nMissing: Tyson Fury's birthdate (to compute age on his first loss) and the exact dates of each boxer's first professional loss. The first query gets Fury's full biography (including birthdate). The second retrieves the date of Fury's first loss to Klitschko. The third ensures we have the date of Tyson's first loss to Buster Douglas.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Tyson vs Douglas 1990\",\n \"Tyson Fury\",\n \"Fury vs Klitschko\"\n ],\n \"feedback\": \"Missing: Mike Tyson's exact first loss date, Tyson Fury's birthdate, and Tyson Fury's first loss date. These queries target the specific bouts and a general biography for Fury to retrieve the needed dates.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1384, + "osl": 82, + "total_tokens": 1466, + "latency_ms": 7956.34, + "tokens_per_second": 10.31 + }, + "context": { + "kept_docs_count": 3, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "41fbd462-9118-42cc-bb79-b3742ae3d64e", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T02:59:59.346033Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: What was the age difference between Mike Tyson and Tyson Fury on the respective days on which they lost their first ever fights? Represent the figure in years only.\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] ted: \"Foreman Ranks Wilder-Fury in Top Three Rematches of All Time,\" February 15, 2020, Boxing Scene, retrieved November 22, 2020: \"'There were three important rematches in boxing history,' Foreman told BoxingScene ... of the heavyweight division. 'The most important was Max Schmeling and Joe Louis when the whole world was watching. That was the most important rematch of all time.'\" ^ a b Erenberg, Lewis A.: The greatest fight of our generation: Louis vs. Schmeling, 2005–2008, as summarized at Oxford University Press [1] , with online copy (subscription required): \"... the second Louis-Schmeling fight sparked excitement around the globe. For all its length–the fight lasted but two minutes–it remains one of the most memorable events in boxing history and, ind\n\n\nKEPT DOCUMENTS (context only):\n[3 documents already kept as relevant]\n[KEPT 1] American boxer and media personality (born 1966) Mike Tyson Tyson at GalaxyCon , 2023 Personal information Nicknames Iron Kid Dynamite The Baddest Man on the Planet Born Michael Gerard Tyson ( 1966-06-30 ) June 30, 1966 (age 59) Brooklyn , New York City, U. S. Height 5 ft 10 in (178 cm) Weight Heavy...\n[KEPT 2] hought of as a bum.\" Since his return from his hiatus, Fury has stated that he still feels bias against his community. Fury is a fan of Manchester United and attends football matches at their home ground Old Trafford . He also supports the England national team . Professional boxing record 37 fights...\n[KEPT 3] Boxing match Tyson is Back! Promotional poster Date February 11, 1990 Venue Tokyo Dome , Tokyo , Japan Title(s) on the line WBA , WBC and IBF undisputed heavyweight championship Tale of the tape Boxer Mike Tyson James Douglas Nickname Iron Buster Hometown Catskill , New York , US Columbus, Ohio , US...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 687, + "osl": 462, + "total_tokens": 1149, + "latency_ms": 2591.6, + "tokens_per_second": 178.27 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 3 + } + }, + { + "call_id": "662a5cf5-3d9f-4561-9464-7c8ef5d4ac39", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:00:02.011839Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What was the age difference between Mike Tyson and Tyson Fury on the respective days on which they lost their first ever fights? Represent the figure in years only.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American boxer and media personality (born 1966) Mike Tyson Tyson at GalaxyCon , 2023 Personal information Nicknames Iron Kid Dynamite The Baddest Man on the Planet Born Michael Gerard Tyson ( 1966-06-30 ) June 30, 1966 (age 59) Brooklyn , New York City, U. S. Height 5 ft 10 in (178 cm) Weight Heavyweight Spouses Robin Givens ( m. 1988; div. 1989) Monica Turner ( m. 1997; div. 2003) Lakiha Spicer ( m. 2009) Children 7 Website miketyson . com Signature Boxing career Reach 71 in (180 cm) Stance Orthodox Boxing record Total fights 59 Wins 50 Win by KO 44 Losses 7 No contests 2 Medal record Men's amateur boxing National Junior Olympics 1981 North Carolina Heavyweight 1982 Tennessee Heavyweight Golden Gloves 1984 New York Heavyweight Michael Gerard Tyson (born Ju\n\n[DOC 2] hought of as a bum.\" Since his return from his hiatus, Fury has stated that he still feels bias against his community. Fury is a fan of Manchester United and attends football matches at their home ground Old Trafford . He also supports the England national team . Professional boxing record 37 fights 34 wins 2 losses By knockout 24 0 By decision 10 2 Draws 1 No. Result Record Opponent Type Round, time Date Location Notes 37 Loss 34–2–1 Oleksandr Usyk UD 12 21 Dec 2024 Kingdom Arena, Riyadh, Saudi Arabia For WBA (Super), WBC, WBO, IBO and The Ring heavyweight titles 36 Loss 34–1–1 Oleksandr Usyk SD 12 18 May 2024 Kingdom Arena, Riyadh, Saudi Arabia Lost WBC heavyweight title; For WBA (Super), IBF, WBO, IBO, and The Ring heavyweight titles 35 Win 34–0–1 Francis\n\n[DOC 3] Boxing match Tyson is Back! Promotional poster Date February 11, 1990 Venue Tokyo Dome , Tokyo , Japan Title(s) on the line WBA , WBC and IBF undisputed heavyweight championship Tale of the tape Boxer Mike Tyson James Douglas Nickname Iron Buster Hometown Catskill , New York , US Columbus, Ohio , US Purse $6,000,000 $1,300,000 Pre-fight record 37–0 (33 KO) 29–4–1 (1) (19 KO) Age 23 years, 7 months 29 years, 10 months Height 5 ft 10 in (178 cm) 6 ft 4 in (193 cm) Weight 220 + 1 ⁄ 2 lb (100 kg) 231 + 1 ⁄ 2 lb (105 kg) Style Orthodox Orthodox Recognition WBA , WBC and IBF undisputed Heavyweight Champion The Ring No. 1 Ranked Heavyweight The Ring No. 1 ranked pound-for-pound fighter IBF No. 2 Ranked Heavyweight WBC No. 3 Ranked Heavyweight WBA No. 4 Ranked Heavy\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide Mike Tyson's birthdate (June 30, 1966) and his age at his first loss (23 years, 7 months on February 11, 1990). However, there is no information about Tyson Fury's birthdate or the date of his first professional loss, so the required age for Fury cannot be determined. Therefore, the answer cannot be computed with the available information.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 991, + "osl": 460, + "total_tokens": 1451, + "latency_ms": 2665.17, + "tokens_per_second": 172.6 + }, + "context": { + "kept_docs_count": 3, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "754d69a2-f543-4bf8-aedc-5d0138feb588", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:00:09.214050Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: What was the age difference between Mike Tyson and Tyson Fury on the respective days on which they lost their first ever fights? Represent the figure in years only.\n\nDOCUMENTS:\n\n[DOC 1] American boxer and media personality (born 1966) Mike Tyson Tyson at GalaxyCon , 2023 Personal information Nicknames Iron Kid Dynamite The Baddest Man on the Planet Born Michael Gerard Tyson ( 1966-06-30 ) June 30, 1966 (age 59) Brooklyn , New York City, U. S. Height 5 ft 10 in (178 cm) Weight Heavyweight Spouses Robin Givens ( m. 1988; div. 1989) Monica Turner ( m. 1997; div. 2003) Lakiha Spicer ( m. 2009) Children 7 Website miketyson . com Signature Boxing career Reach 71 in (180 cm) Stance Orthodox Boxing record Total fights 59 Wins 50 Win by KO 44 Losses 7 No contests 2 Medal record Men's amateur boxing National Junior Olympics 1981 North Carolina Heavyweight 1982 Tennessee Heavyweight Golden Gloves 1984 New York Heavyweight Michael Gerard Tyson (born Ju\n\n[DOC 2] hought of as a bum.\" Since his return from his hiatus, Fury has stated that he still feels bias against his community. Fury is a fan of Manchester United and attends football matches at their home ground Old Trafford . He also supports the England national team . Professional boxing record 37 fights 34 wins 2 losses By knockout 24 0 By decision 10 2 Draws 1 No. Result Record Opponent Type Round, time Date Location Notes 37 Loss 34–2–1 Oleksandr Usyk UD 12 21 Dec 2024 Kingdom Arena, Riyadh, Saudi Arabia For WBA (Super), WBC, WBO, IBO and The Ring heavyweight titles 36 Loss 34–1–1 Oleksandr Usyk SD 12 18 May 2024 Kingdom Arena, Riyadh, Saudi Arabia Lost WBC heavyweight title; For WBA (Super), IBF, WBO, IBO, and The Ring heavyweight titles 35 Win 34–0–1 Francis\n\n[DOC 3] Boxing match Tyson is Back! Promotional poster Date February 11, 1990 Venue Tokyo Dome , Tokyo , Japan Title(s) on the line WBA , WBC and IBF undisputed heavyweight championship Tale of the tape Boxer Mike Tyson James Douglas Nickname Iron Buster Hometown Catskill , New York , US Columbus, Ohio , US Purse $6,000,000 $1,300,000 Pre-fight record 37–0 (33 KO) 29–4–1 (1) (19 KO) Age 23 years, 7 months 29 years, 10 months Height 5 ft 10 in (178 cm) 6 ft 4 in (193 cm) Weight 220 + 1 ⁄ 2 lb (100 kg) 231 + 1 ⁄ 2 lb (105 kg) Style Orthodox Orthodox Recognition WBA , WBC and IBF undisputed Heavyweight Champion The Ring No. 1 Ranked Heavyweight The Ring No. 1 ranked pound-for-pound fighter IBF No. 2 Ranked Heavyweight WBC No. 3 Ranked Heavyweight WBA No. 4 Ranked Heavy\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 821, + "osl": 306, + "total_tokens": 1127, + "latency_ms": 7201.55, + "tokens_per_second": 42.49 + }, + "context": { + "num_documents_used": 3, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:00:09.214841Z", + "summary": { + "total_llm_calls": 12, + "total_hops": 5, + "total_input_tokens": 10721, + "total_output_tokens": 4458, + "total_tokens": 15179, + "total_latency_ms": 70556.35, + "average_tokens_per_second": 97.14, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Tyson_Fury", + "https://en.wikipedia.org/wiki/Mike_Tyson", + "https://en.wikipedia.org/wiki/Mike_Tyson_vs._Buster_Douglas", + "https://en.wikipedia.org/wiki/Tyson_Fury_vs_Oleksandr_Usyk" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "12 years." + } + }, + { + "query_id": "26", + "original_query": "Using the Pruett rule, out of all of the blue moons that occurred between the beginning of World War I and the end of World War II, how many of them occurred on the 31st of the month?", + "timestamp_start": "2026-05-16T03:00:09.237305Z", + "llm_calls": [ + { + "call_id": "ea7654c5-8916-4f16-9f36-c6703f93256f", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:00:14.225934Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: Using the Pruett rule, out of all of the blue moons that occurred between the beginning of World War I and the end of World War II, how many of them occurred on the 31st of the month?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Pruett rule blue moon\",\n \"blue moons 1914 1945\",\n \"blue moon 31st\"\n ],\n \"feedback\": \"Need to understand the Pruett rule and obtain a list of blue moons between July 1914 and September 1945, then identify which of those fell on the 31st of a month.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 480, + "osl": 527, + "total_tokens": 1007, + "latency_ms": 4988.46, + "tokens_per_second": 105.64 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "e80d3258-1d74-420b-a9e7-8772a3ee607f", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:00:17.671040Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: Using the Pruett rule, out of all of the blue moons that occurred between the beginning of World War I and the end of World War II, how many of them occurred on the 31st of the month?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] e per year. Two Pruett blue moons can occur in a single year (1915, 1961, 1999, 2018, 2037, 2094). 1915 had four blue moons (two Pruett, one Maine, one astro-seasonal). 1934 and 2048 have three (one of each type). Despite the 187 blue moons appearing across the 200 years in this table, only 146 years have any of these 3 types of blue moons, leaving 54 years (thus averaging just over 1 year in every 4) which have none of the 3 rules represented in that calendar year. While not totally unexpected (given the overlapping frequencies of these 3 rules), it so happens there are not any 2 sequential years (at least within these 200) wherein none of the 3 types of blue moon occur. Conversely, despite the preponderance of years with blue moons (of at least 1 type) occ\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 488, + "osl": 619, + "total_tokens": 1107, + "latency_ms": 2835.88, + "tokens_per_second": 218.27 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "bf4345e2-42c4-415f-8bca-e07351464375", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:00:22.893336Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: Using the Pruett rule, out of all of the blue moons that occurred between the beginning of World War I and the end of World War II, how many of them occurred on the 31st of the month?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] e per year. Two Pruett blue moons can occur in a single year (1915, 1961, 1999, 2018, 2037, 2094). 1915 had four blue moons (two Pruett, one Maine, one astro-seasonal). 1934 and 2048 have three (one of each type). Despite the 187 blue moons appearing across the 200 years in this table, only 146 years have any of these 3 types of blue moons, leaving 54 years (thus averaging just over 1 year in every 4) which have none of the 3 rules represented in that calendar year. While not totally unexpected (given the overlapping frequencies of these 3 rules), it so happens there are not any 2 sequential years (at least within these 200) wherein none of the 3 types of blue moon occur. Conversely, despite the preponderance of years with blue moons (of at least 1 type) occ\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept document only lists years that had Pruett blue moons (e.g., 1915) and notes that 1915 had two Pruett blue moons, but it does not provide the exact dates of those blue moons. To answer the question we need the specific calendar dates of all Pruett blue moons between 1914‑07‑28 and 1945‑09‑02, and then determine which of those dates fell on the 31st of a month. This date-level information is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 465, + "osl": 118, + "total_tokens": 583, + "latency_ms": 5221.69, + "tokens_per_second": 22.6 + }, + "context": { + "kept_docs_count": 1, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "594ff562-0425-4ede-9467-97f2ec75c4f2", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:00:36.268677Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: Using the Pruett rule, out of all of the blue moons that occurred between the beginning of World War I and the end of World War II, how many of them occurred on the 31st of the month?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] e per year. Two Pruett blue moons can occur in a single year (1915, 1961, 1999, 2018, 2037, 2094). 1915 had four blue moons (two Pruett, one Maine, one astro-seasonal). 1934 and 2048 have three (one of each type). Despite the 187 blue moons appearing across the 200 years in this table, only 146 years have any of these 3 types of blue moons, leaving 54 years (thus averaging just over 1 year in every 4) which have none of the 3 rules represented in that calendar year. While not totally unexpected (given the overlapping frequencies of these 3 rules), it so happens there are not any 2 sequential years (at least within these 200) wherein none of the 3 types of blue moon occur. Conversely, despite the preponderance of years with blue moons (of at least 1 type) occ\n\n\nSEARCH HISTORY: - Query: 'Pruett rule blue moon' → 1 docs\n- Query: 'blue moons 1914 1945' → 0 docs\n- Query: 'blue moon 31st' → 0 docs\n\nFEEDBACK: Need to understand the Pruett rule and obtain a list of blue moons between July 1914 and September 1945, then identify which of those fell on the 31st of a month.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Pruett blue moon list\",\n \"blue moon dates 1914 1945\",\n \"blue moon 31st day\"\n ],\n \"feedback\": \"We need a comprehensive list of Pruett‑rule blue moons between July 1914 and September 1945, then identify which of those occurred on the 31st of a month. The first query seeks the specific Pruett‑rule dates, the second obtains any blue‑moon dates in the war‑time span, and the third isolates occurrences that fall on the 31st.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 754, + "osl": 604, + "total_tokens": 1358, + "latency_ms": 13374.72, + "tokens_per_second": 45.16 + }, + "context": { + "kept_docs_count": 1, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "c7984ea0-f2b2-43c4-ae43-2adbbaeed3c8", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:00:42.085568Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: Using the Pruett rule, out of all of the blue moons that occurred between the beginning of World War I and the end of World War II, how many of them occurred on the 31st of the month?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] e per year. Two Pruett blue moons can occur in a single year (1915, 1961, 1999, 2018, 2037, 2094). 1915 had four blue moons (two Pruett, one Maine, one astro-seasonal). 1934 and 2048 have three (one of each type). Despite the 187 blue moons appearing across the 200 years in this table, only 146 years have any of these 3 types of blue moons, leaving 54 years (thus averaging just over 1 year in every 4) which have none of the 3 rules represented in that calendar year. While not totally unexpected (given the overlapping frequencies of these 3 rules), it so happens there are not any 2 sequential years (at least within these 200) wherein none of the 3 types of blue moon occur. Conversely, despite the preponderance of years with blue moons (of at least 1 type) occ\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept document only provides a few years (e.g., 1915, 1934) that had Pruett blue moons and mentions the number of Pruett blue moons in those years, but it does not list the actual dates of those blue moons, nor does it specify which, if any, fell on the 31st of a month. To answer the question we would need a complete list of Pruett blue moon dates between July 1914 and September 1945 and the day‑of‑month for each, which is not present in the available documents.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 530, + "osl": 473, + "total_tokens": 1003, + "latency_ms": 5223.02, + "tokens_per_second": 90.56 + }, + "context": { + "kept_docs_count": 1, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "c71b4097-2b64-42f9-b776-393c3255c3fc", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:00:46.153260Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: Using the Pruett rule, out of all of the blue moons that occurred between the beginning of World War I and the end of World War II, how many of them occurred on the 31st of the month?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] e per year. Two Pruett blue moons can occur in a single year (1915, 1961, 1999, 2018, 2037, 2094). 1915 had four blue moons (two Pruett, one Maine, one astro-seasonal). 1934 and 2048 have three (one of each type). Despite the 187 blue moons appearing across the 200 years in this table, only 146 years have any of these 3 types of blue moons, leaving 54 years (thus averaging just over 1 year in every 4) which have none of the 3 rules represented in that calendar year. While not totally unexpected (given the overlapping frequencies of these 3 rules), it so happens there are not any 2 sequential years (at least within these 200) wherein none of the 3 types of blue moon occur. Conversely, despite the preponderance of years with blue moons (of at least 1 type) occ\n\n\nSEARCH HISTORY: - Query: 'Pruett rule blue moon' → 1 docs\n- Query: 'blue moons 1914 1945' → 0 docs\n- Query: 'blue moon 31st' → 0 docs\n- Query: 'Pruett blue moon list' → 0 docs\n- Query: 'blue moon dates 1914 1945' → 0 docs\n- Query: 'blue moon 31st day' → 0 docs\n\nFEEDBACK: Need to understand the Pruett rule and obtain a list of blue moons between July 1914 and September 1945, then identify which of those fell on the 31st of a month.\nWe need a comprehensive list of Pruett‑rule blue moons between July 1914 and September 1945, then identify which of those occurred on the 31st of a month. The first query seeks the specific Pruett‑rule dates, the second obtains any blue‑moon dates in the war‑time span, and the third isolates occurrences that fall on the 31st.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Pruett blue moon definition\",\n \"list of Pruett blue moons 1914-1945\",\n \"full moon dates 31st 1914 1945\"\n ],\n \"feedback\": \"We need the exact dates of blue moons that follow the Pruett rule between July 1914 and September 1945, then determine which of those occurred on the 31st. The first query gets the rule definition, the second aims for a compiled list of Pruett‑rule blue moons in the war‑time period, and the third directly searches for full moons that fell on the 31st within that span, which can be cross‑referenced to identify the relevant blue moons.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 909, + "osl": 1087, + "total_tokens": 1996, + "latency_ms": 4066.84, + "tokens_per_second": 267.28 + }, + "context": { + "kept_docs_count": 1, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "1072dd15-9282-48b8-907e-0be0cb05f93b", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:00:52.169035Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: Using the Pruett rule, out of all of the blue moons that occurred between the beginning of World War I and the end of World War II, how many of them occurred on the 31st of the month?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] e per year. Two Pruett blue moons can occur in a single year (1915, 1961, 1999, 2018, 2037, 2094). 1915 had four blue moons (two Pruett, one Maine, one astro-seasonal). 1934 and 2048 have three (one of each type). Despite the 187 blue moons appearing across the 200 years in this table, only 146 years have any of these 3 types of blue moons, leaving 54 years (thus averaging just over 1 year in every 4) which have none of the 3 rules represented in that calendar year. While not totally unexpected (given the overlapping frequencies of these 3 rules), it so happens there are not any 2 sequential years (at least within these 200) wherein none of the 3 types of blue moon occur. Conversely, despite the preponderance of years with blue moons (of at least 1 type) occ\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept document only lists years that have Pruett blue moons (e.g., 1915, 1934) and some aggregate counts, but it does not provide the specific dates of those blue moons. To answer the question we need the exact dates of each Pruett blue moon between July 1914 and September 1945 and then count how many fall on the 31st of a month. This detailed date information is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 465, + "osl": 106, + "total_tokens": 571, + "latency_ms": 5417.37, + "tokens_per_second": 19.57 + }, + "context": { + "kept_docs_count": 1, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "d286a12f-3f20-4e82-9434-e5528b33a9f8", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:00:55.439118Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: Using the Pruett rule, out of all of the blue moons that occurred between the beginning of World War I and the end of World War II, how many of them occurred on the 31st of the month?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] e per year. Two Pruett blue moons can occur in a single year (1915, 1961, 1999, 2018, 2037, 2094). 1915 had four blue moons (two Pruett, one Maine, one astro-seasonal). 1934 and 2048 have three (one of each type). Despite the 187 blue moons appearing across the 200 years in this table, only 146 years have any of these 3 types of blue moons, leaving 54 years (thus averaging just over 1 year in every 4) which have none of the 3 rules represented in that calendar year. While not totally unexpected (given the overlapping frequencies of these 3 rules), it so happens there are not any 2 sequential years (at least within these 200) wherein none of the 3 types of blue moon occur. Conversely, despite the preponderance of years with blue moons (of at least 1 type) occ\n\n\nSEARCH HISTORY: - Query: 'Pruett rule blue moon' → 1 docs\n- Query: 'blue moons 1914 1945' → 0 docs\n- Query: 'blue moon 31st' → 0 docs\n- Query: 'Pruett blue moon list' → 0 docs\n- Query: 'blue moon dates 1914 1945' → 0 docs\n- Query: 'blue moon 31st day' → 0 docs\n- Query: 'Pruett blue moon definition' → 0 docs\n- Query: 'list of Pruett blue moons 1914-1945' → 0 docs\n- Query: 'full moon dates 31st 1914 1945' → 0 docs\n\nFEEDBACK: Need to understand the Pruett rule and obtain a list of blue moons between July 1914 and September 1945, then identify which of those fell on the 31st of a month.\nWe need a comprehensive list of Pruett‑rule blue moons between July 1914 and September 1945, then identify which of those occurred on the 31st of a month. The first query seeks the specific Pruett‑rule dates, the second obtains any blue‑moon dates in the war‑time span, and the third isolates occurrences that fall on the 31st.\nWe need the exact dates of blue moons that follow the Pruett rule between July 1914 and September 1945, then determine which of those occurred on the 31st. The first query gets the rule definition, the second aims for a compiled list of Pruett‑rule blue moons in the war‑time period, and the third directly searches for full moons that fell on the 31st within that span, which can be cross‑referenced to identify the relevant blue moons.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Blue moon list\",\n \"Pruett rule blue moon dates\",\n \"Full moon 31st list\"\n ],\n \"feedback\": \"We need a comprehensive list of Pruett‑rule blue moons (to get the dates) and a source that enumerates full moons occurring on the 31st of any month. The first query should retrieve the Wikipedia/astronomy tables of all blue moons. The second adds the specific “Pruett rule” filter to isolate the relevant subset. The third seeks any compiled list of full moons that fell on the 31st, which can be cross‑referenced with the Pruett dates to answer the question.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1060, + "osl": 655, + "total_tokens": 1715, + "latency_ms": 3269.28, + "tokens_per_second": 200.35 + }, + "context": { + "kept_docs_count": 1, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "e84b18c1-f369-4167-89f4-9a014aed98a7", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T03:00:59.361596Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: Using the Pruett rule, out of all of the blue moons that occurred between the beginning of World War I and the end of World War II, how many of them occurred on the 31st of the month?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] list\n\n[NEW 2] : authors list ( link )\n\n[NEW 3] Greener Jack Warden Shampoo Lester Carp 1976 ( 30th ) Brad Dourif One Flew Over the Cuckoo's Nest Billy Bibbit Martin Balsam All the President's Men Howard Simons Michael Hordern The Slipper and the Rose The King Jason Robards † All the President's Men Ben Bradlee 1977 ( 31st ) Edward Fox A Bridge Too Far Brian Horrocks Colin Blakely Equus Frank Strang Robert Duvall Network Frank Hackett Zero Mostel The Front Hecky Brown 1978 ( 32nd ) John Hurt Midnight Express Max ' Gene Hackman Superman Lex Luthor Jason Robards † Julia Dashiell Hammett François Truffaut Close Encounters of the Third Kind Claude Lacombe 1979 ( 33rd ) Robert Duvall Apocalypse Now Bill Kilgore Denholm Elliott Saint Jack William Leigh John Hurt Alien Kane Christopher Walken † The Deer Hunter N\n\n[NEW 4] th Fri 30th Sat 1st Sun Ceremonies OC CC — N/a Aquatics Diving ● 2 1 ● 1 ● 3 ● 1 44 Swimming 4 4 4 4 4 4 4 4 Synchronised swimming ● ● 1 ● 1 Water polo ● ● ● ● ● ● 1 ● ● ● ● ● ● 1 Archery ● ● ● 1 1 1 1 4 Athletics 2 3 5 9 7 6 5 8 1 46 Badminton ● ● ● ● 2 1 2 5 Baseball/Softball Baseball ● ● ● ● ● ● ● ● 1 2 Softball ● ● ● ● ● ● ● ● 1 Basketball ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1 1 2 Boxing ● ● ● ● ● ● ● ● ● ● ● ● ● 6 6 12 Canoeing Slalom ● 2 ● 2 16 Sprint ● ● ● ● 6 6 Cycling Road cycling 1 1 2 18 Track cycling 2 2 1 1 3 3 Mountain biking 1 1 Equestrian ● ● 1 ● ● 1 ● 1 1 ● 1 1 6 Fencing 1 1 1 1 1 2 1 1 1 10 Field hockey ● ● ● ● ● ● ● ● ● ● ● ● ● 1 1 2 Football ● ● ● ● ● ● ● ● ● ● 1 ● 1 2 Gymnastics Artistic ● ● 1 1 1 1 5 5 18 Rhythmic ● ● 1 1 Trampolining 1 1 Handb\n\n[NEW 5] ♍︎ ♍️ U+264E ♎ LIBRA ♎︎ ♎️ U+264F ♏ SCORPIUS ♏︎ ♏️ U+2650 ♐ SAGITTARIUS ♐︎ ♐️ U+2651 ♑ CAPRICORN ♑︎ ♑️ U+2652 ♒ AQUARIUS ♒︎ ♒️ U+2653 ♓ PISCES ♓︎ ♓️ U+26CE ⛎ OPHIUCHUS ⛎︎ ⛎️ See also Astronomy portal Astronomical symbols – Symbols in astronomy Chinese zodiac – Lunar calendar classification in a 12-year cycle Circle of stars – Symbolic motif Cusp (astrology) – Imaginary line that separates a pair of consecutive astrological signs in the zodiac Elements of the zodiac – Elements in zodiac signs Pages displaying short descriptions of redirect targets History of astrology Jewish astrology Mazzaroth – Biblical term for the Zodiac References ^ ^ Because the signs are each 30° in longitude but constellations have irregular shapes, and because of precession, they do\n\n[NEW 6] uly 90° to 119°59\" 4 Negative Water Cardinal Summer Winter Moon Leo The Lion ♌︎ 24 July 23 August 120° to 149°59\" 5 Positive Fire Fixed Summer Winter Sun Virgo The Maiden , Virgin ♍︎ 24 August 23 September 150° to 179°59\" 6 Negative Earth Mutable Summer Winter Ceres Mercury Libra The Scales ♎︎ 24 September 23 October 180° to 209°59\" 7 Positive Air Cardinal Autumn Spring Eris Venus Scorpio The Scorpion ♏︎ 24 October 22 November 210° to 239°59\" 8 Negative Water Fixed Autumn Spring Pluto Mars Sagittarius The Archer ( Centaur ) ♐︎ 23 November 21 December 240° to 269°59\" 9 Positive Fire Mutable Autumn Spring Jupiter Capricorn The Goat ♑︎ 22 December 20 January 270° to 299°59\" 10 Negative Earth Cardinal Winter Summer Saturn Aquarius The Water-bearer ♒︎ 21 January\n\n\nKEPT DOCUMENTS (context only):\n[1 documents already kept as relevant]\n[KEPT 1] e per year. Two Pruett blue moons can occur in a single year (1915, 1961, 1999, 2018, 2037, 2094). 1915 had four blue moons (two Pruett, one Maine, one astro-seasonal). 1934 and 2048 have three (one of each type). Despite the 187 blue moons appearing across the 200 years in this table, only 146 year...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 6 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1446, + "osl": 430, + "total_tokens": 1876, + "latency_ms": 3310.7, + "tokens_per_second": 129.88 + }, + "context": { + "num_documents_evaluated": 6, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 1 + } + }, + { + "call_id": "582adb9d-d62e-4f8d-befb-9d24af6634b8", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:01:10.083381Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: Using the Pruett rule, out of all of the blue moons that occurred between the beginning of World War I and the end of World War II, how many of them occurred on the 31st of the month?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] e per year. Two Pruett blue moons can occur in a single year (1915, 1961, 1999, 2018, 2037, 2094). 1915 had four blue moons (two Pruett, one Maine, one astro-seasonal). 1934 and 2048 have three (one of each type). Despite the 187 blue moons appearing across the 200 years in this table, only 146 years have any of these 3 types of blue moons, leaving 54 years (thus averaging just over 1 year in every 4) which have none of the 3 rules represented in that calendar year. While not totally unexpected (given the overlapping frequencies of these 3 rules), it so happens there are not any 2 sequential years (at least within these 200) wherein none of the 3 types of blue moon occur. Conversely, despite the preponderance of years with blue moons (of at least 1 type) occ\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The retained document lists years that had Pruett blue moons (e.g., 1915) but does not provide the specific dates of those blue moons. Without knowing on which calendar days (especially whether any fell on the 31st) the blue moons occurred between 1914‑07‑28 and 1945‑09‑02, we cannot determine the count asked for.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 515, + "osl": 414, + "total_tokens": 929, + "latency_ms": 10720.93, + "tokens_per_second": 38.62 + }, + "context": { + "kept_docs_count": 1, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "4662b21a-9c74-43b2-83f9-5127d414486f", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:01:11.043222Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: Using the Pruett rule, out of all of the blue moons that occurred between the beginning of World War I and the end of World War II, how many of them occurred on the 31st of the month?\n\nDOCUMENTS:\n\n[DOC 1] e per year. Two Pruett blue moons can occur in a single year (1915, 1961, 1999, 2018, 2037, 2094). 1915 had four blue moons (two Pruett, one Maine, one astro-seasonal). 1934 and 2048 have three (one of each type). Despite the 187 blue moons appearing across the 200 years in this table, only 146 years have any of these 3 types of blue moons, leaving 54 years (thus averaging just over 1 year in every 4) which have none of the 3 rules represented in that calendar year. While not totally unexpected (given the overlapping frequencies of these 3 rules), it so happens there are not any 2 sequential years (at least within these 200) wherein none of the 3 types of blue moon occur. Conversely, despite the preponderance of years with blue moons (of at least 1 type) occ\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 393, + "osl": 149, + "total_tokens": 542, + "latency_ms": 959.19, + "tokens_per_second": 155.34 + }, + "context": { + "num_documents_used": 1, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:01:11.043972Z", + "summary": { + "total_llm_calls": 11, + "total_hops": 5, + "total_input_tokens": 7505, + "total_output_tokens": 5182, + "total_tokens": 12687, + "total_latency_ms": 59388.08, + "average_tokens_per_second": 117.57, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Blue_moon#Blue_moon_dates", + "https://en.wikipedia.org/wiki/World_War_I#", + "https://en.wikipedia.org/wiki/World_War_II" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "9" + } + }, + { + "query_id": "27", + "original_query": "What number would Tommy Lawton have worn playing for Chelsea FC?", + "timestamp_start": "2026-05-16T03:01:11.066069Z", + "llm_calls": [ + { + "call_id": "807d37c3-2767-482a-a1a5-cd03910c62e8", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:01:13.089699Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What number would Tommy Lawton have worn playing for Chelsea FC?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Tommy Lawton jersey number\",\n \"Tommy Lawton Chelsea number\",\n \"Tommy Lawton shirt number\"\n ],\n \"feedback\": \"Need the specific squad number Tommy Lawton wore while at Chelsea FC. Initial broad queries will locate his player profile or historical squad lists.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 459, + "osl": 227, + "total_tokens": 686, + "latency_ms": 2023.49, + "tokens_per_second": 112.18 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "ae051154-978a-425c-9479-d9b65f8c7445", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:01:17.698201Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: What number would Tommy Lawton have worn playing for Chelsea FC?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] English association footballer (1919–1996) Tommy Lawton Lawton circa 1951 Personal information Full name Thomas Lawton Date of birth ( 1919-10-06 ) 6 October 1919 Place of birth Farnworth , England Date of death 6 November 1996 (1996-11-06) (aged 77) Place of death Nottingham , England Height 5 ft 11 in (1.80 m) Position Centre-forward Youth career 1933 Rossendale United 1933–1936 Burnley Senior career* Years Team Apps ( Gls ) 1936–1937 Burnley 25 (16) 1937–1945 Everton 87 (65) 1945–1947 Chelsea 42 (30) 1947–1952 Notts County 151 (90) 1952–1953 Brentford 50 (17) 1953–1955 Arsenal 35 (13) 1955–1956 Kettering Town 30 (15) Total 420 (246) International career 1939–19?? The Football League XI 3 (2) 1938–1948 England 23 (22) 1939–1946 → England (wartime) 23 (24)\n\n[NEW 2] 39 1941–1944 1946–1963 BBWAA 76.01% 1972 Ross Youngs RF New York Giants 1917–1926 VC — 1973 Roberto Clemente RF Pittsburgh Pirates 1955–1972 BBWAA 92.69% 1973 Billy Evans UMP — 1906–1927 VC — 1973 Monte Irvin LF Newark Eagles 1937–1942 1945–1956 NLC 75% 1973 George Kelly 1B New York Giants 1915–1917 1919–1930 1932 VC — 1973 Warren Spahn P Milwaukee Braves 1942 1946–1965 BBWAA 82.89% 1973 Mickey Welch P New York Giants 1880–1892 VC — 1974 Cool Papa Bell CF St. Louis Stars 1922–1938 1942 1947–1950 NLC 100% 1974 Jim Bottomley 1B St. Louis Cardinals 1922–1937 VC — 1974 Jocko Conlan UMP — 1941–1965 VC — 1974 Whitey Ford P New York Yankees 1950 1953–1967 BBWAA 77.81% 1974 Mickey Mantle CF New York Yankees 1951–1968 BBWAA 88.22% 1974 Sam Thompson RF Philadelphia Ph\n\n[NEW 3] rmon (c) 77-75-77-78=307 Jack Isaacs 76-79-76-76=307 Gene Sarazen (c) 79-75-78-75=307 T58 Don Cherry (a) 80-79-74-76=309 +21 0 Fred Hawkins 78-80-76-75=309 250 T60 Bill Ogden 81-76-77-76=310 +22 250 Johnny Revolta 81-79-75-75=310 Jim Turnesa 83-79-75-73=310 63 Robert Sweeny Jr. (a) 81-76-79-76=312 +24 0 T64 Jimmy Jackson (a) 75-82-79-78=314 +26 Bobby Kuntz (a) 80-77-82-75=314 66 Jim McHale Jr. (a) 78-82-79-76=315 +27 67 Don Albert (a) 77-79-83-77=316 +28 T68 Angelo Santilli (a) 79-81-82-75=317 +29 Denny Shute 73-85-82-77=317 250 70 Ray Palmer (a) 84-78-80-77=319 +31 0 71 Craig Wood (c) 87-80-78-75=320 +32 250 72 Sam Parks Jr. 86-79-79-80=324 +36 250 WD Dale Morey (a) 83-75-76=234 +18 Jim Ferrier 78-78=156 +12 Jess Sweetser (a) 79 +7 Chandler Harper 83 +11 Fr\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 3 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[1,0,0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1205, + "osl": 211, + "total_tokens": 1416, + "latency_ms": 3916.27, + "tokens_per_second": 53.88 + }, + "context": { + "num_documents_evaluated": 3, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "9065bea1-5cb8-4175-9d83-70250dd88d35", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:01:19.326495Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What number would Tommy Lawton have worn playing for Chelsea FC?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] English association footballer (1919–1996) Tommy Lawton Lawton circa 1951 Personal information Full name Thomas Lawton Date of birth ( 1919-10-06 ) 6 October 1919 Place of birth Farnworth , England Date of death 6 November 1996 (1996-11-06) (aged 77) Place of death Nottingham , England Height 5 ft 11 in (1.80 m) Position Centre-forward Youth career 1933 Rossendale United 1933–1936 Burnley Senior career* Years Team Apps ( Gls ) 1936–1937 Burnley 25 (16) 1937–1945 Everton 87 (65) 1945–1947 Chelsea 42 (30) 1947–1952 Notts County 151 (90) 1952–1953 Brentford 50 (17) 1953–1955 Arsenal 35 (13) 1955–1956 Kettering Town 30 (15) Total 420 (246) International career 1939–19?? The Football League XI 3 (2) 1938–1948 England 23 (22) 1939–1946 → England (wartime) 23 (24)\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept document provides Tommy Lawton's career details, including his years at Chelsea (1945–1947) and his position (centre‑forward), but it does not contain any information about the specific shirt number he wore while playing for Chelsea FC. The required fact (shirt number) is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 496, + "osl": 76, + "total_tokens": 572, + "latency_ms": 1627.47, + "tokens_per_second": 46.7 + }, + "context": { + "kept_docs_count": 1, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "50c769f0-3fac-4317-a500-aa4f7a596ae7", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:01:26.901269Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What number would Tommy Lawton have worn playing for Chelsea FC?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] English association footballer (1919–1996) Tommy Lawton Lawton circa 1951 Personal information Full name Thomas Lawton Date of birth ( 1919-10-06 ) 6 October 1919 Place of birth Farnworth , England Date of death 6 November 1996 (1996-11-06) (aged 77) Place of death Nottingham , England Height 5 ft 11 in (1.80 m) Position Centre-forward Youth career 1933 Rossendale United 1933–1936 Burnley Senior career* Years Team Apps ( Gls ) 1936–1937 Burnley 25 (16) 1937–1945 Everton 87 (65) 1945–1947 Chelsea 42 (30) 1947–1952 Notts County 151 (90) 1952–1953 Brentford 50 (17) 1953–1955 Arsenal 35 (13) 1955–1956 Kettering Town 30 (15) Total 420 (246) International career 1939–19?? The Football League XI 3 (2) 1938–1948 England 23 (22) 1939–1946 → England (wartime) 23 (24)\n\n\nSEARCH HISTORY: - Query: 'Tommy Lawton jersey number' → 3 docs\n- Query: 'Tommy Lawton Chelsea number' → 0 docs\n- Query: 'Tommy Lawton shirt number' → 0 docs\n\nFEEDBACK: Need the specific squad number Tommy Lawton wore while at Chelsea FC. Initial broad queries will locate his player profile or historical squad lists.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Tommy Lawton biography\",\n \"Chelsea 1945 squad\",\n \"Chelsea centre forward 1946\"\n ],\n \"feedback\": \"Previous attempts using 'jersey', 'shirt number', and 'Chelsea number' returned no results. The new queries broaden the search to a full biography and historic squad lists, avoiding the failing patterns while targeting the likely source of the squad number information.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 781, + "osl": 539, + "total_tokens": 1320, + "latency_ms": 7574.22, + "tokens_per_second": 71.16 + }, + "context": { + "kept_docs_count": 1, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "b17333eb-70c3-464b-8350-464825847061", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T03:01:42.328206Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: What number would Tommy Lawton have worn playing for Chelsea FC?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] from Chelsea ) 13 GK ESP Fran Vieites 14 FW JAM Bobby De Cordova-Reid 15 DF AUS Harry Souttar 16 DF DEN Victor Kristiansen 17 MF BAN Hamza Choudhury 18 MF NGA Joe Aribo (on loan from Southampton ) No. Pos. Nation Player 20 FW ZAM Patson Daka 21 DF POR Ricardo Pereira ( captain ) 22 MF ENG Oliver Skipp 23 DF DEN Jannik Vestergaard 24 DF ENG Jamaal Lascelles 25 MF ENG Louis Page 27 MF POR Wanya Marçal 28 FW ENG Jeremy Monga 29 MF ENG Divine Mukasa (on loan from Manchester City ) 30 MF ENG Aaron Ramsey (on loan from Burnley ) 31 GK BIH Asmir Begović 33 DF ENG Luke Thomas 34 MF ENG Michael Golding 39 MF ENG Silko Thomas 56 DF ENG Olabade Aluko Out on loan Note: Flags indicate national team as defined under FIFA eligibility rules ; some limited exceptions apply.\n\n[NEW 2] s 11 FW ENG Omari Patrick 12 GK ENG Jack Barrett (on loan from Blackburn Rovers ) 13 GK IRL Joe Murphy 14 FW ENG Jayden Joseph (on loan from Leicester City ) 15 DF ENG William Tamen (on loan from Everton ) 16 MF ENG Jason Lowe 17 FW Jersey Sol Solomon No. Pos. Nation Player 18 FW ENG Connor Jennings 21 MF WAL Josh Williams 22 MF IRL Lee O'Connor 23 DF SKN Ethan Bristow 24 MF ENG Billy Blacker (on loan from Sheffield United ) 25 MF ENG Lewis Warrington 26 FW ENG James Plant 27 FW ENG Dylan Jones (on loan from Norwich City ) 28 DF MDA Stephan Negru (on loan from Oxford United ) 29 FW ENG Joe Ironside 30 DF ENG Aaron McGowan 31 FW ENG Max Dickov (on loan from Mansfield Town ) 32 MF KEN Zech Obiero (on loan from Leyton Orient ) 41 MF ENG Kaiyne Woolery 42 MF LBR\n\n[NEW 3] f the club. The players were: Greg Abbott Bruce Bannister Sam Barkas Bobby Bauld Peter Beagrie Charlie Bicknell Robbie Blake Dicky Bond Irvine Boocock Tommy Cairns Bobby Campbell Robert Campbell Eddie Carr Trevor Cherry Joe Cooke Ian Cooper Terry Dolan Peter Downsborough Donald Duckett Lee Duxbury Roy Ellam Mark Ellis Dave Evans Jock Ewart Tommy Flockett Oscar Fox David Fretwell Allan Gilliver David Gray John Hall Tom Hallett John Hallows Bobby Ham Joe Hargreaves Derek Hawksworth John Hendrie George Hinsley Don Hutchins Gerry Ingram David Jackson Peter Jackson (born 1937) Peter Jackson (born 1961) Wayne Jacobs Paul Jewell Rod Johnson Chris Kamara Jimmy Lawlor Jamie Lawrence David Layne Ken Leek Peter Logan Stuart McCall Sean McCarthy John McCole Jimmy McDona\n\n[NEW 4] 5 0 1928 1931 William Hamilton FW 1 0 1885 1885 William Hamilton FW 1 0 1908 1908 Willoughby Hamilton FW 1 0 1885 1885 Harry Hampton DF 9 0 1911 1914 Jack Hanna GK 2 0 1912 1912 John Hanna FW 1 0 1899 1899 Dinny Hannon FW 6 1 1908 1913 Terry Harkin FW 5 2 1968 1970 Alfie Harland GK 1 0 1922 1922 John Harris DF 1 0 1921 1921 Val Harris FW 20 0 1906 1914 Martin Harvey DF 34 3 1961 1971 Jack Hastings DF 7 0 1882 1886 Sammy Hatton DF 2 0 1962 1962 Billy Hayes DF 4 0 1937 1938 Conor Hazard * GK 4 0 2018 2022 David Healy FW 95 36 2000 2013 Felix Healy MF 4 0 1982 1982 Danny Hegan MF 7 0 1969 1973 Jack Hehir GK 1 0 1910 1910 Jack Henderson GK 3 0 1885 1885 George Hewison DF 2 0 1885 1885 Colin Hill DF 27 1 1990 1998 Jimmy Hill FW 7 0 1959 1963 Ted Hinton GK 7 0 194\n\n[NEW 5] Sándor Kocsis 11 1958 Sweden Just Fontaine 13 Pelé 1962 Chile Flórián Albert Garrincha Vavá Valentin Ivanov Dražan Jerković Leonel Sánchez 4 Flórián Albert 1966 England Eusébio 9 Franz Beckenbauer 1970 Mexico Gerd Müller 10 Teófilo Cubillas Peru 1974 West Germany Grzegorz Lato 7 Władysław Żmuda West Germany 1978 Argentina Mario Kempes 6 Antonio Cabrini Argentina 1982 Spain Paolo Rossi Paolo Rossi 6 Manuel Amoros Brazil 1986 Mexico Diego Maradona Gary Lineker 6 Enzo Scifo Brazil 1990 Italy Salvatore Schillaci Salvatore Schillaci 6 Robert Prosinečki England 1994 United States Romário Oleg Salenko Hristo Stoichkov 6 Michel Preud'homme 2 Marc Overmars Brazil 1998 France Ronaldo Davor Šuker 6 Fabien Barthez 5 Michael Owen England France 2002 South Korea/Japan Ol\n\n\nKEPT DOCUMENTS (context only):\n[1 documents already kept as relevant]\n[KEPT 1] English association footballer (1919–1996) Tommy Lawton Lawton circa 1951 Personal information Full name Thomas Lawton Date of birth ( 1919-10-06 ) 6 October 1919 Place of birth Farnworth , England Date of death 6 November 1996 (1996-11-06) (aged 77) Place of death Nottingham , England Height 5 ft 1...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 5 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1541, + "osl": 1052, + "total_tokens": 2593, + "latency_ms": 14895.94, + "tokens_per_second": 70.62 + }, + "context": { + "num_documents_evaluated": 5, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 1 + } + }, + { + "call_id": "4fc208f7-61af-49d6-8dcc-70d136e23b73", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:01:44.455170Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What number would Tommy Lawton have worn playing for Chelsea FC?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] English association footballer (1919–1996) Tommy Lawton Lawton circa 1951 Personal information Full name Thomas Lawton Date of birth ( 1919-10-06 ) 6 October 1919 Place of birth Farnworth , England Date of death 6 November 1996 (1996-11-06) (aged 77) Place of death Nottingham , England Height 5 ft 11 in (1.80 m) Position Centre-forward Youth career 1933 Rossendale United 1933–1936 Burnley Senior career* Years Team Apps ( Gls ) 1936–1937 Burnley 25 (16) 1937–1945 Everton 87 (65) 1945–1947 Chelsea 42 (30) 1947–1952 Notts County 151 (90) 1952–1953 Brentford 50 (17) 1953–1955 Arsenal 35 (13) 1955–1956 Kettering Town 30 (15) Total 420 (246) International career 1939–19?? The Football League XI 3 (2) 1938–1948 England 23 (22) 1939–1946 → England (wartime) 23 (24)\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept document lists Tommy Lawton's clubs and years, including his time at Chelsea (1945–1947), but it does not provide any information about the jersey number he wore while playing for Chelsea. Therefore, the required fact is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 566, + "osl": 160, + "total_tokens": 726, + "latency_ms": 2125.97, + "tokens_per_second": 75.26 + }, + "context": { + "kept_docs_count": 1, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "e7c3c865-fe58-4428-9240-ae709f106cba", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:01:49.195024Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What number would Tommy Lawton have worn playing for Chelsea FC?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] English association footballer (1919–1996) Tommy Lawton Lawton circa 1951 Personal information Full name Thomas Lawton Date of birth ( 1919-10-06 ) 6 October 1919 Place of birth Farnworth , England Date of death 6 November 1996 (1996-11-06) (aged 77) Place of death Nottingham , England Height 5 ft 11 in (1.80 m) Position Centre-forward Youth career 1933 Rossendale United 1933–1936 Burnley Senior career* Years Team Apps ( Gls ) 1936–1937 Burnley 25 (16) 1937–1945 Everton 87 (65) 1945–1947 Chelsea 42 (30) 1947–1952 Notts County 151 (90) 1952–1953 Brentford 50 (17) 1953–1955 Arsenal 35 (13) 1955–1956 Kettering Town 30 (15) Total 420 (246) International career 1939–19?? The Football League XI 3 (2) 1938–1948 England 23 (22) 1939–1946 → England (wartime) 23 (24)\n\n\nSEARCH HISTORY: - Query: 'Tommy Lawton jersey number' → 3 docs\n- Query: 'Tommy Lawton Chelsea number' → 0 docs\n- Query: 'Tommy Lawton shirt number' → 0 docs\n- Query: 'Tommy Lawton biography' → 0 docs\n- Query: 'Chelsea 1945 squad' → 5 docs\n- Query: 'Chelsea centre forward 1946' → 0 docs\n\nFEEDBACK: Need the specific squad number Tommy Lawton wore while at Chelsea FC. Initial broad queries will locate his player profile or historical squad lists.\nPrevious attempts using 'jersey', 'shirt number', and 'Chelsea number' returned no results. The new queries broaden the search to a full biography and historic squad lists, avoiding the failing patterns while targeting the likely source of the squad number information.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Tommy Lawton Wikipedia\",\n \"Chelsea squad numbers 1940s\",\n \"Chelsea players 1945‑47 shirt\"\n ],\n \"feedback\": \"Previous attempts focused on specific phrases like 'jersey number' and returned no results. The new queries broaden the search to full player profiles, historical squad number lists for Chelsea in the 1940s, and generic shirt information for the 1945‑47 Chelsea squad, which should surface the needed squad number.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 816, + "osl": 106, + "total_tokens": 922, + "latency_ms": 4739.31, + "tokens_per_second": 22.37 + }, + "context": { + "kept_docs_count": 1, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "0051f38c-88a3-4d3c-978a-77473764be0d", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T03:01:51.016297Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: What number would Tommy Lawton have worn playing for Chelsea FC?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] the most emblematic squad numbers in football, due to the sheer number of football legends that have worn the number 10 shirt; playmakers , second strikers , and attacking midfielders have worn this number. History First use of numbers The first record of numbered jerseys in football date back to 1911, with Australian teams Sydney Leichhardt and HMS Powerful being the first to use squad numbers on their backs. One year later, numbering in football would be ruled as mandatory in New South Wales . The next recorded use was on 23 March 1914 when the English Wanderers, a team of amateur players from Football League clubs, played Corinthians at Stamford Bridge , London. This was Corinthians' first match after their FA ban for joining the Amateur Football Associat\n\n[NEW 2] l 1 to 11 numbers. References ^ ^ a b c d e f g h i j k ^ riferimento dal sito web del club reale più accurato rispetto agli altri [ clarification needed ] ^ a b ^ ^ ^ a b ^ a b c ^ a b c d e f g h i j k l ^ ^ ^ ^ a b c d {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ a b ^ ^ ^ ^ ^ ^ a b c ^ ^ ^ a b c d ^ ^ ^ a b c ^ ^ ^ {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ [ dead link ] ^ ^ ^ ^ a b ^ ^ a b ^ ^ a b ^ a b c ^ a b ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ External links Official website (in Italian) Genoa CFC at Serie A Genoa CFC at UEFA Media related to Genoa CFC at Wikimedia Commons Portals : Association football Italy\n\n\nKEPT DOCUMENTS (context only):\n[1 documents already kept as relevant]\n[KEPT 1] English association footballer (1919–1996) Tommy Lawton Lawton circa 1951 Personal information Full name Thomas Lawton Date of birth ( 1919-10-06 ) 6 October 1919 Place of birth Farnworth , England Date of death 6 November 1996 (1996-11-06) (aged 77) Place of death Nottingham , England Height 5 ft 1...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 2 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 743, + "osl": 373, + "total_tokens": 1116, + "latency_ms": 1247.36, + "tokens_per_second": 299.03 + }, + "context": { + "num_documents_evaluated": 2, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 1 + } + }, + { + "call_id": "41e01e76-6e06-466b-a073-2237622c986d", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:01:52.468175Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What number would Tommy Lawton have worn playing for Chelsea FC?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] English association footballer (1919–1996) Tommy Lawton Lawton circa 1951 Personal information Full name Thomas Lawton Date of birth ( 1919-10-06 ) 6 October 1919 Place of birth Farnworth , England Date of death 6 November 1996 (1996-11-06) (aged 77) Place of death Nottingham , England Height 5 ft 11 in (1.80 m) Position Centre-forward Youth career 1933 Rossendale United 1933–1936 Burnley Senior career* Years Team Apps ( Gls ) 1936–1937 Burnley 25 (16) 1937–1945 Everton 87 (65) 1945–1947 Chelsea 42 (30) 1947–1952 Notts County 151 (90) 1952–1953 Brentford 50 (17) 1953–1955 Arsenal 35 (13) 1955–1956 Kettering Town 30 (15) Total 420 (246) International career 1939–19?? The Football League XI 3 (2) 1938–1948 England 23 (22) 1939–1946 → England (wartime) 23 (24)\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The document provides Tommy Lawton's tenure at Chelsea (1945–1947) and his appearance/goal stats, but it does not include the jersey number he wore for Chelsea. The specific shirt number is missing, so the information is insufficient to answer the question.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 566, + "osl": 163, + "total_tokens": 729, + "latency_ms": 1451.34, + "tokens_per_second": 112.31 + }, + "context": { + "kept_docs_count": 1, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "0bdcb38a-096a-44fe-b294-4bce7b11e406", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:02:01.566477Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What number would Tommy Lawton have worn playing for Chelsea FC?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] English association footballer (1919–1996) Tommy Lawton Lawton circa 1951 Personal information Full name Thomas Lawton Date of birth ( 1919-10-06 ) 6 October 1919 Place of birth Farnworth , England Date of death 6 November 1996 (1996-11-06) (aged 77) Place of death Nottingham , England Height 5 ft 11 in (1.80 m) Position Centre-forward Youth career 1933 Rossendale United 1933–1936 Burnley Senior career* Years Team Apps ( Gls ) 1936–1937 Burnley 25 (16) 1937–1945 Everton 87 (65) 1945–1947 Chelsea 42 (30) 1947–1952 Notts County 151 (90) 1952–1953 Brentford 50 (17) 1953–1955 Arsenal 35 (13) 1955–1956 Kettering Town 30 (15) Total 420 (246) International career 1939–19?? The Football League XI 3 (2) 1938–1948 England 23 (22) 1939–1946 → England (wartime) 23 (24)\n\n\nSEARCH HISTORY: - Query: 'Tommy Lawton jersey number' → 3 docs\n- Query: 'Tommy Lawton Chelsea number' → 0 docs\n- Query: 'Tommy Lawton shirt number' → 0 docs\n- Query: 'Tommy Lawton biography' → 0 docs\n- Query: 'Chelsea 1945 squad' → 5 docs\n- Query: 'Chelsea centre forward 1946' → 0 docs\n- Query: 'Tommy Lawton Wikipedia' → 0 docs\n- Query: 'Chelsea squad numbers 1940s' → 2 docs\n- Query: 'Chelsea players 1945‑47 shirt' → 0 docs\n\nFEEDBACK: Need the specific squad number Tommy Lawton wore while at Chelsea FC. Initial broad queries will locate his player profile or historical squad lists.\nPrevious attempts using 'jersey', 'shirt number', and 'Chelsea number' returned no results. The new queries broaden the search to a full biography and historic squad lists, avoiding the failing patterns while targeting the likely source of the squad number information.\nPrevious attempts focused on specific phrases like 'jersey number' and returned no results. The new queries broaden the search to full player profiles, historical squad number lists for Chelsea in the 1940s, and generic shirt information for the 1945‑47 Chelsea squad, which should surface the needed squad number.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Tommy Lawton number 9 Chelsea\",\n \"Chelsea 1945‑46 squad list\",\n \"Chelsea historical player numbers\"\n ],\n \"feedback\": \"Previous attempts using 'jersey', 'shirt', and specific year‑number combos returned no results. The new queries broaden the search to full squad lists for the 1945‑46 season and general historical player‑number resources, while also testing the likely number (9) for a centre‑forward like Lawton.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 990, + "osl": 630, + "total_tokens": 1620, + "latency_ms": 9097.78, + "tokens_per_second": 69.25 + }, + "context": { + "kept_docs_count": 1, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "ba799173-d87f-4e83-ab3c-895f891bc9df", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T03:02:04.206043Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: What number would Tommy Lawton have worn playing for Chelsea FC?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] ed and Chelsea Most goals scored: 37 – Manchester United Fewest goals scored: 8 – Sheffield United Most goals conceded: 42 – Fulham Fewest goals conceded: 13 – Chelsea Statistics Top scorers Rank Player Club Goals 1 Didier Drogba Chelsea 20 2 Benni McCarthy Blackburn Rovers 18 3 Cristiano Ronaldo Manchester United 17 4 Wayne Rooney Manchester United 14 Mark Viduka Middlesbrough 6 Darren Bent Charlton Athletic 13 Kevin Doyle Reading 8 Dimitar Berbatov Tottenham Hotspur 12 Dirk Kuyt Liverpool Yakubu Middlesbrough Historic goals 15,000th goal The Premier League expected to have the league's 15,000th goal scored at some point in the period between Christmas and New Year. The target was reached on 30 December when Moritz Volz scored for Fulham against Chelsea. Ba\n\n[NEW 2] rby County 2019–2021 Chelsea 2022–2023 Everton 2023 Chelsea (caretaker) 2024– Coventry City * Club domestic league appearances and goals Frank James Lampard (born 20 June 1978) is an English football manager and former midfielder who is the manager of EFL Championship club Coventry City . Widely regarded as one of the greatest midfielders of all time and one of Chelsea 's and the Premier League ’s greatest ever players, Lampard holds the record of the most Premier League goals (177) by a midfielder in its history. A box-to-box midfielder , Lampard began his career in 1995 at West Ham United , the club where his father Frank Sr. also played. In 2001, he was signed by London rivals Chelsea for £11 million. In his thirteen years with the club, Lampard establish\n\n\nKEPT DOCUMENTS (context only):\n[1 documents already kept as relevant]\n[KEPT 1] English association footballer (1919–1996) Tommy Lawton Lawton circa 1951 Personal information Full name Thomas Lawton Date of birth ( 1919-10-06 ) 6 October 1919 Place of birth Farnworth , England Date of death 6 November 1996 (1996-11-06) (aged 77) Place of death Nottingham , England Height 5 ft 1...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 2 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 704, + "osl": 149, + "total_tokens": 853, + "latency_ms": 2117.53, + "tokens_per_second": 70.36 + }, + "context": { + "num_documents_evaluated": 2, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 1 + } + }, + { + "call_id": "890427a1-cd7a-4349-ac82-ba61088f5605", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:02:06.893298Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What number would Tommy Lawton have worn playing for Chelsea FC?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] English association footballer (1919–1996) Tommy Lawton Lawton circa 1951 Personal information Full name Thomas Lawton Date of birth ( 1919-10-06 ) 6 October 1919 Place of birth Farnworth , England Date of death 6 November 1996 (1996-11-06) (aged 77) Place of death Nottingham , England Height 5 ft 11 in (1.80 m) Position Centre-forward Youth career 1933 Rossendale United 1933–1936 Burnley Senior career* Years Team Apps ( Gls ) 1936–1937 Burnley 25 (16) 1937–1945 Everton 87 (65) 1945–1947 Chelsea 42 (30) 1947–1952 Notts County 151 (90) 1952–1953 Brentford 50 (17) 1953–1955 Arsenal 35 (13) 1955–1956 Kettering Town 30 (15) Total 420 (246) International career 1939–19?? The Football League XI 3 (2) 1938–1948 England 23 (22) 1939–1946 → England (wartime) 23 (24)\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The document provides Tommy Lawton's tenure, appearances, and goals for Chelsea FC but does not include any information about the shirt number he wore. Therefore, the required fact to answer the question is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 566, + "osl": 425, + "total_tokens": 991, + "latency_ms": 2686.53, + "tokens_per_second": 158.2 + }, + "context": { + "kept_docs_count": 1, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "acc619bc-221c-4d19-9ec5-7231b5514433", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:02:08.142299Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: What number would Tommy Lawton have worn playing for Chelsea FC?\n\nDOCUMENTS:\n\n[DOC 1] English association footballer (1919–1996) Tommy Lawton Lawton circa 1951 Personal information Full name Thomas Lawton Date of birth ( 1919-10-06 ) 6 October 1919 Place of birth Farnworth , England Date of death 6 November 1996 (1996-11-06) (aged 77) Place of death Nottingham , England Height 5 ft 11 in (1.80 m) Position Centre-forward Youth career 1933 Rossendale United 1933–1936 Burnley Senior career* Years Team Apps ( Gls ) 1936–1937 Burnley 25 (16) 1937–1945 Everton 87 (65) 1945–1947 Chelsea 42 (30) 1947–1952 Notts County 151 (90) 1952–1953 Brentford 50 (17) 1953–1955 Arsenal 35 (13) 1955–1956 Kettering Town 30 (15) Total 420 (246) International career 1939–19?? The Football League XI 3 (2) 1938–1948 England 23 (22) 1939–1946 → England (wartime) 23 (24)\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 396, + "osl": 40, + "total_tokens": 436, + "latency_ms": 1248.43, + "tokens_per_second": 32.04 + }, + "context": { + "num_documents_used": 1, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:02:08.143049Z", + "summary": { + "total_llm_calls": 13, + "total_hops": 5, + "total_input_tokens": 9829, + "total_output_tokens": 4151, + "total_tokens": 13980, + "total_latency_ms": 54751.64, + "average_tokens_per_second": 91.8, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Tommy_Lawton", + "https://en.wikipedia.org/wiki/Squad_number_(association_football)" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "9" + } + }, + { + "query_id": "28", + "original_query": "If you subtract the year that William McCrary \"Billy\" Ray II was born from the year Obama was first sworn in as President to the United States and multiply it by the number of administrative regions in France as of January 1, 2024, what number would you get?", + "timestamp_start": "2026-05-16T03:02:08.167305Z", + "llm_calls": [ + { + "call_id": "5290f56a-ef3b-42cf-8869-9009a6264af9", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:02:15.545524Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: If you subtract the year that William McCrary \"Billy\" Ray II was born from the year Obama was first sworn in as President to the United States and multiply it by the number of administrative regions in France as of January 1, 2024, what number would you get?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"William McCrary Billy Ray II birth year\",\n \"Billy Ray II born\",\n \"William McCrary biography\"\n ],\n \"feedback\": \"We need the birth year of William McCrary \\\"Billy\\\" Ray II. No documents retrieved yet; the above queries aim to locate his biographical info (birth year).\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 480, + "osl": 233, + "total_tokens": 713, + "latency_ms": 7378.06, + "tokens_per_second": 31.58 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "647bd4ec-13d0-48e4-afe1-ed597c9a082b", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:02:46.723013Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: If you subtract the year that William McCrary \"Billy\" Ray II was born from the year Obama was first sworn in as President to the United States and multiply it by the number of administrative regions in France as of January 1, 2024, what number would you get?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] May 3, 1963 (age 62) Macon , Georgia , U. S. Party Republican Spouse Kelle Chandler ( m. 1989) Children Chandler, Davis, Avery Education University of Georgia ( BBA , MBA , JD ) William McCrary \"Billy\" Ray II (born May 3, 1963) is a United States district judge of the United States District Court for the Northern District of Georgia . He was previously a judge of the Georgia Court of Appeals and a member of the Georgia State Senate for District 48 . Biography Ray was born in Macon, Georgia in 1963. He received his Bachelor of Business Administration from the University of Georgia 's Terry College of Business in 1985, magna cum laude , his Master of Business Administration from the Terry College of Business in 1986, and his Juris Doctor , cum laude , from the\n\n[NEW 2] ry (before 1949) Bert Assirati Billy Riley Billy Sandow Ed \"Strangler\" Lewis Frank Gotch George Hackenschmidt The Great Gama Jack Pfefer Joe Stecher Jim Londos Lou Thesz Martin \"Farmer\" Burns Orville Brown Salvador Lutteroth Toots Mondt Mid 20th century (1950−1969) Blue Demon Bruno Sammartino Buddy Rogers Danny Hodge Dick \"The Destroyer\" Beyer Édouard Carpentier El Santo Freddie Blassie Fritz Von Erich Gene Kiniski Gorgeous George Karl Gotch Killer Kowalski Pat O'Connor Rikidōzan Sam Muchnick Stu Hart Verne Gagne Whipper Billy Watson 1970s Abdullah The Butcher Akira Maeda André the Giant Antonio Inoki Baron von Raschke Big Daddy Billy Robinson Carlos Colón Dory Funk Jr. Dusty Rhodes Ernie Ladd Giant Baba Gordon Solie Harley Race Ivan Koloff Jack Brisco Mil M\n\n[NEW 3] + 1 ⁄ 2 Fast 2:37.75 1876 Vagrant Robert Swim James Williams William Astor Jr. 1 + 1 ⁄ 2 Fast 2:38.25 1877 Baden-Baden Billy Walker Ed Brown Daniel Swigert 1 + 1 ⁄ 2 Fast 2:38.0 1878 Day Star Jimmy Carter Lee Paul Thomas J. Nichols 1 + 1 ⁄ 2 Fast 2:37.25 1879 Lord Murphy Charlie Shauer George Rice George W. Darden & Co. 1 + 1 ⁄ 2 Fast 2:37.00 1880 Fonso George Lewis Tice Hutsell J. S. Shawhan 1 + 1 ⁄ 2 Fast 2:37.50 1881 Hindoo †‡ Jim McLaughlin James Rowe Sr. Dwyer Brothers 1 + 1 ⁄ 2 Fast 2:40.0 1882 Apollo Babe Hurd Green B. Morris Green B. Morris, James D. Patton 1 + 1 ⁄ 2 Fast 2:40.25 1883 Leonatus William Donohue Raleigh Colston Sr. Jack P. Chinn, George Morgan 1 + 1 ⁄ 2 Heavy 2:43.0 1884 Buchanan Isaac Murphy William Bird William Cottrill, Sam S. Brown\n\n[NEW 4] EEV -lənd ; né Judkins ; born May 13, 1950), known professionally as Stevie Wonder , is an American singer-songwriter, musician, and record producer. He is widely regarded as one of the most influential musicians of the 20th century, and is credited as a pioneer and influence by musicians across a range of genres that include R&B , pop, soul , gospel , funk , and jazz . A virtual one-man band during much of his peak years, Wonder's use of synthesizers and other electronic musical instruments in the 1970s reshaped the conventions of contemporary R&B . He also helped drive such genres into the album era , crafting his LPs as cohesive and consistent, in addition to socially conscious statements with complex compositions. Blind since shortly after his birth, Won\n\n[NEW 5] can actor and producer Timothy Snyder , American author and historian August 19 Nate Dogg , African-American rapper (d. 2011 ) Matthew Perry , American actor (d. 2023 ) August 20 Neil Fitzmaurice , English actor, comedian and writer Santeri Kinnunen , Finnish actor August 23 – Jean-Marc Tellier , French politician August 28 – Jack Black , American actor and musician August 29 – Lucero , Mexican singer and actress September Mojtaba Khamenei Tyler Perry Bong Joon-ho Hal Sparks Catherine Zeta-Jones September 3 – Robert Karlsson , Swedish golfer September 4 Giorgi Margvelashvili , politician; 4th President of the Republic of Georgia Noah Taylor , Australian actor Alexander Paul Coe , Welsh DJ and record producer September 6 Michellie Jones , Australian triathlet\n\n[NEW 6] William Bellasis of Newburgh Priory 1575–1576 Sir Thomas Danby (c.1530–1590) 1576–1577 Thomas Boynton 1577–1578 William Fairfax of Gilling Castle 1578–1579 Christopher Wandesforde 1579–1580 Richard Goodricke of Ribston Hall 1580–1581 Ralph Bourchier 1581–1582 Sir Robert Stapleton of Easdyke, Wighill 1582–1583 Thomas Wentworth 1583–1584 Sir Cotton Gargrave 1584–1585 John Hotham 1585–1586 Brian Stapleton 1586–1587 Henry Constable of Burton Constable 1587–1588 Robert Aske 1588–1589 Sir Richard Mauleverer 1589–1590 Sir John Dawnay 1590–1591 Philip Constable 1591–1592 Richard Goodricke of Ribston Hall (son of Richard, HS 1579) 1592–1593 Sir William Mallory 1593–1594 Ralph Eure 1594–1595 Francis Vaughan 1595–1596 Sir Christopher Hildyard 1596–1597 Francis Boynton\n\n[NEW 7] , a murderer, a thief and a liar.\" He was most certainly a gambler, a cockfighter, a Negro trader, and a bigamist, although at the late hour of 1828 it was perhaps ungracious to bring it up. Jackson believed that attacks on him were personal vendettas consequent to past disputes, writing, \"I am branded with every crime, and Doctor McNary , Col. Erwin , Anderson and Williams are associated for this purpose.\" One thing that all these men had in common is that they had all known and/or been business partners and/or been military comrades of Jackson going back decades. McNairy's brother gave Jackson his first law job in Nashville in 1789, Erwin and Jackson had been long-time land speculation partners until a massive deal went bad, Anderson had been Jackson's aid\n\n[NEW 8] nty In office January 1, 1871 – December 31, 1873 Preceded by Charles Darcy Succeeded by John B. Weber Personal details Born Stephen Grover Cleveland ( 1837-03-18 ) March 18, 1837 Caldwell, New Jersey , U. S. Died June 24, 1908 (1908-06-24) (aged 71) Princeton, New Jersey , U. S. Resting place Princeton Cemetery Party Democratic Other political affiliations National Democratic Spouse Frances Folsom ( m. 1886 ) Children 6, including Oscar , Ruth , Esther , Richard and Francis Parent Richard Falley Cleveland (father) Relatives Richard Falley Jr. (great-grandfather) Rose Cleveland (sister) Philippa Foot (granddaughter) Occupation Politician lawyer Signature Alleged recording of Grover Cleveland During his 1892 election campaign . The authenticity is in doubt an\n\n[NEW 9] 40–1913) 1872 1877 5 years Democratic TBA 24 Jacob Carl DeGress (TBA–TBA) 1877 1879 2 years Unknown TBA 25 William A. Saylor (TBA–TBA) 1st time 1879 1881 2 years Unknown TBA 26 L. M. Crooker (TBA–TBA) 1881 1883 2 years Unknown TBA 27 William A. Saylor (TBA–TBA) 2nd time 1883 1884 1 year Unknown TBA 28 John W. Robertson (TBA–TBA) 1884 1887 3 years Unknown TBA 29 Joseph Nalle (TBA–TBA) 1887 1890 3 years Unknown TBA 30 John McDonald (TBA–TBA) 1890 1895 5 years Unknown TBA 31 Louis Hancock (TBA–TBA) 1896 1897 1 year Unknown TBA 32 John Dodd McCall (TBA–TBA) 1897 1901 4 years Unknown TBA 33 Robert E. White (TBA–TBA) 1901 1905 4 years Unknown TBA 34 William D. Shelley (TBA–TBA) 1905 1907 2 years Unknown TBA 35 Frank M. Maddox (TBA–TBA) 1907 1909 2 years Unknown TB\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 9 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1,0,0,0,0,0,0,0,0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2322, + "osl": 533, + "total_tokens": 2855, + "latency_ms": 30586.09, + "tokens_per_second": 17.43 + }, + "context": { + "num_documents_evaluated": 9, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "f498bea7-89c2-482a-a5d2-982f8ea0de47", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:02:48.166995Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: If you subtract the year that William McCrary \"Billy\" Ray II was born from the year Obama was first sworn in as President to the United States and multiply it by the number of administrative regions in France as of January 1, 2024, what number would you get?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] May 3, 1963 (age 62) Macon , Georgia , U. S. Party Republican Spouse Kelle Chandler ( m. 1989) Children Chandler, Davis, Avery Education University of Georgia ( BBA , MBA , JD ) William McCrary \"Billy\" Ray II (born May 3, 1963) is a United States district judge of the United States District Court for the Northern District of Georgia . He was previously a judge of the Georgia Court of Appeals and a member of the Georgia State Senate for District 48 . Biography Ray was born in Macon, Georgia in 1963. He received his Bachelor of Business Administration from the University of Georgia 's Terry College of Business in 1985, magna cum laude , his Master of Business Administration from the Terry College of Business in 1986, and his Juris Doctor , cum laude , from the\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"sufficient\": false, \"reasoning\": \"Missing: the year Barack Obama was first sworn in as President (2009) and the number of administrative regions in France as of January 1, 2024 (18). Only the birth year of William McCrary \\\"Billy\\\" Ray II (1963) is present in the kept documents.\"}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 532, + "osl": 322, + "total_tokens": 854, + "latency_ms": 1443.06, + "tokens_per_second": 223.14 + }, + "context": { + "kept_docs_count": 1, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "2df3f8dc-03bb-4d57-bdab-a166983e804f", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:02:52.857123Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: If you subtract the year that William McCrary \"Billy\" Ray II was born from the year Obama was first sworn in as President to the United States and multiply it by the number of administrative regions in France as of January 1, 2024, what number would you get?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] May 3, 1963 (age 62) Macon , Georgia , U. S. Party Republican Spouse Kelle Chandler ( m. 1989) Children Chandler, Davis, Avery Education University of Georgia ( BBA , MBA , JD ) William McCrary \"Billy\" Ray II (born May 3, 1963) is a United States district judge of the United States District Court for the Northern District of Georgia . He was previously a judge of the Georgia Court of Appeals and a member of the Georgia State Senate for District 48 . Biography Ray was born in Macon, Georgia in 1963. He received his Bachelor of Business Administration from the University of Georgia 's Terry College of Business in 1985, magna cum laude , his Master of Business Administration from the Terry College of Business in 1986, and his Juris Doctor , cum laude , from the\n\n\nSEARCH HISTORY: - Query: 'William McCrary Billy Ray II birth year' → 3 docs\n- Query: 'Billy Ray II born' → 2 docs\n- Query: 'William McCrary biography' → 4 docs\n\nFEEDBACK: We need the birth year of William McCrary \"Billy\" Ray II. No documents retrieved yet; the above queries aim to locate his biographical info (birth year).\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Barack Obama inauguration year\",\n \"France administrative regions 2024\",\n \"William McCrary Billy Ray II\"\n ],\n \"feedback\": \"We have the birth year (1963) from the existing document, but need a reliable source for Obama’s first inauguration year and the count of French administrative regions as of 2024. The new queries target those missing pieces while also confirming the judge’s birth year from a full article.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 688, + "osl": 98, + "total_tokens": 786, + "latency_ms": 4689.48, + "tokens_per_second": 20.9 + }, + "context": { + "kept_docs_count": 1, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "87d1e422-57b4-49b0-8c6f-aa5d92ec0333", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T03:02:54.448966Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: If you subtract the year that William McCrary \"Billy\" Ray II was born from the year Obama was first sworn in as President to the United States and multiply it by the number of administrative regions in France as of January 1, 2024, what number would you get?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] President of the United States from 2009 to 2017 Barack Obama Official portrait, 2012 44th President of the United States In office January 20, 2009 – January 20, 2017 Vice President Joe Biden Preceded by George W. Bush Succeeded by Donald Trump United States Senator from Illinois In office January 3, 2005 – November 16, 2008 Preceded by Peter Fitzgerald Succeeded by Roland Burris Member of the Illinois Senate from the 13th district In office January 8, 1997 – November 4, 2004 Preceded by Alice Palmer Succeeded by Kwame Raoul Personal details Born Barack Hussein Obama II ( 1961-08-04 ) August 4, 1961 (age 64) Honolulu , Hawaii, US Party Democratic Spouse Michelle Robinson ( m. 1992 ) Children Malia Sasha Parents Barack Obama Sr. (father) Ann Dunham (mother)\n\n[NEW 2] Administrative divisions of France Regions of France Régions ( French ) Hauts-de-France Normandy Île-de-France Grand Est Bourgogne-Franche- Comté Centre-Val de Loire Pays de la Loire Brittany Nouvelle-Aquitaine Auvergne-Rhône-Alpes Occitania Provence-Alpes- Côte d'Azur Corsica French Guiana Guadeloupe Martinique Mayotte Réunion Belgium Luxembourg Germany Switzerland Liechtenstein Italy Monaco United Kingdom Andorra Brazil Suriname Spain English Channel Bay of Biscay Ligurian Sea Mediterranean Sea Category Unitary state Location French Republic Number 18 Possible status Overseas region ( région d'outre-mer ) (5) Additional status Territorial collectivity ( collectivité territoriale ) Populations 279,471 ( Mayotte ) – 12,997,058 ( Île-de-France ) Areas 376 km\n\n[NEW 3] Paul Blair Amos Otis Mickey Stanley Thurman Munson Jim Kaat † N/A 1974 George Scott Bobby Grich Brooks Robinson * † Mark Belanger Paul Blair Amos Otis Joe Rudi Thurman Munson Jim Kaat † N/A 1975 George Scott Bobby Grich Brooks Robinson * † Mark Belanger Paul Blair Fred Lynn Joe Rudi Thurman Munson Jim Kaat † N/A 1976 George Scott Bobby Grich Aurelio Rodríguez Mark Belanger Dwight Evans Rick Manning Joe Rudi Jim Sundberg Jim Palmer † N/A 1977 Jim Spencer Frank White Graig Nettles Mark Belanger Juan Beníquez Al Cowens Carl Yastrzemski † Jim Sundberg Jim Palmer † N/A 1978 Chris Chambliss Frank White Graig Nettles Mark Belanger Dwight Evans Fred Lynn Rick Miller Jim Sundberg Jim Palmer † N/A 1979 Cecil Cooper Frank White Buddy Bell Rick Burleson Dwight Evans Fre\n\n\nKEPT DOCUMENTS (context only):\n[1 documents already kept as relevant]\n[KEPT 1] May 3, 1963 (age 62) Macon , Georgia , U. S. Party Republican Spouse Kelle Chandler ( m. 1989) Children Chandler, Davis, Avery Education University of Georgia ( BBA , MBA , JD ) William McCrary \"Billy\" Ray II (born May 3, 1963) is a United States district judge of the United States District Court fo...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 3 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 1, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 967, + "osl": 360, + "total_tokens": 1327, + "latency_ms": 990.68, + "tokens_per_second": 363.39 + }, + "context": { + "num_documents_evaluated": 3, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 1 + } + }, + { + "call_id": "8bc37db7-0574-4cd2-86a0-0dab107904b7", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:03:00.231473Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: If you subtract the year that William McCrary \"Billy\" Ray II was born from the year Obama was first sworn in as President to the United States and multiply it by the number of administrative regions in France as of January 1, 2024, what number would you get?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] May 3, 1963 (age 62) Macon , Georgia , U. S. Party Republican Spouse Kelle Chandler ( m. 1989) Children Chandler, Davis, Avery Education University of Georgia ( BBA , MBA , JD ) William McCrary \"Billy\" Ray II (born May 3, 1963) is a United States district judge of the United States District Court for the Northern District of Georgia . He was previously a judge of the Georgia Court of Appeals and a member of the Georgia State Senate for District 48 . Biography Ray was born in Macon, Georgia in 1963. He received his Bachelor of Business Administration from the University of Georgia 's Terry College of Business in 1985, magna cum laude , his Master of Business Administration from the Terry College of Business in 1986, and his Juris Doctor , cum laude , from the\n\n[DOC 2] President of the United States from 2009 to 2017 Barack Obama Official portrait, 2012 44th President of the United States In office January 20, 2009 – January 20, 2017 Vice President Joe Biden Preceded by George W. Bush Succeeded by Donald Trump United States Senator from Illinois In office January 3, 2005 – November 16, 2008 Preceded by Peter Fitzgerald Succeeded by Roland Burris Member of the Illinois Senate from the 13th district In office January 8, 1997 – November 4, 2004 Preceded by Alice Palmer Succeeded by Kwame Raoul Personal details Born Barack Hussein Obama II ( 1961-08-04 ) August 4, 1961 (age 64) Honolulu , Hawaii, US Party Democratic Spouse Michelle Robinson ( m. 1992 ) Children Malia Sasha Parents Barack Obama Sr. (father) Ann Dunham (mother)\n\n[DOC 3] Administrative divisions of France Regions of France Régions ( French ) Hauts-de-France Normandy Île-de-France Grand Est Bourgogne-Franche- Comté Centre-Val de Loire Pays de la Loire Brittany Nouvelle-Aquitaine Auvergne-Rhône-Alpes Occitania Provence-Alpes- Côte d'Azur Corsica French Guiana Guadeloupe Martinique Mayotte Réunion Belgium Luxembourg Germany Switzerland Liechtenstein Italy Monaco United Kingdom Andorra Brazil Suriname Spain English Channel Bay of Biscay Ligurian Sea Mediterranean Sea Category Unitary state Location French Republic Number 18 Possible status Overseas region ( région d'outre-mer ) (5) Additional status Territorial collectivity ( collectivité territoriale ) Populations 279,471 ( Mayotte ) – 12,997,058 ( Île-de-France ) Areas 376 km\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": true,\n \"reasoning\": \"DOC 1 provides William McCrary \\\"Billy\\\" Ray II's birth year (1963). DOC 2 indicates Barack Obama was first sworn in as President in 2009. DOC 3 lists the French administrative regions and states the number as 18. All required facts are present to compute (2009 - 1963) * 18.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 901, + "osl": 397, + "total_tokens": 1298, + "latency_ms": 5781.83, + "tokens_per_second": 68.66 + }, + "context": { + "kept_docs_count": 3, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "bb0ac8cf-524f-458c-a6b0-d601be01c4a4", + "component": "answer_generator", + "hop_count": 3, + "timestamp": "2026-05-16T03:03:03.125532Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: If you subtract the year that William McCrary \"Billy\" Ray II was born from the year Obama was first sworn in as President to the United States and multiply it by the number of administrative regions in France as of January 1, 2024, what number would you get?\n\nDOCUMENTS:\n\n[DOC 1] May 3, 1963 (age 62) Macon , Georgia , U. S. Party Republican Spouse Kelle Chandler ( m. 1989) Children Chandler, Davis, Avery Education University of Georgia ( BBA , MBA , JD ) William McCrary \"Billy\" Ray II (born May 3, 1963) is a United States district judge of the United States District Court for the Northern District of Georgia . He was previously a judge of the Georgia Court of Appeals and a member of the Georgia State Senate for District 48 . Biography Ray was born in Macon, Georgia in 1963. He received his Bachelor of Business Administration from the University of Georgia 's Terry College of Business in 1985, magna cum laude , his Master of Business Administration from the Terry College of Business in 1986, and his Juris Doctor , cum laude , from the\n\n[DOC 2] President of the United States from 2009 to 2017 Barack Obama Official portrait, 2012 44th President of the United States In office January 20, 2009 – January 20, 2017 Vice President Joe Biden Preceded by George W. Bush Succeeded by Donald Trump United States Senator from Illinois In office January 3, 2005 – November 16, 2008 Preceded by Peter Fitzgerald Succeeded by Roland Burris Member of the Illinois Senate from the 13th district In office January 8, 1997 – November 4, 2004 Preceded by Alice Palmer Succeeded by Kwame Raoul Personal details Born Barack Hussein Obama II ( 1961-08-04 ) August 4, 1961 (age 64) Honolulu , Hawaii, US Party Democratic Spouse Michelle Robinson ( m. 1992 ) Children Malia Sasha Parents Barack Obama Sr. (father) Ann Dunham (mother)\n\n[DOC 3] Administrative divisions of France Regions of France Régions ( French ) Hauts-de-France Normandy Île-de-France Grand Est Bourgogne-Franche- Comté Centre-Val de Loire Pays de la Loire Brittany Nouvelle-Aquitaine Auvergne-Rhône-Alpes Occitania Provence-Alpes- Côte d'Azur Corsica French Guiana Guadeloupe Martinique Mayotte Réunion Belgium Luxembourg Germany Switzerland Liechtenstein Italy Monaco United Kingdom Andorra Brazil Suriname Spain English Channel Bay of Biscay Ligurian Sea Mediterranean Sea Category Unitary state Location French Republic Number 18 Possible status Overseas region ( région d'outre-mer ) (5) Additional status Territorial collectivity ( collectivité territoriale ) Populations 279,471 ( Mayotte ) – 12,997,058 ( Île-de-France ) Areas 376 km\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "828", + "finish_reason": "stop" + }, + "metrics": { + "isl": 718, + "osl": 1, + "total_tokens": 719, + "latency_ms": 2893.46, + "tokens_per_second": 0.35 + }, + "context": { + "num_documents_used": 3, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:03:03.126154Z", + "summary": { + "total_llm_calls": 7, + "total_hops": 3, + "total_input_tokens": 6608, + "total_output_tokens": 1944, + "total_tokens": 8552, + "total_latency_ms": 53762.66, + "average_tokens_per_second": 103.64, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/William_M._Ray_II", + "https://en.wikipedia.org/wiki/Barack_Obama", + "https://en.wikipedia.org/wiki/Regions_of_France" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "828", + "ground_truth_answer": "828" + } + }, + { + "query_id": "29", + "original_query": "If Princess Diana had been born three years earlier, who would have been Prime Minister when she was ten? ", + "timestamp_start": "2026-05-16T03:03:03.150801Z", + "llm_calls": [ + { + "call_id": "6885d191-fba5-4c2f-8245-b086e3700805", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:03:07.332547Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: If Princess Diana had been born three years earlier, who would have been Prime Minister when she was ten? \n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Princess Diana birth date\",\n \"UK Prime Minister 1968\",\n \"Harold Wilson term dates\"\n ],\n \"feedback\": \"We need Diana's birth year to adjust by three years and determine the year she would be ten (1968). Then identify who held the UK Prime Minister office in 1968.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 397, + "osl": 76, + "total_tokens": 473, + "latency_ms": 4181.59, + "tokens_per_second": 18.17 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "24872bde-9fac-41fc-80a7-6f0688df9530", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:03:23.285839Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: If Princess Diana had been born three years earlier, who would have been Prime Minister when she was ten? \n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Look up Diana in Wiktionary, the free dictionary. Diana most commonly refers to: Diana (name) , given name (including a list of people with the name) Diana (mythology) , ancient Roman goddess of the hunt and wild animals; later associated with the Moon Diana, Princess of Wales (1961–1997), formerly Lady Diana Spencer, activist, philanthropist, and member of the British royal family Diana may also refer to: Places and jurisdictions Africa Diana (see) , a town and commune in Souk Ahras Province in north-eastern Algeria Diana's Peak , the highest point on the island of Saint Helena Diana Region , a region in Madagascar Diana Veteranorum , an ancient city, former bishopric and present Latin Catholic titular see in Algeria Asia Diana, Iraq , a town in Iraqi Kurdi\n\n[NEW 2] , pp. 305–306, 315. ^ a b c d Weir 1996 , p. 317. ^ Weir 1996 , pp. 319, 321. ^ Tomlinson (2017): \"A New Name – Windsor\" . ^ Weir 1996 , pp. 322, 326. ^ Heard 1990 . ^ Weir 1996 , pp. 327–328. ^ Weir 1996 , pp. 329–330. ^ Weir 1996 , p. 331. ^ a b c d Westminster Abbey: \"Charles III\" . ^ BBC News (2004): \"Timeline: Diana, Princess of Wales\" . Sources Web sources News sources Book sources External links Media related to British monarchs at Wikimedia Commons\n\n[NEW 3] andra Prince Michael of Kent Princess Michael of Kent v t e Charles III (Charles Philip Arthur George; born 14 November 1948) is King of the United Kingdom and 14 other Commonwealth realms . Charles was born during the reign of his maternal grandfather, King George VI , and became heir apparent when his mother, Queen Elizabeth II , acceded to the throne in 1952. He was created Prince of Wales in 1958 and his investiture was held in 1969. Charles was educated at Cheam School and Gordonstoun , and later spent six months at the Timbertop campus of Geelong Grammar School in Victoria, Australia. After completing a history degree at the University of Cambridge , he served in the Royal Air Force and the Royal Navy from 1971 to 1976. He married Lady Diana Spencer in\n\n[NEW 4] n 1986, to Russia in 1994, and to the Republic of Ireland in 2011; she met five popes and fourteen US presidents. Significant events of her reign included her coronation in 1953 and the celebrations of her Silver , Golden , Diamond , and Platinum jubilees . Although there was occasional republican sentiment and media criticism of her family—particularly after the breakdowns of her children's marriages, her annus horribilis in 1992, and the death of her former daughter-in-law Diana in 1997—support for the monarchy and her popularity in the United Kingdom remained consistently high. Elizabeth died aged 96 at Balmoral Castle in Scotland, and was succeeded by her eldest son as Charles III. Early life Elizabeth was born at 2:40 am on 21 April 1926 by Caesarean se\n\n[NEW 5] Prime Minister of the United Kingdom from 1970 to 1974 The Right Honourable Sir Edward Heath KG MBE Portrait by Allan Warren , 1987 Prime Minister of the United Kingdom In office 19 June 1970 – 4 March 1974 Monarch Elizabeth II Preceded by Harold Wilson Succeeded by Harold Wilson Senior political offices Leader of the Opposition In office 4 March 1974 – 11 February 1975 Monarch Elizabeth II Prime Minister Harold Wilson Preceded by Harold Wilson Succeeded by Margaret Thatcher In office 28 July 1965 – 19 June 1970 Monarch Elizabeth II Prime Minister Harold Wilson Deputy Reginald Maudling Preceded by Alec Douglas-Home Succeeded by Harold Wilson Leader of the Conservative Party In office 28 July 1965 – 11 February 1975 Deputy Reginald Maudling (1965–1972) Preced\n\n[NEW 6] Prime Minister of the United Kingdom from 1979 to 1990 The Right Honourable The Baroness Thatcher LG OM DStJ PC FRS HonFRSC Studio portrait, c. 1995–96 Prime Minister of the United Kingdom In office 4 May 1979 – 28 November 1990 Monarch Elizabeth II Deputy Geoffrey Howe (1989–90) Preceded by James Callaghan Succeeded by John Major Leader of the Opposition In office 11 February 1975 – 4 May 1979 Monarch Elizabeth II Prime Minister Harold Wilson James Callaghan Deputy William Whitelaw Preceded by Edward Heath Succeeded by James Callaghan Leader of the Conservative Party In office 11 February 1975 – 28 November 1990 Deputy The Viscount Whitelaw Chairman See list The Lord Thorneycroft Cecil Parkinson John Gummer Norman Tebbit Peter Brooke Kenneth Baker Preceded\n\n[NEW 7] e Civil Service (1968–1970) Labour Wilson I 1966 Wilson II Edward Heath MP for Bexley (1916–2005) 19 June 1970 4 March 1974 3 years, 259 days 1970 First Lord of the Treasury Minister for the Civil Service Conservative Heath Harold Wilson MP for Huyton (1916–1995) Premiership 4 March 1974 5 April 1976 2 years, 33 days ( Feb. 1974 ) First Lord of the Treasury Minister for the Civil Service Labour Wilson III Oct. 1974 Wilson IV James Callaghan MP for Cardiff South East (1912–2005) 5 April 1976 4 May 1979 3 years, 30 days — First Lord of the Treasury Minister for the Civil Service Callaghan Margaret Thatcher MP for Finchley (1925–2013) Premiership 4 May 1979 28 November 1990 11 years, 209 days 1979 First Lord of the Treasury Minister for the Civil Service Conser\n\n[NEW 8] 65) The Gambia Prime Minister (1965–1970), President (1970–1994) Yusuf Lule Uganda President (1979) Fawzi Mulki Jordan Prime Minister (1953–1954) Lord Dunrossil Australia Governor-General (1960–1961) Daniel Chanis Pinzón Panama President (1949) Julius Nyerere Tanganyika Chief Minister (1960–1961), Prime Minister (1961–1962), President (1962–1964) Tanzania President (1964–1985) Paul Reeves New Zealand Governor-General (1985–1990) Lord John Russell United Kingdom Prime Minister (1846–1852; 1865–1866) Sir Ninian Stephen Australia Governor-General (1982-1989) John Swinney Scotland First Minister (2024–) Lord Palmerston United Kingdom Prime Minister (1855–1858; 1859–1865) Sir Charles Tupper Canada Prime Minister (1896) William Walker Nicaragua Filibuster of Nicar\n\n[NEW 9] ) 29 March 1943 (age 83) St Helier, Surrey , England Party Conservative Spouse Norma Johnson ( m. 1970 ) Children 2 Parent Tom Major-Ball (father) Relatives Terry Major-Ball (brother) Signature Website Official website John Major's voice Major's comments on the 25th anniversary of the Downing Street Declaration Recorded 11 December 2018 This article is part of a series about John Major Electoral history MP for Huntingdon 1990 budget Prime Minister of the United Kingdom Premiership First ministry and term (November 1990 – April 1992) 1990 leadership election Ministry Citizen's Charter Charter Mark Cones Hotline Early 1990s recession Gulf War 1992 general election Second ministry and term (April 1992 – May 1997) Ministry Black Wednesday National Lottery Furth\n\n[NEW 10] 19 ( 20th ) 1922 ( 21st ) 10 July 1912 10 May 1925 12 years, 305 days Reform Reform with Liberal 1915–1919; C&S with independents 20 The Right Honourable Francis Bell GCMG KC Councillor (1851–1936) – ( 21st ) 14 May 1925 30 May 1925 17 days Reform 21 The Right Honourable Gordon Coates MC* MP for Kaipara (1878–1943) – ( 21st ) 1925 ( 22nd ) 30 May 1925 10 December 1928 3 years, 195 days Reform (17) The Right Honourable Sir Joseph Ward Bt GCMG MP for Invercargill (1856–1930) 1928 ( 23rd ) 10 December 1928 28 May 1930 1 year, 170 days United United with Labour ; C&S with independents 22 The Right Honourable George Forbes MP for Hurunui (1869–1947) – ( 23rd ) 28 May 1930 6 December 1935 5 years, 193 days United 1931 ( 24th ) United–Reform Coalition 23 The Right\n\n[NEW 11] ) 1st term March 4, 1885 – March 4, 1889 Democratic 1884 Thomas A. Hendricks Vacant after November 25, 1885 23 Benjamin Harrison (1833–1901) March 4, 1889 – March 4, 1893 Republican 1888 Levi P. Morton 24 Grover Cleveland (1837–1908) 2nd term March 4, 1893 – March 4, 1897 Democratic 1892 Adlai Stevenson I 25 William McKinley (1843–1901) March 4, 1897 – September 14, 1901 Republican 1896 Garret Hobart Vacant after November 21, 1899 1900 Theodore Roosevelt 26 Theodore Roosevelt (1858–1919) September 14, 1901 – March 4, 1909 Republican – Vacant through March 4, 1905 1904 Charles W. Fairbanks 27 William Howard Taft (1857–1930) March 4, 1909 – March 4, 1913 Republican 1908 James S. Sherman Vacant after October 30, 1912 28 Woodrow Wilson (1856–1924) March 4, 1913\n\n[NEW 12] ) 1st term March 4, 1885 – March 4, 1889 Democratic 1884 Thomas A. Hendricks Vacant after November 25, 1885 23 Benjamin Harrison (1833–1901) March 4, 1889 – March 4, 1893 Republican 1888 Levi P. Morton 24 Grover Cleveland (1837–1908) 2nd term March 4, 1893 – March 4, 1897 Democratic 1892 Adlai Stevenson I 25 William McKinley (1843–1901) March 4, 1897 – September 14, 1901 Republican 1896 Garret Hobart Vacant after November 21, 1899 1900 Theodore Roosevelt 26 Theodore Roosevelt (1858–1919) September 14, 1901 – March 4, 1909 Republican – Vacant through March 4, 1905 1904 Charles W. Fairbanks 27 William Howard Taft (1857–1930) March 4, 1909 – March 4, 1913 Republican 1908 James S. Sherman Vacant after October 30, 1912 28 Woodrow Wilson (1856–1924) March 4, 1913\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 12 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2812, + "osl": 1315, + "total_tokens": 4127, + "latency_ms": 15412.05, + "tokens_per_second": 85.32 + }, + "context": { + "num_documents_evaluated": 12, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "427cd9d3-709b-408e-b16f-025b4724d782", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:03:29.116945Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: If Princess Diana had been born three years earlier, who would have been Prime Minister when she was ten? \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Prime Minister of the United Kingdom from 1970 to 1974 The Right Honourable Sir Edward Heath KG MBE Portrait by Allan Warren , 1987 Prime Minister of the United Kingdom In office 19 June 1970 – 4 March 1974 Monarch Elizabeth II Preceded by Harold Wilson Succeeded by Harold Wilson Senior political offices Leader of the Opposition In office 4 March 1974 – 11 February 1975 Monarch Elizabeth II Prime Minister Harold Wilson Preceded by Harold Wilson Succeeded by Margaret Thatcher In office 28 July 1965 – 19 June 1970 Monarch Elizabeth II Prime Minister Harold Wilson Deputy Reginald Maudling Preceded by Alec Douglas-Home Succeeded by Harold Wilson Leader of the Conservative Party In office 28 July 1965 – 11 February 1975 Deputy Reginald Maudling (1965–1972) Preced\n\n[DOC 2] Prime Minister of the United Kingdom from 1979 to 1990 The Right Honourable The Baroness Thatcher LG OM DStJ PC FRS HonFRSC Studio portrait, c. 1995–96 Prime Minister of the United Kingdom In office 4 May 1979 – 28 November 1990 Monarch Elizabeth II Deputy Geoffrey Howe (1989–90) Preceded by James Callaghan Succeeded by John Major Leader of the Opposition In office 11 February 1975 – 4 May 1979 Monarch Elizabeth II Prime Minister Harold Wilson James Callaghan Deputy William Whitelaw Preceded by Edward Heath Succeeded by James Callaghan Leader of the Conservative Party In office 11 February 1975 – 28 November 1990 Deputy The Viscount Whitelaw Chairman See list The Lord Thorneycroft Cecil Parkinson John Gummer Norman Tebbit Peter Brooke Kenneth Baker Preceded\n\n[DOC 3] e Civil Service (1968–1970) Labour Wilson I 1966 Wilson II Edward Heath MP for Bexley (1916–2005) 19 June 1970 4 March 1974 3 years, 259 days 1970 First Lord of the Treasury Minister for the Civil Service Conservative Heath Harold Wilson MP for Huyton (1916–1995) Premiership 4 March 1974 5 April 1976 2 years, 33 days ( Feb. 1974 ) First Lord of the Treasury Minister for the Civil Service Labour Wilson III Oct. 1974 Wilson IV James Callaghan MP for Cardiff South East (1912–2005) 5 April 1976 4 May 1979 3 years, 30 days — First Lord of the Treasury Minister for the Civil Service Callaghan Margaret Thatcher MP for Finchley (1925–2013) Premiership 4 May 1979 28 November 1990 11 years, 209 days 1979 First Lord of the Treasury Minister for the Civil Service Conser\n\n[DOC 4] 65) The Gambia Prime Minister (1965–1970), President (1970–1994) Yusuf Lule Uganda President (1979) Fawzi Mulki Jordan Prime Minister (1953–1954) Lord Dunrossil Australia Governor-General (1960–1961) Daniel Chanis Pinzón Panama President (1949) Julius Nyerere Tanganyika Chief Minister (1960–1961), Prime Minister (1961–1962), President (1962–1964) Tanzania President (1964–1985) Paul Reeves New Zealand Governor-General (1985–1990) Lord John Russell United Kingdom Prime Minister (1846–1852; 1865–1866) Sir Ninian Stephen Australia Governor-General (1982-1989) John Swinney Scotland First Minister (2024–) Lord Palmerston United Kingdom Prime Minister (1855–1858; 1859–1865) Sir Charles Tupper Canada Prime Minister (1896) William Walker Nicaragua Filibuster of Nicar\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Missing: Princess Diana's birth date is not provided in any kept document, which is required to calculate the year she would have turned ten after shifting her birth three years earlier. Without that date, we cannot determine which Prime Minister was in office at that time.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1073, + "osl": 67, + "total_tokens": 1140, + "latency_ms": 5830.13, + "tokens_per_second": 11.49 + }, + "context": { + "kept_docs_count": 4, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "6ee873c6-df6f-4cb4-8365-45611688a8aa", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:03:35.320236Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: If Princess Diana had been born three years earlier, who would have been Prime Minister when she was ten? \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Prime Minister of the United Kingdom from 1970 to 1974 The Right Honourable Sir Edward Heath KG MBE Portrait by Allan Warren , 1987 Prime Minister of the United Kingdom In office 19 June 1970 – 4 March 1974 Monarch Elizabeth II Preceded by Harold Wilson Succeeded by Harold Wilson Senior political offices Leader of the Opposition In office 4 March 1974 – 11 February 1975 Monarch Elizabeth II Prime Minister Harold Wilson Preceded by Harold Wilson Succeeded by Margaret Thatcher In office 28 July 1965 – 19 June 1970 Monarch Elizabeth II Prime Minister Harold Wilson Deputy Reginald Maudling Preceded by Alec Douglas-Home Succeeded by Harold Wilson Leader of the Conservative Party In office 28 July 1965 – 11 February 1975 Deputy Reginald Maudling (1965–1972) Preced\n\n[DOC 2] Prime Minister of the United Kingdom from 1979 to 1990 The Right Honourable The Baroness Thatcher LG OM DStJ PC FRS HonFRSC Studio portrait, c. 1995–96 Prime Minister of the United Kingdom In office 4 May 1979 – 28 November 1990 Monarch Elizabeth II Deputy Geoffrey Howe (1989–90) Preceded by James Callaghan Succeeded by John Major Leader of the Opposition In office 11 February 1975 – 4 May 1979 Monarch Elizabeth II Prime Minister Harold Wilson James Callaghan Deputy William Whitelaw Preceded by Edward Heath Succeeded by James Callaghan Leader of the Conservative Party In office 11 February 1975 – 28 November 1990 Deputy The Viscount Whitelaw Chairman See list The Lord Thorneycroft Cecil Parkinson John Gummer Norman Tebbit Peter Brooke Kenneth Baker Preceded\n\n[DOC 3] e Civil Service (1968–1970) Labour Wilson I 1966 Wilson II Edward Heath MP for Bexley (1916–2005) 19 June 1970 4 March 1974 3 years, 259 days 1970 First Lord of the Treasury Minister for the Civil Service Conservative Heath Harold Wilson MP for Huyton (1916–1995) Premiership 4 March 1974 5 April 1976 2 years, 33 days ( Feb. 1974 ) First Lord of the Treasury Minister for the Civil Service Labour Wilson III Oct. 1974 Wilson IV James Callaghan MP for Cardiff South East (1912–2005) 5 April 1976 4 May 1979 3 years, 30 days — First Lord of the Treasury Minister for the Civil Service Callaghan Margaret Thatcher MP for Finchley (1925–2013) Premiership 4 May 1979 28 November 1990 11 years, 209 days 1979 First Lord of the Treasury Minister for the Civil Service Conser\n\n[DOC 4] 65) The Gambia Prime Minister (1965–1970), President (1970–1994) Yusuf Lule Uganda President (1979) Fawzi Mulki Jordan Prime Minister (1953–1954) Lord Dunrossil Australia Governor-General (1960–1961) Daniel Chanis Pinzón Panama President (1949) Julius Nyerere Tanganyika Chief Minister (1960–1961), Prime Minister (1961–1962), President (1962–1964) Tanzania President (1964–1985) Paul Reeves New Zealand Governor-General (1985–1990) Lord John Russell United Kingdom Prime Minister (1846–1852; 1865–1866) Sir Ninian Stephen Australia Governor-General (1982-1989) John Swinney Scotland First Minister (2024–) Lord Palmerston United Kingdom Prime Minister (1855–1858; 1859–1865) Sir Charles Tupper Canada Prime Minister (1896) William Walker Nicaragua Filibuster of Nicar\n\n\nSEARCH HISTORY: - Query: 'Princess Diana birth date' → 4 docs\n- Query: 'UK Prime Minister 1968' → 5 docs\n- Query: 'Harold Wilson term dates' → 3 docs\n\nFEEDBACK: We need Diana's birth year to adjust by three years and determine the year she would be ten (1968). Then identify who held the UK Prime Minister office in 1968.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Princess Diana birth\",\n \"UK Prime Minister 1968\",\n \"Harold Wilson July 1968\"\n ],\n \"feedback\": \"We have Diana's birth year (1961) and need to shift it three years earlier to 1958, making her ten in 1968. We must identify who was Prime Minister in 1968. The queries above retrieve Diana's birth date and the Prime Minister serving in 1968.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1359, + "osl": 441, + "total_tokens": 1800, + "latency_ms": 6202.65, + "tokens_per_second": 71.1 + }, + "context": { + "kept_docs_count": 4, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "141ae342-52a3-443b-bbbf-b9254372dcb8", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T03:03:38.112758Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: If Princess Diana had been born three years earlier, who would have been Prime Minister when she was ten? \n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] even-bedroom property adjoining the Princess Royal's Gatcombe Park estate. Their first child, Mia Grace, was born on 17 January 2014 at Gloucestershire Royal Hospital and was 16th, later 23rd in the line of succession. A second daughter, Lena Elizabeth, was born on 18 June 2018 at Stroud Maternity Hospital and was 19th, later 24th, in the line of succession. Their son, Lucas Philip, was born on 21 March 2021 at the family home on the Gatcombe Park estate and was 22nd, later 25th, in the line of succession. Tindall is a godmother to Prince George of Wales , the son of her cousin William, Prince of Wales . In December 2000, before her marriage, Phillips was involved in a serious car crash near Bourton-on-the-Water , escaping injury after overturning her Land R\n\n[NEW 2] Month July 1997 Succeeded by Bernie Williams\n\n[NEW 3] bruary 27, 1969] - Reconstruction\n\n[NEW 4] 5–1943 1946–1951 March 11, 1995 October 15, 2016 1 Johnny Bower G 1958–1969 March 11, 1995 October 15, 2016 4 Hap Day D 1924–1937 October 4, 2006 October 15, 2016 4 Red Kelly C 1960–1967 October 4, 2006 October 15, 2016 5 Bill Barilko D 1945–1951 Not honoured October 17, 1992 6 Ace Bailey RW 1926–1933 Not honoured February 14, 1934 7 King Clancy D 1930–1937 November 21, 1995 October 15, 2016 7 Tim Horton D 1949–1970 November 21, 1995 October 15, 2016 9 Charlie Conacher RW 1929–1938 February 28, 1998 October 15, 2016 9 Ted Kennedy C 1942–1955 1956–1957 October 3, 1993 October 15, 2016 10 Syl Apps C 1936–1943 1945–1948 October 3, 1993 October 15, 2016 10 George Armstrong RW 1949–1971 February 28, 1998 October 15, 2016 13 Mats Sundin C 1994–2008 February 11, 20\n\n\nKEPT DOCUMENTS (context only):\n[4 documents already kept as relevant]\n[KEPT 1] Prime Minister of the United Kingdom from 1970 to 1974 The Right Honourable Sir Edward Heath KG MBE Portrait by Allan Warren , 1987 Prime Minister of the United Kingdom In office 19 June 1970 – 4 March 1974 Monarch Elizabeth II Preceded by Harold Wilson Succeeded by Harold Wilson Senior political of...\n[KEPT 2] Prime Minister of the United Kingdom from 1979 to 1990 The Right Honourable The Baroness Thatcher LG OM DStJ PC FRS HonFRSC Studio portrait, c. 1995–96 Prime Minister of the United Kingdom In office 4 May 1979 – 28 November 1990 Monarch Elizabeth II Deputy Geoffrey Howe (1989–90) Preceded by James C...\n[KEPT 3] e Civil Service (1968–1970) Labour Wilson I 1966 Wilson II Edward Heath MP for Bexley (1916–2005) 19 June 1970 4 March 1974 3 years, 259 days 1970 First Lord of the Treasury Minister for the Civil Service Conservative Heath Harold Wilson MP for Huyton (1916–1995) Premiership 4 March 1974 5 April 197...\n[KEPT 4] 65) The Gambia Prime Minister (1965–1970), President (1970–1994) Yusuf Lule Uganda President (1979) Fawzi Mulki Jordan Prime Minister (1953–1954) Lord Dunrossil Australia Governor-General (1960–1961) Daniel Chanis Pinzón Panama President (1949) Julius Nyerere Tanganyika Chief Minister (1960–1961), P...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 4 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1183, + "osl": 361, + "total_tokens": 1544, + "latency_ms": 2259.8, + "tokens_per_second": 159.75 + }, + "context": { + "num_documents_evaluated": 4, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 4 + } + }, + { + "call_id": "59b7b252-b7db-4a0c-8a72-52753a744082", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:03:41.308765Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: If Princess Diana had been born three years earlier, who would have been Prime Minister when she was ten? \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Prime Minister of the United Kingdom from 1970 to 1974 The Right Honourable Sir Edward Heath KG MBE Portrait by Allan Warren , 1987 Prime Minister of the United Kingdom In office 19 June 1970 – 4 March 1974 Monarch Elizabeth II Preceded by Harold Wilson Succeeded by Harold Wilson Senior political offices Leader of the Opposition In office 4 March 1974 – 11 February 1975 Monarch Elizabeth II Prime Minister Harold Wilson Preceded by Harold Wilson Succeeded by Margaret Thatcher In office 28 July 1965 – 19 June 1970 Monarch Elizabeth II Prime Minister Harold Wilson Deputy Reginald Maudling Preceded by Alec Douglas-Home Succeeded by Harold Wilson Leader of the Conservative Party In office 28 July 1965 – 11 February 1975 Deputy Reginald Maudling (1965–1972) Preced\n\n[DOC 2] Prime Minister of the United Kingdom from 1979 to 1990 The Right Honourable The Baroness Thatcher LG OM DStJ PC FRS HonFRSC Studio portrait, c. 1995–96 Prime Minister of the United Kingdom In office 4 May 1979 – 28 November 1990 Monarch Elizabeth II Deputy Geoffrey Howe (1989–90) Preceded by James Callaghan Succeeded by John Major Leader of the Opposition In office 11 February 1975 – 4 May 1979 Monarch Elizabeth II Prime Minister Harold Wilson James Callaghan Deputy William Whitelaw Preceded by Edward Heath Succeeded by James Callaghan Leader of the Conservative Party In office 11 February 1975 – 28 November 1990 Deputy The Viscount Whitelaw Chairman See list The Lord Thorneycroft Cecil Parkinson John Gummer Norman Tebbit Peter Brooke Kenneth Baker Preceded\n\n[DOC 3] e Civil Service (1968–1970) Labour Wilson I 1966 Wilson II Edward Heath MP for Bexley (1916–2005) 19 June 1970 4 March 1974 3 years, 259 days 1970 First Lord of the Treasury Minister for the Civil Service Conservative Heath Harold Wilson MP for Huyton (1916–1995) Premiership 4 March 1974 5 April 1976 2 years, 33 days ( Feb. 1974 ) First Lord of the Treasury Minister for the Civil Service Labour Wilson III Oct. 1974 Wilson IV James Callaghan MP for Cardiff South East (1912–2005) 5 April 1976 4 May 1979 3 years, 30 days — First Lord of the Treasury Minister for the Civil Service Callaghan Margaret Thatcher MP for Finchley (1925–2013) Premiership 4 May 1979 28 November 1990 11 years, 209 days 1979 First Lord of the Treasury Minister for the Civil Service Conser\n\n[DOC 4] 65) The Gambia Prime Minister (1965–1970), President (1970–1994) Yusuf Lule Uganda President (1979) Fawzi Mulki Jordan Prime Minister (1953–1954) Lord Dunrossil Australia Governor-General (1960–1961) Daniel Chanis Pinzón Panama President (1949) Julius Nyerere Tanganyika Chief Minister (1960–1961), Prime Minister (1961–1962), President (1962–1964) Tanzania President (1964–1985) Paul Reeves New Zealand Governor-General (1985–1990) Lord John Russell United Kingdom Prime Minister (1846–1852; 1865–1866) Sir Ninian Stephen Australia Governor-General (1982-1989) John Swinney Scotland First Minister (2024–) Lord Palmerston United Kingdom Prime Minister (1855–1858; 1859–1865) Sir Charles Tupper Canada Prime Minister (1896) William Walker Nicaragua Filibuster of Nicar\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents provide the dates of Harold Wilson's premiership (28 July 1965 – 19 June 1970), which covers the target year 1968. However, they do not contain Princess Diana's birth date (1 July 1961) needed to calculate the adjusted birth year (1958) and the year she would be ten (1968). Without Diana's birth year, we cannot definitively link the date to a specific Prime Minister.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1073, + "osl": 121, + "total_tokens": 1194, + "latency_ms": 3195.22, + "tokens_per_second": 37.87 + }, + "context": { + "kept_docs_count": 4, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "bd95c0cc-0fd5-464e-8ed5-eb405428c596", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:03:44.751992Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: If Princess Diana had been born three years earlier, who would have been Prime Minister when she was ten? \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Prime Minister of the United Kingdom from 1970 to 1974 The Right Honourable Sir Edward Heath KG MBE Portrait by Allan Warren , 1987 Prime Minister of the United Kingdom In office 19 June 1970 – 4 March 1974 Monarch Elizabeth II Preceded by Harold Wilson Succeeded by Harold Wilson Senior political offices Leader of the Opposition In office 4 March 1974 – 11 February 1975 Monarch Elizabeth II Prime Minister Harold Wilson Preceded by Harold Wilson Succeeded by Margaret Thatcher In office 28 July 1965 – 19 June 1970 Monarch Elizabeth II Prime Minister Harold Wilson Deputy Reginald Maudling Preceded by Alec Douglas-Home Succeeded by Harold Wilson Leader of the Conservative Party In office 28 July 1965 – 11 February 1975 Deputy Reginald Maudling (1965–1972) Preced\n\n[DOC 2] Prime Minister of the United Kingdom from 1979 to 1990 The Right Honourable The Baroness Thatcher LG OM DStJ PC FRS HonFRSC Studio portrait, c. 1995–96 Prime Minister of the United Kingdom In office 4 May 1979 – 28 November 1990 Monarch Elizabeth II Deputy Geoffrey Howe (1989–90) Preceded by James Callaghan Succeeded by John Major Leader of the Opposition In office 11 February 1975 – 4 May 1979 Monarch Elizabeth II Prime Minister Harold Wilson James Callaghan Deputy William Whitelaw Preceded by Edward Heath Succeeded by James Callaghan Leader of the Conservative Party In office 11 February 1975 – 28 November 1990 Deputy The Viscount Whitelaw Chairman See list The Lord Thorneycroft Cecil Parkinson John Gummer Norman Tebbit Peter Brooke Kenneth Baker Preceded\n\n[DOC 3] e Civil Service (1968–1970) Labour Wilson I 1966 Wilson II Edward Heath MP for Bexley (1916–2005) 19 June 1970 4 March 1974 3 years, 259 days 1970 First Lord of the Treasury Minister for the Civil Service Conservative Heath Harold Wilson MP for Huyton (1916–1995) Premiership 4 March 1974 5 April 1976 2 years, 33 days ( Feb. 1974 ) First Lord of the Treasury Minister for the Civil Service Labour Wilson III Oct. 1974 Wilson IV James Callaghan MP for Cardiff South East (1912–2005) 5 April 1976 4 May 1979 3 years, 30 days — First Lord of the Treasury Minister for the Civil Service Callaghan Margaret Thatcher MP for Finchley (1925–2013) Premiership 4 May 1979 28 November 1990 11 years, 209 days 1979 First Lord of the Treasury Minister for the Civil Service Conser\n\n[DOC 4] 65) The Gambia Prime Minister (1965–1970), President (1970–1994) Yusuf Lule Uganda President (1979) Fawzi Mulki Jordan Prime Minister (1953–1954) Lord Dunrossil Australia Governor-General (1960–1961) Daniel Chanis Pinzón Panama President (1949) Julius Nyerere Tanganyika Chief Minister (1960–1961), Prime Minister (1961–1962), President (1962–1964) Tanzania President (1964–1985) Paul Reeves New Zealand Governor-General (1985–1990) Lord John Russell United Kingdom Prime Minister (1846–1852; 1865–1866) Sir Ninian Stephen Australia Governor-General (1982-1989) John Swinney Scotland First Minister (2024–) Lord Palmerston United Kingdom Prime Minister (1855–1858; 1859–1865) Sir Charles Tupper Canada Prime Minister (1896) William Walker Nicaragua Filibuster of Nicar\n\n\nSEARCH HISTORY: - Query: 'Princess Diana birth date' → 4 docs\n- Query: 'UK Prime Minister 1968' → 5 docs\n- Query: 'Harold Wilson term dates' → 3 docs\n- Query: 'Princess Diana birth' → 1 docs\n- Query: 'UK Prime Minister 1968' → 0 docs\n- Query: 'Harold Wilson July 1968' → 3 docs\n\nFEEDBACK: We need Diana's birth year to adjust by three years and determine the year she would be ten (1968). Then identify who held the UK Prime Minister office in 1968.\nWe have Diana's birth year (1961) and need to shift it three years earlier to 1958, making her ten in 1968. We must identify who was Prime Minister in 1968. The queries above retrieve Diana's birth date and the Prime Minister serving in 1968.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Princess Diana birth year\",\n \"Harold Wilson prime minister 1968\",\n \"List of prime ministers of the United Kingdom\"\n ],\n \"feedback\": \"We need Diana's birth year to compute the adjusted ten‑year point (1968) and then identify who held the UK Prime Minister office in that year. The first query confirms Diana's birth year (1961). The second query directly targets the incumbent Prime Minister in 1968. The third query provides a complete chronology of UK Prime Ministers to cross‑verify the answer.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1475, + "osl": 468, + "total_tokens": 1943, + "latency_ms": 3442.56, + "tokens_per_second": 135.95 + }, + "context": { + "kept_docs_count": 4, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "90f28855-20a8-46b7-8f17-14e859317f77", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T03:03:46.704053Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: If Princess Diana had been born three years earlier, who would have been Prime Minister when she was ten? \n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Prime Minister of the United Kingdom from 2019 to 2022 The Right Honourable Boris Johnson Hon FRIBA Official portrait, 2019 Prime Minister of the United Kingdom In office 24 July 2019 – 6 September 2022 Monarch Elizabeth II Deputy Dominic Raab Preceded by Theresa May Succeeded by Liz Truss Leader of the Conservative Party In office 23 July 2019 – 5 September 2022 Preceded by Theresa May Succeeded by Liz Truss Secretary of State for Foreign and Commonwealth Affairs In office 13 July 2016 – 9 July 2018 Prime Minister Theresa May Preceded by Philip Hammond Succeeded by Jeremy Hunt Mayor of London In office 3 May 2008 – 9 May 2016 Deputy Richard Barnes Victoria Borwick Roger Evans Preceded by Ken Livingstone Succeeded by Sadiq Khan Member of Parliament In office\n\n[NEW 2] Prime Minister of the United Kingdom from 2016 to 2019 The Right Honourable The Baroness May of Maidenhead PC Official portrait, 2024 Prime Minister of the United Kingdom In office 13 July 2016 – 24 July 2019 Monarch Elizabeth II First Secretary Damian Green (2017) Preceded by David Cameron Succeeded by Boris Johnson Ministerial offices Home Secretary In office 12 May 2010 – 13 July 2016 Prime Minister David Cameron Preceded by Alan Johnson Succeeded by Amber Rudd Minister for Women and Equalities In office 12 May 2010 – 4 September 2012 Prime Minister David Cameron Preceded by Harriet Harman Succeeded by Maria Miller Party political offices Leader of the Conservative Party In office 11 July 2016 – 23 July 2019 Preceded by David Cameron Succeeded by Boris Jo\n\n[NEW 3] Prime Minister of the United Kingdom from 1997 to 2007 The Right Honourable Sir Tony Blair KG Blair in 2010 Prime Minister of the United Kingdom In office 2 May 1997 – 27 June 2007 Monarch Elizabeth II Deputy John Prescott Preceded by John Major Succeeded by Gordon Brown Executive Chairman of the Tony Blair Institute for Global Change Current Assumed role 1 December 2016 Preceded by Role established Special Envoy of the Quartet on the Middle East In office 27 June 2007 – 27 May 2015 Preceded by James Wolfensohn Succeeded by Kito de Boer Senior political offices 1994‍–‍2007 Leader of the Opposition In office 21 July 1994 – 2 May 1997 Monarch Elizabeth II Prime Minister John Major Deputy John Prescott Preceded by Margaret Beckett Succeeded by John Major Leader\n\n\nKEPT DOCUMENTS (context only):\n[4 documents already kept as relevant]\n[KEPT 1] Prime Minister of the United Kingdom from 1970 to 1974 The Right Honourable Sir Edward Heath KG MBE Portrait by Allan Warren , 1987 Prime Minister of the United Kingdom In office 19 June 1970 – 4 March 1974 Monarch Elizabeth II Preceded by Harold Wilson Succeeded by Harold Wilson Senior political of...\n[KEPT 2] Prime Minister of the United Kingdom from 1979 to 1990 The Right Honourable The Baroness Thatcher LG OM DStJ PC FRS HonFRSC Studio portrait, c. 1995–96 Prime Minister of the United Kingdom In office 4 May 1979 – 28 November 1990 Monarch Elizabeth II Deputy Geoffrey Howe (1989–90) Preceded by James C...\n[KEPT 3] e Civil Service (1968–1970) Labour Wilson I 1966 Wilson II Edward Heath MP for Bexley (1916–2005) 19 June 1970 4 March 1974 3 years, 259 days 1970 First Lord of the Treasury Minister for the Civil Service Conservative Heath Harold Wilson MP for Huyton (1916–1995) Premiership 4 March 1974 5 April 197...\n[KEPT 4] 65) The Gambia Prime Minister (1965–1970), President (1970–1994) Yusuf Lule Uganda President (1979) Fawzi Mulki Jordan Prime Minister (1953–1954) Lord Dunrossil Australia Governor-General (1960–1961) Daniel Chanis Pinzón Panama President (1949) Julius Nyerere Tanganyika Chief Minister (1960–1961), P...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 3 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 1, 1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1196, + "osl": 818, + "total_tokens": 2014, + "latency_ms": 1467.49, + "tokens_per_second": 557.42 + }, + "context": { + "num_documents_evaluated": 3, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 4 + } + }, + { + "call_id": "9a9cf845-0853-44b5-85d4-1603b48c1d1f", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:03:47.672135Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: If Princess Diana had been born three years earlier, who would have been Prime Minister when she was ten? \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Prime Minister of the United Kingdom from 1970 to 1974 The Right Honourable Sir Edward Heath KG MBE Portrait by Allan Warren , 1987 Prime Minister of the United Kingdom In office 19 June 1970 – 4 March 1974 Monarch Elizabeth II Preceded by Harold Wilson Succeeded by Harold Wilson Senior political offices Leader of the Opposition In office 4 March 1974 – 11 February 1975 Monarch Elizabeth II Prime Minister Harold Wilson Preceded by Harold Wilson Succeeded by Margaret Thatcher In office 28 July 1965 – 19 June 1970 Monarch Elizabeth II Prime Minister Harold Wilson Deputy Reginald Maudling Preceded by Alec Douglas-Home Succeeded by Harold Wilson Leader of the Conservative Party In office 28 July 1965 – 11 February 1975 Deputy Reginald Maudling (1965–1972) Preced\n\n[DOC 2] Prime Minister of the United Kingdom from 1979 to 1990 The Right Honourable The Baroness Thatcher LG OM DStJ PC FRS HonFRSC Studio portrait, c. 1995–96 Prime Minister of the United Kingdom In office 4 May 1979 – 28 November 1990 Monarch Elizabeth II Deputy Geoffrey Howe (1989–90) Preceded by James Callaghan Succeeded by John Major Leader of the Opposition In office 11 February 1975 – 4 May 1979 Monarch Elizabeth II Prime Minister Harold Wilson James Callaghan Deputy William Whitelaw Preceded by Edward Heath Succeeded by James Callaghan Leader of the Conservative Party In office 11 February 1975 – 28 November 1990 Deputy The Viscount Whitelaw Chairman See list The Lord Thorneycroft Cecil Parkinson John Gummer Norman Tebbit Peter Brooke Kenneth Baker Preceded\n\n[DOC 3] e Civil Service (1968–1970) Labour Wilson I 1966 Wilson II Edward Heath MP for Bexley (1916–2005) 19 June 1970 4 March 1974 3 years, 259 days 1970 First Lord of the Treasury Minister for the Civil Service Conservative Heath Harold Wilson MP for Huyton (1916–1995) Premiership 4 March 1974 5 April 1976 2 years, 33 days ( Feb. 1974 ) First Lord of the Treasury Minister for the Civil Service Labour Wilson III Oct. 1974 Wilson IV James Callaghan MP for Cardiff South East (1912–2005) 5 April 1976 4 May 1979 3 years, 30 days — First Lord of the Treasury Minister for the Civil Service Callaghan Margaret Thatcher MP for Finchley (1925–2013) Premiership 4 May 1979 28 November 1990 11 years, 209 days 1979 First Lord of the Treasury Minister for the Civil Service Conser\n\n[DOC 4] 65) The Gambia Prime Minister (1965–1970), President (1970–1994) Yusuf Lule Uganda President (1979) Fawzi Mulki Jordan Prime Minister (1953–1954) Lord Dunrossil Australia Governor-General (1960–1961) Daniel Chanis Pinzón Panama President (1949) Julius Nyerere Tanganyika Chief Minister (1960–1961), Prime Minister (1961–1962), President (1962–1964) Tanzania President (1964–1985) Paul Reeves New Zealand Governor-General (1985–1990) Lord John Russell United Kingdom Prime Minister (1846–1852; 1865–1866) Sir Ninian Stephen Australia Governor-General (1982-1989) John Swinney Scotland First Minister (2024–) Lord Palmerston United Kingdom Prime Minister (1855–1858; 1859–1865) Sir Charles Tupper Canada Prime Minister (1896) William Walker Nicaragua Filibuster of Nicar\n\n[DOC 5] Prime Minister of the United Kingdom from 2019 to 2022 The Right Honourable Boris Johnson Hon FRIBA Official portrait, 2019 Prime Minister of the United Kingdom In office 24 July 2019 – 6 September 2022 Monarch Elizabeth II Deputy Dominic Raab Preceded by Theresa May Succeeded by Liz Truss Leader of the Conservative Party In office 23 July 2019 – 5 September 2022 Preceded by Theresa May Succeeded by Liz Truss Secretary of State for Foreign and Commonwealth Affairs In office 13 July 2016 – 9 July 2018 Prime Minister Theresa May Preceded by Philip Hammond Succeeded by Jeremy Hunt Mayor of London In office 3 May 2008 – 9 May 2016 Deputy Richard Barnes Victoria Borwick Roger Evans Preceded by Ken Livingstone Succeeded by Sadiq Khan Member of Parliament In office\n\n[DOC 6] Prime Minister of the United Kingdom from 2016 to 2019 The Right Honourable The Baroness May of Maidenhead PC Official portrait, 2024 Prime Minister of the United Kingdom In office 13 July 2016 – 24 July 2019 Monarch Elizabeth II First Secretary Damian Green (2017) Preceded by David Cameron Succeeded by Boris Johnson Ministerial offices Home Secretary In office 12 May 2010 – 13 July 2016 Prime Minister David Cameron Preceded by Alan Johnson Succeeded by Amber Rudd Minister for Women and Equalities In office 12 May 2010 – 4 September 2012 Prime Minister David Cameron Preceded by Harriet Harman Succeeded by Maria Miller Party political offices Leader of the Conservative Party In office 11 July 2016 – 23 July 2019 Preceded by David Cameron Succeeded by Boris Jo\n\n[DOC 7] Prime Minister of the United Kingdom from 1997 to 2007 The Right Honourable Sir Tony Blair KG Blair in 2010 Prime Minister of the United Kingdom In office 2 May 1997 – 27 June 2007 Monarch Elizabeth II Deputy John Prescott Preceded by John Major Succeeded by Gordon Brown Executive Chairman of the Tony Blair Institute for Global Change Current Assumed role 1 December 2016 Preceded by Role established Special Envoy of the Quartet on the Middle East In office 27 June 2007 – 27 May 2015 Preceded by James Wolfensohn Succeeded by Kito de Boer Senior political offices 1994‍–‍2007 Leader of the Opposition In office 21 July 1994 – 2 May 1997 Monarch Elizabeth II Prime Minister John Major Deputy John Prescott Preceded by Margaret Beckett Succeeded by John Major Leader\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Missing: Princess Diana's birth date (required to calculate the year she would be ten). The provided documents only contain information about UK Prime Ministers and their terms, but no data on Diana's birth year.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1698, + "osl": 331, + "total_tokens": 2029, + "latency_ms": 967.41, + "tokens_per_second": 342.15 + }, + "context": { + "kept_docs_count": 7, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "42266762-5759-45e8-ab29-9bfb744c5e32", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:03:53.182010Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: If Princess Diana had been born three years earlier, who would have been Prime Minister when she was ten? \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Prime Minister of the United Kingdom from 1970 to 1974 The Right Honourable Sir Edward Heath KG MBE Portrait by Allan Warren , 1987 Prime Minister of the United Kingdom In office 19 June 1970 – 4 March 1974 Monarch Elizabeth II Preceded by Harold Wilson Succeeded by Harold Wilson Senior political offices Leader of the Opposition In office 4 March 1974 – 11 February 1975 Monarch Elizabeth II Prime Minister Harold Wilson Preceded by Harold Wilson Succeeded by Margaret Thatcher In office 28 July 1965 – 19 June 1970 Monarch Elizabeth II Prime Minister Harold Wilson Deputy Reginald Maudling Preceded by Alec Douglas-Home Succeeded by Harold Wilson Leader of the Conservative Party In office 28 July 1965 – 11 February 1975 Deputy Reginald Maudling (1965–1972) Preced\n\n[DOC 2] Prime Minister of the United Kingdom from 1979 to 1990 The Right Honourable The Baroness Thatcher LG OM DStJ PC FRS HonFRSC Studio portrait, c. 1995–96 Prime Minister of the United Kingdom In office 4 May 1979 – 28 November 1990 Monarch Elizabeth II Deputy Geoffrey Howe (1989–90) Preceded by James Callaghan Succeeded by John Major Leader of the Opposition In office 11 February 1975 – 4 May 1979 Monarch Elizabeth II Prime Minister Harold Wilson James Callaghan Deputy William Whitelaw Preceded by Edward Heath Succeeded by James Callaghan Leader of the Conservative Party In office 11 February 1975 – 28 November 1990 Deputy The Viscount Whitelaw Chairman See list The Lord Thorneycroft Cecil Parkinson John Gummer Norman Tebbit Peter Brooke Kenneth Baker Preceded\n\n[DOC 3] e Civil Service (1968–1970) Labour Wilson I 1966 Wilson II Edward Heath MP for Bexley (1916–2005) 19 June 1970 4 March 1974 3 years, 259 days 1970 First Lord of the Treasury Minister for the Civil Service Conservative Heath Harold Wilson MP for Huyton (1916–1995) Premiership 4 March 1974 5 April 1976 2 years, 33 days ( Feb. 1974 ) First Lord of the Treasury Minister for the Civil Service Labour Wilson III Oct. 1974 Wilson IV James Callaghan MP for Cardiff South East (1912–2005) 5 April 1976 4 May 1979 3 years, 30 days — First Lord of the Treasury Minister for the Civil Service Callaghan Margaret Thatcher MP for Finchley (1925–2013) Premiership 4 May 1979 28 November 1990 11 years, 209 days 1979 First Lord of the Treasury Minister for the Civil Service Conser\n\n[DOC 4] 65) The Gambia Prime Minister (1965–1970), President (1970–1994) Yusuf Lule Uganda President (1979) Fawzi Mulki Jordan Prime Minister (1953–1954) Lord Dunrossil Australia Governor-General (1960–1961) Daniel Chanis Pinzón Panama President (1949) Julius Nyerere Tanganyika Chief Minister (1960–1961), Prime Minister (1961–1962), President (1962–1964) Tanzania President (1964–1985) Paul Reeves New Zealand Governor-General (1985–1990) Lord John Russell United Kingdom Prime Minister (1846–1852; 1865–1866) Sir Ninian Stephen Australia Governor-General (1982-1989) John Swinney Scotland First Minister (2024–) Lord Palmerston United Kingdom Prime Minister (1855–1858; 1859–1865) Sir Charles Tupper Canada Prime Minister (1896) William Walker Nicaragua Filibuster of Nicar\n\n[DOC 5] Prime Minister of the United Kingdom from 2019 to 2022 The Right Honourable Boris Johnson Hon FRIBA Official portrait, 2019 Prime Minister of the United Kingdom In office 24 July 2019 – 6 September 2022 Monarch Elizabeth II Deputy Dominic Raab Preceded by Theresa May Succeeded by Liz Truss Leader of the Conservative Party In office 23 July 2019 – 5 September 2022 Preceded by Theresa May Succeeded by Liz Truss Secretary of State for Foreign and Commonwealth Affairs In office 13 July 2016 – 9 July 2018 Prime Minister Theresa May Preceded by Philip Hammond Succeeded by Jeremy Hunt Mayor of London In office 3 May 2008 – 9 May 2016 Deputy Richard Barnes Victoria Borwick Roger Evans Preceded by Ken Livingstone Succeeded by Sadiq Khan Member of Parliament In office\n\n[DOC 6] Prime Minister of the United Kingdom from 2016 to 2019 The Right Honourable The Baroness May of Maidenhead PC Official portrait, 2024 Prime Minister of the United Kingdom In office 13 July 2016 – 24 July 2019 Monarch Elizabeth II First Secretary Damian Green (2017) Preceded by David Cameron Succeeded by Boris Johnson Ministerial offices Home Secretary In office 12 May 2010 – 13 July 2016 Prime Minister David Cameron Preceded by Alan Johnson Succeeded by Amber Rudd Minister for Women and Equalities In office 12 May 2010 – 4 September 2012 Prime Minister David Cameron Preceded by Harriet Harman Succeeded by Maria Miller Party political offices Leader of the Conservative Party In office 11 July 2016 – 23 July 2019 Preceded by David Cameron Succeeded by Boris Jo\n\n[DOC 7] Prime Minister of the United Kingdom from 1997 to 2007 The Right Honourable Sir Tony Blair KG Blair in 2010 Prime Minister of the United Kingdom In office 2 May 1997 – 27 June 2007 Monarch Elizabeth II Deputy John Prescott Preceded by John Major Succeeded by Gordon Brown Executive Chairman of the Tony Blair Institute for Global Change Current Assumed role 1 December 2016 Preceded by Role established Special Envoy of the Quartet on the Middle East In office 27 June 2007 – 27 May 2015 Preceded by James Wolfensohn Succeeded by Kito de Boer Senior political offices 1994‍–‍2007 Leader of the Opposition In office 21 July 1994 – 2 May 1997 Monarch Elizabeth II Prime Minister John Major Deputy John Prescott Preceded by Margaret Beckett Succeeded by John Major Leader\n\n\nSEARCH HISTORY: - Query: 'Princess Diana birth date' → 4 docs\n- Query: 'UK Prime Minister 1968' → 5 docs\n- Query: 'Harold Wilson term dates' → 3 docs\n- Query: 'Princess Diana birth' → 1 docs\n- Query: 'UK Prime Minister 1968' → 0 docs\n- Query: 'Harold Wilson July 1968' → 3 docs\n- Query: 'Princess Diana birth year' → 0 docs\n- Query: 'Harold Wilson prime minister 1968' → 0 docs\n- Query: 'List of prime ministers of the United Kingdom' → 3 docs\n\nFEEDBACK: We need Diana's birth year to adjust by three years and determine the year she would be ten (1968). Then identify who held the UK Prime Minister office in 1968.\nWe have Diana's birth year (1961) and need to shift it three years earlier to 1958, making her ten in 1968. We must identify who was Prime Minister in 1968. The queries above retrieve Diana's birth date and the Prime Minister serving in 1968.\nWe need Diana's birth year to compute the adjusted ten‑year point (1968) and then identify who held the UK Prime Minister office in that year. The first query confirms Diana's birth year (1961). The second query directly targets the incumbent Prime Minister in 1968. The third query provides a complete chronology of UK Prime Ministers to cross‑verify the answer.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Princess Diana\",\n \"United Kingdom prime minister 1968\",\n \"Harold Wilson term\"\n ],\n \"feedback\": \"We need Diana's birth year (1961) to calculate the adjusted ten‑year point (1968) and then identify who was Prime Minister in that year. The first query returns Diana's full biography with birth date. The second query directly asks for the UK Prime Minister in 1968. The third query provides Harold Wilson's term dates to confirm he was in office during 1968.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2169, + "osl": 569, + "total_tokens": 2738, + "latency_ms": 5509.32, + "tokens_per_second": 103.28 + }, + "context": { + "kept_docs_count": 7, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "9c5ae289-5110-4672-b8d3-49ce23aa0ea9", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:04:03.542934Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: If Princess Diana had been born three years earlier, who would have been Prime Minister when she was ten? \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Prime Minister of the United Kingdom from 1970 to 1974 The Right Honourable Sir Edward Heath KG MBE Portrait by Allan Warren , 1987 Prime Minister of the United Kingdom In office 19 June 1970 – 4 March 1974 Monarch Elizabeth II Preceded by Harold Wilson Succeeded by Harold Wilson Senior political offices Leader of the Opposition In office 4 March 1974 – 11 February 1975 Monarch Elizabeth II Prime Minister Harold Wilson Preceded by Harold Wilson Succeeded by Margaret Thatcher In office 28 July 1965 – 19 June 1970 Monarch Elizabeth II Prime Minister Harold Wilson Deputy Reginald Maudling Preceded by Alec Douglas-Home Succeeded by Harold Wilson Leader of the Conservative Party In office 28 July 1965 – 11 February 1975 Deputy Reginald Maudling (1965–1972) Preced\n\n[DOC 2] Prime Minister of the United Kingdom from 1979 to 1990 The Right Honourable The Baroness Thatcher LG OM DStJ PC FRS HonFRSC Studio portrait, c. 1995–96 Prime Minister of the United Kingdom In office 4 May 1979 – 28 November 1990 Monarch Elizabeth II Deputy Geoffrey Howe (1989–90) Preceded by James Callaghan Succeeded by John Major Leader of the Opposition In office 11 February 1975 – 4 May 1979 Monarch Elizabeth II Prime Minister Harold Wilson James Callaghan Deputy William Whitelaw Preceded by Edward Heath Succeeded by James Callaghan Leader of the Conservative Party In office 11 February 1975 – 28 November 1990 Deputy The Viscount Whitelaw Chairman See list The Lord Thorneycroft Cecil Parkinson John Gummer Norman Tebbit Peter Brooke Kenneth Baker Preceded\n\n[DOC 3] e Civil Service (1968–1970) Labour Wilson I 1966 Wilson II Edward Heath MP for Bexley (1916–2005) 19 June 1970 4 March 1974 3 years, 259 days 1970 First Lord of the Treasury Minister for the Civil Service Conservative Heath Harold Wilson MP for Huyton (1916–1995) Premiership 4 March 1974 5 April 1976 2 years, 33 days ( Feb. 1974 ) First Lord of the Treasury Minister for the Civil Service Labour Wilson III Oct. 1974 Wilson IV James Callaghan MP for Cardiff South East (1912–2005) 5 April 1976 4 May 1979 3 years, 30 days — First Lord of the Treasury Minister for the Civil Service Callaghan Margaret Thatcher MP for Finchley (1925–2013) Premiership 4 May 1979 28 November 1990 11 years, 209 days 1979 First Lord of the Treasury Minister for the Civil Service Conser\n\n[DOC 4] 65) The Gambia Prime Minister (1965–1970), President (1970–1994) Yusuf Lule Uganda President (1979) Fawzi Mulki Jordan Prime Minister (1953–1954) Lord Dunrossil Australia Governor-General (1960–1961) Daniel Chanis Pinzón Panama President (1949) Julius Nyerere Tanganyika Chief Minister (1960–1961), Prime Minister (1961–1962), President (1962–1964) Tanzania President (1964–1985) Paul Reeves New Zealand Governor-General (1985–1990) Lord John Russell United Kingdom Prime Minister (1846–1852; 1865–1866) Sir Ninian Stephen Australia Governor-General (1982-1989) John Swinney Scotland First Minister (2024–) Lord Palmerston United Kingdom Prime Minister (1855–1858; 1859–1865) Sir Charles Tupper Canada Prime Minister (1896) William Walker Nicaragua Filibuster of Nicar\n\n[DOC 5] Prime Minister of the United Kingdom from 2019 to 2022 The Right Honourable Boris Johnson Hon FRIBA Official portrait, 2019 Prime Minister of the United Kingdom In office 24 July 2019 – 6 September 2022 Monarch Elizabeth II Deputy Dominic Raab Preceded by Theresa May Succeeded by Liz Truss Leader of the Conservative Party In office 23 July 2019 – 5 September 2022 Preceded by Theresa May Succeeded by Liz Truss Secretary of State for Foreign and Commonwealth Affairs In office 13 July 2016 – 9 July 2018 Prime Minister Theresa May Preceded by Philip Hammond Succeeded by Jeremy Hunt Mayor of London In office 3 May 2008 – 9 May 2016 Deputy Richard Barnes Victoria Borwick Roger Evans Preceded by Ken Livingstone Succeeded by Sadiq Khan Member of Parliament In office\n\n[DOC 6] Prime Minister of the United Kingdom from 2016 to 2019 The Right Honourable The Baroness May of Maidenhead PC Official portrait, 2024 Prime Minister of the United Kingdom In office 13 July 2016 – 24 July 2019 Monarch Elizabeth II First Secretary Damian Green (2017) Preceded by David Cameron Succeeded by Boris Johnson Ministerial offices Home Secretary In office 12 May 2010 – 13 July 2016 Prime Minister David Cameron Preceded by Alan Johnson Succeeded by Amber Rudd Minister for Women and Equalities In office 12 May 2010 – 4 September 2012 Prime Minister David Cameron Preceded by Harriet Harman Succeeded by Maria Miller Party political offices Leader of the Conservative Party In office 11 July 2016 – 23 July 2019 Preceded by David Cameron Succeeded by Boris Jo\n\n[DOC 7] Prime Minister of the United Kingdom from 1997 to 2007 The Right Honourable Sir Tony Blair KG Blair in 2010 Prime Minister of the United Kingdom In office 2 May 1997 – 27 June 2007 Monarch Elizabeth II Deputy John Prescott Preceded by John Major Succeeded by Gordon Brown Executive Chairman of the Tony Blair Institute for Global Change Current Assumed role 1 December 2016 Preceded by Role established Special Envoy of the Quartet on the Middle East In office 27 June 2007 – 27 May 2015 Preceded by James Wolfensohn Succeeded by Kito de Boer Senior political offices 1994‍–‍2007 Leader of the Opposition In office 21 July 1994 – 2 May 1997 Monarch Elizabeth II Prime Minister John Major Deputy John Prescott Preceded by Margaret Beckett Succeeded by John Major Leader\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": true,\n \"reasoning\": \"Princess Diana was born on 1 July 1961; three years earlier is 1 July 1958, making her ten years old on 1 July 1968. DOC 1 lists Harold Wilson as Prime Minister from 28 July 1965 to 19 June 1970, covering July 1968. Therefore, the Prime Minister when she would have been ten is Harold Wilson.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1681, + "osl": 469, + "total_tokens": 2150, + "latency_ms": 9824.83, + "tokens_per_second": 47.74 + }, + "context": { + "kept_docs_count": 7, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "38c5a18f-ec85-497c-973a-dd1f3e721183", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:04:06.566016Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: If Princess Diana had been born three years earlier, who would have been Prime Minister when she was ten? \n\nDOCUMENTS:\n\n[DOC 1] Prime Minister of the United Kingdom from 1970 to 1974 The Right Honourable Sir Edward Heath KG MBE Portrait by Allan Warren , 1987 Prime Minister of the United Kingdom In office 19 June 1970 – 4 March 1974 Monarch Elizabeth II Preceded by Harold Wilson Succeeded by Harold Wilson Senior political offices Leader of the Opposition In office 4 March 1974 – 11 February 1975 Monarch Elizabeth II Prime Minister Harold Wilson Preceded by Harold Wilson Succeeded by Margaret Thatcher In office 28 July 1965 – 19 June 1970 Monarch Elizabeth II Prime Minister Harold Wilson Deputy Reginald Maudling Preceded by Alec Douglas-Home Succeeded by Harold Wilson Leader of the Conservative Party In office 28 July 1965 – 11 February 1975 Deputy Reginald Maudling (1965–1972) Preced\n\n[DOC 2] Prime Minister of the United Kingdom from 1979 to 1990 The Right Honourable The Baroness Thatcher LG OM DStJ PC FRS HonFRSC Studio portrait, c. 1995–96 Prime Minister of the United Kingdom In office 4 May 1979 – 28 November 1990 Monarch Elizabeth II Deputy Geoffrey Howe (1989–90) Preceded by James Callaghan Succeeded by John Major Leader of the Opposition In office 11 February 1975 – 4 May 1979 Monarch Elizabeth II Prime Minister Harold Wilson James Callaghan Deputy William Whitelaw Preceded by Edward Heath Succeeded by James Callaghan Leader of the Conservative Party In office 11 February 1975 – 28 November 1990 Deputy The Viscount Whitelaw Chairman See list The Lord Thorneycroft Cecil Parkinson John Gummer Norman Tebbit Peter Brooke Kenneth Baker Preceded\n\n[DOC 3] e Civil Service (1968–1970) Labour Wilson I 1966 Wilson II Edward Heath MP for Bexley (1916–2005) 19 June 1970 4 March 1974 3 years, 259 days 1970 First Lord of the Treasury Minister for the Civil Service Conservative Heath Harold Wilson MP for Huyton (1916–1995) Premiership 4 March 1974 5 April 1976 2 years, 33 days ( Feb. 1974 ) First Lord of the Treasury Minister for the Civil Service Labour Wilson III Oct. 1974 Wilson IV James Callaghan MP for Cardiff South East (1912–2005) 5 April 1976 4 May 1979 3 years, 30 days — First Lord of the Treasury Minister for the Civil Service Callaghan Margaret Thatcher MP for Finchley (1925–2013) Premiership 4 May 1979 28 November 1990 11 years, 209 days 1979 First Lord of the Treasury Minister for the Civil Service Conser\n\n[DOC 4] 65) The Gambia Prime Minister (1965–1970), President (1970–1994) Yusuf Lule Uganda President (1979) Fawzi Mulki Jordan Prime Minister (1953–1954) Lord Dunrossil Australia Governor-General (1960–1961) Daniel Chanis Pinzón Panama President (1949) Julius Nyerere Tanganyika Chief Minister (1960–1961), Prime Minister (1961–1962), President (1962–1964) Tanzania President (1964–1985) Paul Reeves New Zealand Governor-General (1985–1990) Lord John Russell United Kingdom Prime Minister (1846–1852; 1865–1866) Sir Ninian Stephen Australia Governor-General (1982-1989) John Swinney Scotland First Minister (2024–) Lord Palmerston United Kingdom Prime Minister (1855–1858; 1859–1865) Sir Charles Tupper Canada Prime Minister (1896) William Walker Nicaragua Filibuster of Nicar\n\n[DOC 5] Prime Minister of the United Kingdom from 2019 to 2022 The Right Honourable Boris Johnson Hon FRIBA Official portrait, 2019 Prime Minister of the United Kingdom In office 24 July 2019 – 6 September 2022 Monarch Elizabeth II Deputy Dominic Raab Preceded by Theresa May Succeeded by Liz Truss Leader of the Conservative Party In office 23 July 2019 – 5 September 2022 Preceded by Theresa May Succeeded by Liz Truss Secretary of State for Foreign and Commonwealth Affairs In office 13 July 2016 – 9 July 2018 Prime Minister Theresa May Preceded by Philip Hammond Succeeded by Jeremy Hunt Mayor of London In office 3 May 2008 – 9 May 2016 Deputy Richard Barnes Victoria Borwick Roger Evans Preceded by Ken Livingstone Succeeded by Sadiq Khan Member of Parliament In office\n\n[DOC 6] Prime Minister of the United Kingdom from 2016 to 2019 The Right Honourable The Baroness May of Maidenhead PC Official portrait, 2024 Prime Minister of the United Kingdom In office 13 July 2016 – 24 July 2019 Monarch Elizabeth II First Secretary Damian Green (2017) Preceded by David Cameron Succeeded by Boris Johnson Ministerial offices Home Secretary In office 12 May 2010 – 13 July 2016 Prime Minister David Cameron Preceded by Alan Johnson Succeeded by Amber Rudd Minister for Women and Equalities In office 12 May 2010 – 4 September 2012 Prime Minister David Cameron Preceded by Harriet Harman Succeeded by Maria Miller Party political offices Leader of the Conservative Party In office 11 July 2016 – 23 July 2019 Preceded by David Cameron Succeeded by Boris Jo\n\n[DOC 7] Prime Minister of the United Kingdom from 1997 to 2007 The Right Honourable Sir Tony Blair KG Blair in 2010 Prime Minister of the United Kingdom In office 2 May 1997 – 27 June 2007 Monarch Elizabeth II Deputy John Prescott Preceded by John Major Succeeded by Gordon Brown Executive Chairman of the Tony Blair Institute for Global Change Current Assumed role 1 December 2016 Preceded by Role established Special Envoy of the Quartet on the Middle East In office 27 June 2007 – 27 May 2015 Preceded by James Wolfensohn Succeeded by Kito de Boer Senior political offices 1994‍–‍2007 Leader of the Opposition In office 21 July 1994 – 2 May 1997 Monarch Elizabeth II Prime Minister John Major Deputy John Prescott Preceded by Margaret Beckett Succeeded by John Major Leader\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1535, + "osl": 101, + "total_tokens": 1636, + "latency_ms": 3022.27, + "tokens_per_second": 33.42 + }, + "context": { + "num_documents_used": 7, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:04:06.567016Z", + "summary": { + "total_llm_calls": 12, + "total_hops": 5, + "total_input_tokens": 17651, + "total_output_tokens": 5137, + "total_tokens": 22788, + "total_latency_ms": 61315.32, + "average_tokens_per_second": 133.64, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Diana,_Princess_of_Wales", + "https://en.wikipedia.org/wiki/List_of_prime_ministers_of_the_United_Kingdom" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "Harold Wilson" + } + }, + { + "query_id": "30", + "original_query": "As of August 1, 2024, what is the population of the writer of the \"Culdcept Saga\"'s birthplace? Write the answer to the nearest million, in characters.", + "timestamp_start": "2026-05-16T03:04:06.591555Z", + "llm_calls": [ + { + "call_id": "304edf01-91c6-4e3d-8446-e38a33199215", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:04:10.706487Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 1, 2024, what is the population of the writer of the \"Culdcept Saga\"'s birthplace? Write the answer to the nearest million, in characters.\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Culdcept Saga writer\",\n \"Culdcept Saga writer birthplace\",\n \"city population 2024\"\n ],\n \"feedback\": \"First need to identify the writer of the game, then locate their birthplace, finally find the 2024 population of that city to answer the question.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 473, + "osl": 673, + "total_tokens": 1146, + "latency_ms": 4114.8, + "tokens_per_second": 163.56 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "04e05608-39d8-4f8c-81ce-dc1a1d11795b", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:04:29.293057Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of August 1, 2024, what is the population of the writer of the \"Culdcept Saga\"'s birthplace? Write the answer to the nearest million, in characters.\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] maint: multiple names: authors list ( link )\n\n[NEW 2] Novels Poetry Biography\n\n[NEW 3] 4) , p. 95. ^ Djurdjevic (2014) , p. 106. ^ a b Djurdjevic (2014) , p. 96. ^ a b Djurdjevic (2014) , p. 109. ^ Djurdjevic (2014) , pp. 92–93. ^ Djurdjevic (2014) , p. 98. ^ Djurdjevic (2014) , p. 99. ^ Djurdjevic (2014) , p. 100. ^ Hedenborg White (2020) , p. 168. ^ Hedenborg White (2020) , p. 174. ^ Djurdjevic (2014) , p. 107. ^ Hedenborg White (2020) , p. 169. ^ Hedenborg White (2020) , p. 165. ^ a b c Thompson (2018) . ^ a b Lees (2018) . ^ a b c Crowley (1976) , ch. 3, v. 47. ^ Stratton-Kent (1988) . ^ Stratton-Kent (2011) . ^ Thompson (2016) . ^ Grant (1980) , p. [ page needed ] . ^ Grant (1999) , p. [ page needed ] . ^ Kraig (n. d.) . Works cited Primary sources Contains a lengthy account of the writing of Nema's Liber Pennae Praenumbra . Contains a ph\n\n[NEW 4] oks Ken MacLeod The Sky Road Tor Books Nalo Hopkinson Midnight Robber Warner Aspect 2002 Neil Gaiman * American Gods William Morrow and Company Lois McMaster Bujold The Curse of Chalion Eos Connie Willis Passage Bantam Books China Miéville Perdido Street Station Macmillan Publishers Robert Charles Wilson The Chronoliths Tor Books Ken MacLeod Cosmonaut Keep Orbit Books 2003 Robert J. Sawyer * Hominids Analog Science Fiction and Fact David Brin Kiln People (UK: Kil'n People ) Tor Books Michael Swanwick Bones of the Earth Eos China Miéville The Scar Del Rey Books Kim Stanley Robinson The Years of Rice and Salt Bantam Books 2004 Lois McMaster Bujold * Paladin of Souls Eos Dan Simmons Ilium Eos Charles Stross Singularity Sky Ace Books Robert Charles Wilson Blind\n\n[NEW 5] : authors list ( link )\n\n[NEW 6] 2006 video game Culdcept Saga Developers OmiyaSoft , Jamsworks Publisher Namco Bandai Games Writer Tow Ubukata Composer Kenji Ito Platform Xbox 360 Release JP : November 22, 2006 NA : February 5, 2008 Genres Board game , Collectible card game Modes Single-player , multiplayer Culdcept Saga ( カルドセプト サーガ , Karudoseputo Sāga ) is a video game in the Culdcept series developed exclusively for the Xbox 360 video game console . It is the first entry for a Microsoft console in the franchise. A demo of the title was made available to Xbox Live users on December 4, 2007. The demo includes two different pre-made card decks and supports both single player gameplay and local multiplayer for up to four players. Initially released in Japan in 2006, the full game wasn't rel\n\n[NEW 7] 20, to July 1, 2024 The municipality land area as of January 1, 2020 The municipality population density as of April 1, 2020 (residents per unit of land area) The municipality latitude and longitude coordinates Map this section's coordinates using OpenStreetMap Download coordinates as: KML GPX (all coordinates) GPX (primary coordinates) GPX (secondary coordinates) State capital State's largest municipality State capital and largest municipality Federal capital Municipality ST 2024 estimate 2020 census Change 2020 land area 2020 density Location mi 2 km 2 / mi 2 / km 2 New York NY 8,478,072 8,804,190 −3.70% 300.5 778.3 29,298 11,312 40°40′N 73°56′W  /  40.66°N 73.94°W  / 40.66; -73.94  ( New York City ) Los Angeles CA 3,878,704 3,898,747 −0.51% 469.5 1,21\n\n[NEW 8] absolute numbers in 2021. China has over 160 cities with a population of over one million, including the 18 megacities as of 2024 [update] (cities with a population of over 10 million) of Chongqing , Shanghai , Beijing , Chengdu , Guangzhou , Shenzhen , Tianjin , Xi'an , Suzhou , Zhengzhou , Wuhan , Hangzhou , Linyi , Shijiazhuang , Dongguan , Qingdao , Changsha and Hefei . The total permanent population of Chongqing, Shanghai, Beijing and Chengdu is above 20 million. Shanghai is China's most populous urban area while Chongqing is its largest city proper , the only city in China with a permanent population of over 30 million. The figures in the table below are from the 2020 census, and are only estimates of the urban populations within administrative city li\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 8 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 0, 0, 0, 1, 1, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1432, + "osl": 750, + "total_tokens": 2182, + "latency_ms": 17922.53, + "tokens_per_second": 41.85 + }, + "context": { + "num_documents_evaluated": 8, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "516cc71c-85d9-4f9a-bcee-1e59475fcfc4", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:04:35.132558Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 1, 2024, what is the population of the writer of the \"Culdcept Saga\"'s birthplace? Write the answer to the nearest million, in characters.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] maint: multiple names: authors list ( link )\n\n[DOC 2] : authors list ( link )\n\n[DOC 3] 2006 video game Culdcept Saga Developers OmiyaSoft , Jamsworks Publisher Namco Bandai Games Writer Tow Ubukata Composer Kenji Ito Platform Xbox 360 Release JP : November 22, 2006 NA : February 5, 2008 Genres Board game , Collectible card game Modes Single-player , multiplayer Culdcept Saga ( カルドセプト サーガ , Karudoseputo Sāga ) is a video game in the Culdcept series developed exclusively for the Xbox 360 video game console . It is the first entry for a Microsoft console in the franchise. A demo of the title was made available to Xbox Live users on December 4, 2007. The demo includes two different pre-made card decks and supports both single player gameplay and local multiplayer for up to four players. Initially released in Japan in 2006, the full game wasn't rel\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide the writer of Culdcept Saga (Tow Ubukata) but do not include information about his birthplace or the population of that location as of August 1, 2024. Therefore the necessary facts to answer the question are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 475, + "osl": 68, + "total_tokens": 543, + "latency_ms": 5838.54, + "tokens_per_second": 11.65 + }, + "context": { + "kept_docs_count": 3, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "2a3dfa79-5388-4b76-93ec-97e9601bda43", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:04:41.281004Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 1, 2024, what is the population of the writer of the \"Culdcept Saga\"'s birthplace? Write the answer to the nearest million, in characters.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] maint: multiple names: authors list ( link )\n\n[DOC 2] : authors list ( link )\n\n[DOC 3] 2006 video game Culdcept Saga Developers OmiyaSoft , Jamsworks Publisher Namco Bandai Games Writer Tow Ubukata Composer Kenji Ito Platform Xbox 360 Release JP : November 22, 2006 NA : February 5, 2008 Genres Board game , Collectible card game Modes Single-player , multiplayer Culdcept Saga ( カルドセプト サーガ , Karudoseputo Sāga ) is a video game in the Culdcept series developed exclusively for the Xbox 360 video game console . It is the first entry for a Microsoft console in the franchise. A demo of the title was made available to Xbox Live users on December 4, 2007. The demo includes two different pre-made card decks and supports both single player gameplay and local multiplayer for up to four players. Initially released in Japan in 2006, the full game wasn't rel\n\n\nSEARCH HISTORY: - Query: 'Culdcept Saga writer' → 5 docs\n- Query: 'Culdcept Saga writer birthplace' → 1 docs\n- Query: 'city population 2024' → 2 docs\n\nFEEDBACK: First need to identify the writer of the game, then locate their birthplace, finally find the 2024 population of that city to answer the question.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Tow Ubukata birthplace\",\n \"Matsumoto population 2024\",\n \"Nagano city population 2024\"\n ],\n \"feedback\": \"First query will reveal the writer's birthplace. Subsequent queries target the 2024 population of that city (or its prefectural capital if needed) to compute the answer.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 756, + "osl": 441, + "total_tokens": 1197, + "latency_ms": 6147.83, + "tokens_per_second": 71.73 + }, + "context": { + "kept_docs_count": 3, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "e9dde3bf-3ed3-4829-acbd-571ddb87f51c", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T03:04:47.353824Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of August 1, 2024, what is the population of the writer of the \"Culdcept Saga\"'s birthplace? Write the answer to the nearest million, in characters.\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] ctress, model and radio personality Kaiu Shirai , manga artist Chiune Sugihara , diplomat Teiji Takagi , mathematician n-buna , musician LiSA , singer-songwriter, lyricist. See also Solar Ark , a solar energy project located in Gifu Prefecture Notes ^ ^ ^ Nussbaum, Louis-Frédéric. (2005). \"Gifu-ken\" in Japan Encyclopedia , p. 246 , p. 246, at Google Books ^ Nussbaum, \"Chūbu\" in p. 126 , p. 126, at Google Books ^ a b ^ Nussbaum, \"Gifu\" in p. 246 , p. 246, at Google Books ^ Instant Gifu . Gifu International Center, 1995. ^ Nussbaum, \"Provinces and prefectures\" in p. 780 , p. 780, at Google Books ^ Stone ledger in front of Kashimori Shrine . Erected by Kashimori Shrine. ^ Gifu tour guide – Outline of Gifu Prefecture Archived October 1, 2011, at the Wayback Mach\n\n[NEW 2] Kemusuk is a hamlet ( dukuh ) in the Argomulyo village, Sedayu subdistrict, Bantul Regency , Special Region of Yogyakarta , Indonesia . The area, around 10 km to the west of Yogyakarta towards the town of Wates , is known as the birthplace of the second president of Indonesia , Suharto . Significance with Suharto's life and family Suharto was born to a 'poor but not unimportant farmer's family' in the village. His father, Kertosudiro, was a local irrigation official in charge of overseeing the allocation of water to different farmers in Kemusuk. His mother, Sukirah, was a village woman from a nearby hamlet. The Suharto family have returned to the village on various occasions in recent years. Suharto himself, accompanied by members of his family, made a trip\n\n[NEW 3] is the date accepted by most historians; the historian Paul Ratchnevsky noted that Temüjin himself may not have known the truth. The location of Temüjin's birth, which the Secret History records as Delüün Boldog on the Onon River , is similarly debated: it has been placed at either Dadal in Khentii Province or in southern Agin-Buryat Okrug , Russia. The Onon River , near which Temüjin was born, pictured here in Khentii Province , Mongolia Temüjin was born into the Borjigin clan of the Mongol tribe to Yesügei , a chieftain who claimed descent from the legendary warlord Bodonchar Munkhag , and his principal wife Hö'elün , originally of the Olkhonud clan, whom Yesügei had abducted from her Merkit bridegroom Chiledu. The origin of his birth name is contested: th\n\n[NEW 4] rned to Kenya in 2011. She took up her position at Royal Media Services in 2012. Personal life Amimo is a Christian . Her parents are from Kenya and Nigeria. Uduak , her Nigerian name means God's will in her father's language. Her second name Amimo is a Kenyan name. She is named after her great-grandmother on her mother's side of the family from western Kenya. She is a first-born. References ^ a b c d ^ ^ ^ {{ cite web }} : CS1 maint: bot: original URL status unknown ( link ) ^ ^ ^ ^ ^ a b ^ ^\n\n[NEW 5] uzuki T j (2.98) · 3:2 40 km (25 mi) MPC · JPL 8552 Hyoichi 1995 HE Hyoichi April 20, 1995 Kuma Kogen A. Nakamura · 8.9 km (5.5 mi) MPC · JPL 8553 Bradsmith 1995 HG Bradsmith April 20, 1995 Kitami K. Endate , K. Watanabe · 4.6 km (2.9 mi) MPC · JPL 8554 Gabreta 1995 KH Gabreta May 25, 1995 Kleť M. Tichý · 2.4 km (1.5 mi) MPC · JPL 8555 Mirimao 1995 LD Mirimao June 3, 1995 Stroncone Santa Lucia V 2.4 km (1.5 mi) MPC · JPL 8556 Jana 1995 NB Jana July 7, 1995 Kleť Z. Moravec · 7.3 km (4.5 mi) MPC · JPL 8557 Šaroun 1995 OK Šaroun July 23, 1995 Ondřejov L. Kotková V 2.0 km (1.2 mi) MPC · JPL 8558 Hack 1995 PC Hack August 1, 1995 San Marcello L. Tesi , A. Boattini · 7.7 km (4.8 mi) MPC · JPL 8559 1995 QM 2 — August 25, 1995 Nachi-Katsuura Y. Shimizu , T. Urata AGN\n\n[NEW 6] 33 13 477 4,803 1 31 1 Matsudo - - 14 8 134 1,792 - 10 - Noda 1 - 5 1 7 1,935 - - 1 Mobara - - - - 2 68 - - - Narita - - 17 4 68 1,327 - 7 - Sakura - - 3 33 240 1,669 - 8 - Tōgane - - 2 4 15 164 - - - Asahi 14 2 12 318 916 2,546 - - 2 Narashino 1 - 7 9 717 4,619 - - 1 Kashiwa 1 - 24 1 17 4,803 - - - Katsuura - - 2 - - 1 - - - Ichihara - - 6 - 1 1,171 - - - Nagareyama - - 1 - - 1,077 - - - Yachiyo 1 - 3 6 24 1,507 - - 2 Abiko - - 2 134 101 3,323 - 19 - Kamogawa - - 2 - - - - - - Kamagaya - - 5 - 9 810 - - - Kimitsu - - 2 - - 10 - - - Urayasu - - 29 10 3,649 5,111 1 93 - Yotsukaidō - - - - 1 532 - - - Sodegaura - - - - 1 25 - - - Yachimata - - 2 - - 284 - - - Inzai - - 7 11 79 2,222 - - - Shiroi - - 2 - - 422 - - - Tomisato - - 3 11 7 687 - - - Tomisato - - -\n\n[NEW 7] 33 13 477 4,803 1 31 1 Matsudo - - 14 8 134 1,792 - 10 - Noda 1 - 5 1 7 1,935 - - 1 Mobara - - - - 2 68 - - - Narita - - 17 4 68 1,327 - 7 - Sakura - - 3 33 240 1,669 - 8 - Tōgane - - 2 4 15 164 - - - Asahi 14 2 12 318 916 2,546 - - 2 Narashino 1 - 7 9 717 4,619 - - 1 Kashiwa 1 - 24 1 17 4,803 - - - Katsuura - - 2 - - 1 - - - Ichihara - - 6 - 1 1,171 - - - Nagareyama - - 1 - - 1,077 - - - Yachiyo 1 - 3 6 24 1,507 - - 2 Abiko - - 2 134 101 3,323 - 19 - Kamogawa - - 2 - - - - - - Kamagaya - - 5 - 9 810 - - - Kimitsu - - 2 - - 10 - - - Urayasu - - 29 10 3,649 5,111 1 93 - Yotsukaidō - - - - 1 532 - - - Sodegaura - - - - 1 25 - - - Yachimata - - 2 - - 284 - - - Inzai - - 7 11 79 2,222 - - - Shiroi - - 2 - - 422 - - - Tomisato - - 3 11 7 687 - - - Tomisato - - -\n\n[NEW 8] hs of Japan (1946–2019) Population pyramids by prefecture Population pyramids of Japan's prefectures in 2020 Tokyo Nagasaki Hiroshima Hokkaido Kyoto Aichi Fukushima Osaka Okinawa Aomori Akita Chiba Ibaraki Miyagi Yamagata Iwate Fukuoka Yamaguchi Saga Okayama Toyama Hyogo Ishikawa Niigata Fukui Ehime Tokushima Kagawa Miyazaki Kumamoto Kagoshima Kochi Yamanashi Oita Kanagawa Shizuoka Mie Wakayama Saitama Nara Tochigi Nagano Gunma Shiga Gifu Tottori Shimane Population estimates by sex and age group (01. VII.2020) (Because of rounding, totals are not in all cases the sum of the respective components. Estimates or projections based on the 2015 population census.): Age group Male Female Total % Total 61 226 000 64 610 000 125 836 000 100 0–4 2 406 000 2 288 000 4\n\n[NEW 9] itan Government • Governor Yuriko Koike ( indp. ) • Representatives 42 • Councilors 11 Area • Total 2,194 km 2 (847 sq mi) • Metro 13,452 km 2 (5,194 sq mi) • Rank 45th in Japan Highest elevation 2,017 m (6,617 ft) Lowest elevation 0 m (0 ft) Population (May 2025) • Total 14,254,039 • Rank 1st in Japan • Density 6,363/km 2 (16,480/sq mi) • Urban 39,105,000 • Metro 41,000,000 • Metro density 3,000/km 2 (7,900/sq mi) • Dialects Tokyo Tama Northern Izu Islands Demonym Tokyoite GDP (nominal, 2022) • Total ¥120.22 trillion (2022) ( US$ 914.23 billion ) • Metro ¥239.12 trillion (2022) ( US$ 1.82 trillion ) Time zone UTC+09:00 ( Japan Standard Time ) ISO 3166-2 JP-13 Climate Humid subtropical climate (Cfa) Flower Yoshino cherry Tree Ginkgo Bird Black-headed gull We\n\n\nKEPT DOCUMENTS (context only):\n[3 documents already kept as relevant]\n[KEPT 1] maint: multiple names: authors list ( link )...\n[KEPT 2] : authors list ( link )...\n[KEPT 3] 2006 video game Culdcept Saga Developers OmiyaSoft , Jamsworks Publisher Namco Bandai Games Writer Tow Ubukata Composer Kenji Ito Platform Xbox 360 Release JP : November 22, 2006 NA : February 5, 2008 Genres Board game , Collectible card game Modes Single-player , multiplayer Culdcept Saga ( カルドセプト ...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 9 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[0,0,0,0,0,0,0,1,1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2865, + "osl": 1162, + "total_tokens": 4027, + "latency_ms": 5379.81, + "tokens_per_second": 215.99 + }, + "context": { + "num_documents_evaluated": 9, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 3 + } + }, + { + "call_id": "7715386b-a056-4ed3-8aea-0f41da9742e5", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:04:50.192549Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 1, 2024, what is the population of the writer of the \"Culdcept Saga\"'s birthplace? Write the answer to the nearest million, in characters.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] maint: multiple names: authors list ( link )\n\n[DOC 2] : authors list ( link )\n\n[DOC 3] 2006 video game Culdcept Saga Developers OmiyaSoft , Jamsworks Publisher Namco Bandai Games Writer Tow Ubukata Composer Kenji Ito Platform Xbox 360 Release JP : November 22, 2006 NA : February 5, 2008 Genres Board game , Collectible card game Modes Single-player , multiplayer Culdcept Saga ( カルドセプト サーガ , Karudoseputo Sāga ) is a video game in the Culdcept series developed exclusively for the Xbox 360 video game console . It is the first entry for a Microsoft console in the franchise. A demo of the title was made available to Xbox Live users on December 4, 2007. The demo includes two different pre-made card decks and supports both single player gameplay and local multiplayer for up to four players. Initially released in Japan in 2006, the full game wasn't rel\n\n[DOC 4] hs of Japan (1946–2019) Population pyramids by prefecture Population pyramids of Japan's prefectures in 2020 Tokyo Nagasaki Hiroshima Hokkaido Kyoto Aichi Fukushima Osaka Okinawa Aomori Akita Chiba Ibaraki Miyagi Yamagata Iwate Fukuoka Yamaguchi Saga Okayama Toyama Hyogo Ishikawa Niigata Fukui Ehime Tokushima Kagawa Miyazaki Kumamoto Kagoshima Kochi Yamanashi Oita Kanagawa Shizuoka Mie Wakayama Saitama Nara Tochigi Nagano Gunma Shiga Gifu Tottori Shimane Population estimates by sex and age group (01. VII.2020) (Because of rounding, totals are not in all cases the sum of the respective components. Estimates or projections based on the 2015 population census.): Age group Male Female Total % Total 61 226 000 64 610 000 125 836 000 100 0–4 2 406 000 2 288 000 4\n\n[DOC 5] itan Government • Governor Yuriko Koike ( indp. ) • Representatives 42 • Councilors 11 Area • Total 2,194 km 2 (847 sq mi) • Metro 13,452 km 2 (5,194 sq mi) • Rank 45th in Japan Highest elevation 2,017 m (6,617 ft) Lowest elevation 0 m (0 ft) Population (May 2025) • Total 14,254,039 • Rank 1st in Japan • Density 6,363/km 2 (16,480/sq mi) • Urban 39,105,000 • Metro 41,000,000 • Metro density 3,000/km 2 (7,900/sq mi) • Dialects Tokyo Tama Northern Izu Islands Demonym Tokyoite GDP (nominal, 2022) • Total ¥120.22 trillion (2022) ( US$ 914.23 billion ) • Metro ¥239.12 trillion (2022) ( US$ 1.82 trillion ) Time zone UTC+09:00 ( Japan Standard Time ) ISO 3166-2 JP-13 Climate Humid subtropical climate (Cfa) Flower Yoshino cherry Tree Ginkgo Bird Black-headed gull We\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents identify the writer of Culdcept Saga as Tow Ubukata (DOC 3) but do not provide his birthplace. While DOC 5 gives Tokyo's population for May 2025, there is no information linking Tow Ubukata to Tokyo or any other location, nor is there population data specifically for August 1, 2024. Therefore the required facts to compute the population of the writer's birthplace as of that date are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1034, + "osl": 529, + "total_tokens": 1563, + "latency_ms": 2837.85, + "tokens_per_second": 186.41 + }, + "context": { + "kept_docs_count": 5, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "a00fe2c8-312d-4085-9411-1cbae4b65a65", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:04:53.329090Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 1, 2024, what is the population of the writer of the \"Culdcept Saga\"'s birthplace? Write the answer to the nearest million, in characters.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] maint: multiple names: authors list ( link )\n\n[DOC 2] : authors list ( link )\n\n[DOC 3] 2006 video game Culdcept Saga Developers OmiyaSoft , Jamsworks Publisher Namco Bandai Games Writer Tow Ubukata Composer Kenji Ito Platform Xbox 360 Release JP : November 22, 2006 NA : February 5, 2008 Genres Board game , Collectible card game Modes Single-player , multiplayer Culdcept Saga ( カルドセプト サーガ , Karudoseputo Sāga ) is a video game in the Culdcept series developed exclusively for the Xbox 360 video game console . It is the first entry for a Microsoft console in the franchise. A demo of the title was made available to Xbox Live users on December 4, 2007. The demo includes two different pre-made card decks and supports both single player gameplay and local multiplayer for up to four players. Initially released in Japan in 2006, the full game wasn't rel\n\n[DOC 4] hs of Japan (1946–2019) Population pyramids by prefecture Population pyramids of Japan's prefectures in 2020 Tokyo Nagasaki Hiroshima Hokkaido Kyoto Aichi Fukushima Osaka Okinawa Aomori Akita Chiba Ibaraki Miyagi Yamagata Iwate Fukuoka Yamaguchi Saga Okayama Toyama Hyogo Ishikawa Niigata Fukui Ehime Tokushima Kagawa Miyazaki Kumamoto Kagoshima Kochi Yamanashi Oita Kanagawa Shizuoka Mie Wakayama Saitama Nara Tochigi Nagano Gunma Shiga Gifu Tottori Shimane Population estimates by sex and age group (01. VII.2020) (Because of rounding, totals are not in all cases the sum of the respective components. Estimates or projections based on the 2015 population census.): Age group Male Female Total % Total 61 226 000 64 610 000 125 836 000 100 0–4 2 406 000 2 288 000 4\n\n[DOC 5] itan Government • Governor Yuriko Koike ( indp. ) • Representatives 42 • Councilors 11 Area • Total 2,194 km 2 (847 sq mi) • Metro 13,452 km 2 (5,194 sq mi) • Rank 45th in Japan Highest elevation 2,017 m (6,617 ft) Lowest elevation 0 m (0 ft) Population (May 2025) • Total 14,254,039 • Rank 1st in Japan • Density 6,363/km 2 (16,480/sq mi) • Urban 39,105,000 • Metro 41,000,000 • Metro density 3,000/km 2 (7,900/sq mi) • Dialects Tokyo Tama Northern Izu Islands Demonym Tokyoite GDP (nominal, 2022) • Total ¥120.22 trillion (2022) ( US$ 914.23 billion ) • Metro ¥239.12 trillion (2022) ( US$ 1.82 trillion ) Time zone UTC+09:00 ( Japan Standard Time ) ISO 3166-2 JP-13 Climate Humid subtropical climate (Cfa) Flower Yoshino cherry Tree Ginkgo Bird Black-headed gull We\n\n\nSEARCH HISTORY: - Query: 'Culdcept Saga writer' → 5 docs\n- Query: 'Culdcept Saga writer birthplace' → 1 docs\n- Query: 'city population 2024' → 2 docs\n- Query: 'Tow Ubukata birthplace' → 5 docs\n- Query: 'Matsumoto population 2024' → 3 docs\n- Query: 'Nagano city population 2024' → 1 docs\n\nFEEDBACK: First need to identify the writer of the game, then locate their birthplace, finally find the 2024 population of that city to answer the question.\nFirst query will reveal the writer's birthplace. Subsequent queries target the 2024 population of that city (or its prefectural capital if needed) to compute the answer.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Tow Ubukata birthplace\",\n \"Matsumoto city population 2024\",\n \"Nagano city population 2024\"\n ],\n \"feedback\": \"First query will confirm the writer's birthplace (likely Matsumoto). The next two queries retrieve the 2024 population of that city (or the regional capital if needed) to answer the question.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1333, + "osl": 766, + "total_tokens": 2099, + "latency_ms": 3135.93, + "tokens_per_second": 244.27 + }, + "context": { + "kept_docs_count": 5, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "6e071be2-cc54-4084-b2a1-2ab4d23a63ed", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:04:57.851707Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 1, 2024, what is the population of the writer of the \"Culdcept Saga\"'s birthplace? Write the answer to the nearest million, in characters.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] maint: multiple names: authors list ( link )\n\n[DOC 2] : authors list ( link )\n\n[DOC 3] 2006 video game Culdcept Saga Developers OmiyaSoft , Jamsworks Publisher Namco Bandai Games Writer Tow Ubukata Composer Kenji Ito Platform Xbox 360 Release JP : November 22, 2006 NA : February 5, 2008 Genres Board game , Collectible card game Modes Single-player , multiplayer Culdcept Saga ( カルドセプト サーガ , Karudoseputo Sāga ) is a video game in the Culdcept series developed exclusively for the Xbox 360 video game console . It is the first entry for a Microsoft console in the franchise. A demo of the title was made available to Xbox Live users on December 4, 2007. The demo includes two different pre-made card decks and supports both single player gameplay and local multiplayer for up to four players. Initially released in Japan in 2006, the full game wasn't rel\n\n[DOC 4] hs of Japan (1946–2019) Population pyramids by prefecture Population pyramids of Japan's prefectures in 2020 Tokyo Nagasaki Hiroshima Hokkaido Kyoto Aichi Fukushima Osaka Okinawa Aomori Akita Chiba Ibaraki Miyagi Yamagata Iwate Fukuoka Yamaguchi Saga Okayama Toyama Hyogo Ishikawa Niigata Fukui Ehime Tokushima Kagawa Miyazaki Kumamoto Kagoshima Kochi Yamanashi Oita Kanagawa Shizuoka Mie Wakayama Saitama Nara Tochigi Nagano Gunma Shiga Gifu Tottori Shimane Population estimates by sex and age group (01. VII.2020) (Because of rounding, totals are not in all cases the sum of the respective components. Estimates or projections based on the 2015 population census.): Age group Male Female Total % Total 61 226 000 64 610 000 125 836 000 100 0–4 2 406 000 2 288 000 4\n\n[DOC 5] itan Government • Governor Yuriko Koike ( indp. ) • Representatives 42 • Councilors 11 Area • Total 2,194 km 2 (847 sq mi) • Metro 13,452 km 2 (5,194 sq mi) • Rank 45th in Japan Highest elevation 2,017 m (6,617 ft) Lowest elevation 0 m (0 ft) Population (May 2025) • Total 14,254,039 • Rank 1st in Japan • Density 6,363/km 2 (16,480/sq mi) • Urban 39,105,000 • Metro 41,000,000 • Metro density 3,000/km 2 (7,900/sq mi) • Dialects Tokyo Tama Northern Izu Islands Demonym Tokyoite GDP (nominal, 2022) • Total ¥120.22 trillion (2022) ( US$ 914.23 billion ) • Metro ¥239.12 trillion (2022) ( US$ 1.82 trillion ) Time zone UTC+09:00 ( Japan Standard Time ) ISO 3166-2 JP-13 Climate Humid subtropical climate (Cfa) Flower Yoshino cherry Tree Ginkgo Bird Black-headed gull We\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Missing: confirmation of the writer Tow Ubukata's birthplace (none of the documents state his birthplace). Additionally, there is no population figure for that location as of August 1, 2024; the only city population provided is for Tokyo in May 2025, which does not match the required date.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1034, + "osl": 514, + "total_tokens": 1548, + "latency_ms": 3819.62, + "tokens_per_second": 134.57 + }, + "context": { + "kept_docs_count": 5, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "99c56be0-ad7e-4061-ab65-53f583454274", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:04:59.513476Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 1, 2024, what is the population of the writer of the \"Culdcept Saga\"'s birthplace? Write the answer to the nearest million, in characters.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] maint: multiple names: authors list ( link )\n\n[DOC 2] : authors list ( link )\n\n[DOC 3] 2006 video game Culdcept Saga Developers OmiyaSoft , Jamsworks Publisher Namco Bandai Games Writer Tow Ubukata Composer Kenji Ito Platform Xbox 360 Release JP : November 22, 2006 NA : February 5, 2008 Genres Board game , Collectible card game Modes Single-player , multiplayer Culdcept Saga ( カルドセプト サーガ , Karudoseputo Sāga ) is a video game in the Culdcept series developed exclusively for the Xbox 360 video game console . It is the first entry for a Microsoft console in the franchise. A demo of the title was made available to Xbox Live users on December 4, 2007. The demo includes two different pre-made card decks and supports both single player gameplay and local multiplayer for up to four players. Initially released in Japan in 2006, the full game wasn't rel\n\n[DOC 4] hs of Japan (1946–2019) Population pyramids by prefecture Population pyramids of Japan's prefectures in 2020 Tokyo Nagasaki Hiroshima Hokkaido Kyoto Aichi Fukushima Osaka Okinawa Aomori Akita Chiba Ibaraki Miyagi Yamagata Iwate Fukuoka Yamaguchi Saga Okayama Toyama Hyogo Ishikawa Niigata Fukui Ehime Tokushima Kagawa Miyazaki Kumamoto Kagoshima Kochi Yamanashi Oita Kanagawa Shizuoka Mie Wakayama Saitama Nara Tochigi Nagano Gunma Shiga Gifu Tottori Shimane Population estimates by sex and age group (01. VII.2020) (Because of rounding, totals are not in all cases the sum of the respective components. Estimates or projections based on the 2015 population census.): Age group Male Female Total % Total 61 226 000 64 610 000 125 836 000 100 0–4 2 406 000 2 288 000 4\n\n[DOC 5] itan Government • Governor Yuriko Koike ( indp. ) • Representatives 42 • Councilors 11 Area • Total 2,194 km 2 (847 sq mi) • Metro 13,452 km 2 (5,194 sq mi) • Rank 45th in Japan Highest elevation 2,017 m (6,617 ft) Lowest elevation 0 m (0 ft) Population (May 2025) • Total 14,254,039 • Rank 1st in Japan • Density 6,363/km 2 (16,480/sq mi) • Urban 39,105,000 • Metro 41,000,000 • Metro density 3,000/km 2 (7,900/sq mi) • Dialects Tokyo Tama Northern Izu Islands Demonym Tokyoite GDP (nominal, 2022) • Total ¥120.22 trillion (2022) ( US$ 914.23 billion ) • Metro ¥239.12 trillion (2022) ( US$ 1.82 trillion ) Time zone UTC+09:00 ( Japan Standard Time ) ISO 3166-2 JP-13 Climate Humid subtropical climate (Cfa) Flower Yoshino cherry Tree Ginkgo Bird Black-headed gull We\n\n\nSEARCH HISTORY: - Query: 'Culdcept Saga writer' → 5 docs\n- Query: 'Culdcept Saga writer birthplace' → 1 docs\n- Query: 'city population 2024' → 2 docs\n- Query: 'Tow Ubukata birthplace' → 5 docs\n- Query: 'Matsumoto population 2024' → 3 docs\n- Query: 'Nagano city population 2024' → 1 docs\n- Query: 'Tow Ubukata birthplace' → 0 docs\n- Query: 'Matsumoto city population 2024' → 0 docs\n- Query: 'Nagano city population 2024' → 0 docs\n\nFEEDBACK: First need to identify the writer of the game, then locate their birthplace, finally find the 2024 population of that city to answer the question.\nFirst query will reveal the writer's birthplace. Subsequent queries target the 2024 population of that city (or its prefectural capital if needed) to compute the answer.\nFirst query will confirm the writer's birthplace (likely Matsumoto). The next two queries retrieve the 2024 population of that city (or the regional capital if needed) to answer the question.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Tow Ubukata\",\n \"Matsumoto population 2024\",\n \"Nagano city population 2024\"\n ],\n \"feedback\": \"First query gets the writer's full biography (including birthplace). Second query obtains the 2024 population of that birthplace (Matsumoto). Third query is a fallback to retrieve the population of Nagano city if Matsumoto data is unavailable.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1424, + "osl": 295, + "total_tokens": 1719, + "latency_ms": 1660.99, + "tokens_per_second": 177.6 + }, + "context": { + "kept_docs_count": 5, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "8acac5dc-9918-4f04-984a-bf0310773571", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T03:05:10.197676Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of August 1, 2024, what is the population of the writer of the \"Culdcept Saga\"'s birthplace? Write the answer to the nearest million, in characters.\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Japanese writer Tow Ubukata Ubukata in 2019 Native name 冲方 丁 Born Mineo Fujino (藤野峰男) ( 1977-02-14 ) February 14, 1977 (age 49) Gifu Prefecture , Japan Occupation Novelist screenwriter Language Japanese Mineo Fujino ( Japanese : 藤野峰男 , Hepburn : Fujino Mineo ; born February 14, 1977, in Gifu Prefecture ) , known by his pen name Tow Ubukata ( 冲方 丁 , Ubukata Tō ) , is a Japanese novelist and screenwriter. He primarily writes fantasy and science fiction. His major works include Mardock Scramble , Le Chevalier D'Eon and Heroic Age . He also did series composition for the Fafner in the Azure series, Ghost in the Shell: Arise , Psycho-Pass 2 and Psycho-Pass 3 . Early life Ubukata was raised in Singapore and Nepal. Career In high school, Ubukata received several wr\n\n[NEW 2] uTube\n\n[NEW 3] ouTube\n\n\nKEPT DOCUMENTS (context only):\n[5 documents already kept as relevant]\n[KEPT 1] maint: multiple names: authors list ( link )...\n[KEPT 2] : authors list ( link )...\n[KEPT 3] 2006 video game Culdcept Saga Developers OmiyaSoft , Jamsworks Publisher Namco Bandai Games Writer Tow Ubukata Composer Kenji Ito Platform Xbox 360 Release JP : November 22, 2006 NA : February 5, 2008 Genres Board game , Collectible card game Modes Single-player , multiplayer Culdcept Saga ( カルドセプト ...\n[KEPT 4] hs of Japan (1946–2019) Population pyramids by prefecture Population pyramids of Japan's prefectures in 2020 Tokyo Nagasaki Hiroshima Hokkaido Kyoto Aichi Fukushima Osaka Okinawa Aomori Akita Chiba Ibaraki Miyagi Yamagata Iwate Fukuoka Yamaguchi Saga Okayama Toyama Hyogo Ishikawa Niigata Fukui Ehime...\n[KEPT 5] itan Government • Governor Yuriko Koike ( indp. ) • Representatives 42 • Councilors 11 Area • Total 2,194 km 2 (847 sq mi) • Metro 13,452 km 2 (5,194 sq mi) • Rank 45th in Japan Highest elevation 2,017 m (6,617 ft) Lowest elevation 0 m (0 ft) Population (May 2025) • Total 14,254,039 • Rank 1st in Ja...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 3 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 827, + "osl": 466, + "total_tokens": 1293, + "latency_ms": 9994.73, + "tokens_per_second": 46.62 + }, + "context": { + "num_documents_evaluated": 3, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 5 + } + }, + { + "call_id": "d7b40b80-e578-4660-b586-f2293df37718", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:05:12.878027Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 1, 2024, what is the population of the writer of the \"Culdcept Saga\"'s birthplace? Write the answer to the nearest million, in characters.\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] maint: multiple names: authors list ( link )\n\n[DOC 2] : authors list ( link )\n\n[DOC 3] 2006 video game Culdcept Saga Developers OmiyaSoft , Jamsworks Publisher Namco Bandai Games Writer Tow Ubukata Composer Kenji Ito Platform Xbox 360 Release JP : November 22, 2006 NA : February 5, 2008 Genres Board game , Collectible card game Modes Single-player , multiplayer Culdcept Saga ( カルドセプト サーガ , Karudoseputo Sāga ) is a video game in the Culdcept series developed exclusively for the Xbox 360 video game console . It is the first entry for a Microsoft console in the franchise. A demo of the title was made available to Xbox Live users on December 4, 2007. The demo includes two different pre-made card decks and supports both single player gameplay and local multiplayer for up to four players. Initially released in Japan in 2006, the full game wasn't rel\n\n[DOC 4] hs of Japan (1946–2019) Population pyramids by prefecture Population pyramids of Japan's prefectures in 2020 Tokyo Nagasaki Hiroshima Hokkaido Kyoto Aichi Fukushima Osaka Okinawa Aomori Akita Chiba Ibaraki Miyagi Yamagata Iwate Fukuoka Yamaguchi Saga Okayama Toyama Hyogo Ishikawa Niigata Fukui Ehime Tokushima Kagawa Miyazaki Kumamoto Kagoshima Kochi Yamanashi Oita Kanagawa Shizuoka Mie Wakayama Saitama Nara Tochigi Nagano Gunma Shiga Gifu Tottori Shimane Population estimates by sex and age group (01. VII.2020) (Because of rounding, totals are not in all cases the sum of the respective components. Estimates or projections based on the 2015 population census.): Age group Male Female Total % Total 61 226 000 64 610 000 125 836 000 100 0–4 2 406 000 2 288 000 4\n\n[DOC 5] itan Government • Governor Yuriko Koike ( indp. ) • Representatives 42 • Councilors 11 Area • Total 2,194 km 2 (847 sq mi) • Metro 13,452 km 2 (5,194 sq mi) • Rank 45th in Japan Highest elevation 2,017 m (6,617 ft) Lowest elevation 0 m (0 ft) Population (May 2025) • Total 14,254,039 • Rank 1st in Japan • Density 6,363/km 2 (16,480/sq mi) • Urban 39,105,000 • Metro 41,000,000 • Metro density 3,000/km 2 (7,900/sq mi) • Dialects Tokyo Tama Northern Izu Islands Demonym Tokyoite GDP (nominal, 2022) • Total ¥120.22 trillion (2022) ( US$ 914.23 billion ) • Metro ¥239.12 trillion (2022) ( US$ 1.82 trillion ) Time zone UTC+09:00 ( Japan Standard Time ) ISO 3166-2 JP-13 Climate Humid subtropical climate (Cfa) Flower Yoshino cherry Tree Ginkgo Bird Black-headed gull We\n\n[DOC 6] Japanese writer Tow Ubukata Ubukata in 2019 Native name 冲方 丁 Born Mineo Fujino (藤野峰男) ( 1977-02-14 ) February 14, 1977 (age 49) Gifu Prefecture , Japan Occupation Novelist screenwriter Language Japanese Mineo Fujino ( Japanese : 藤野峰男 , Hepburn : Fujino Mineo ; born February 14, 1977, in Gifu Prefecture ) , known by his pen name Tow Ubukata ( 冲方 丁 , Ubukata Tō ) , is a Japanese novelist and screenwriter. He primarily writes fantasy and science fiction. His major works include Mardock Scramble , Le Chevalier D'Eon and Heroic Age . He also did series composition for the Fafner in the Azure series, Ghost in the Shell: Arise , Psycho-Pass 2 and Psycho-Pass 3 . Early life Ubukata was raised in Singapore and Nepal. Career In high school, Ubukata received several wr\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"sufficient\": false, \"reasoning\": \"We have identified the writer as Tow Ubukata and his birthplace as Gifu Prefecture, Japan (Doc 6). However, none of the kept documents provide the population of Gifu Prefecture as of August 1, 2024 (or any recent date). Without that demographic figure, we cannot compute the answer to the nearest million.\"}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1261, + "osl": 408, + "total_tokens": 1669, + "latency_ms": 2679.5, + "tokens_per_second": 152.27 + }, + "context": { + "kept_docs_count": 6, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "4fcc1d6f-ad04-4265-acf1-b2aff4dc9c25", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:05:15.404015Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: As of August 1, 2024, what is the population of the writer of the \"Culdcept Saga\"'s birthplace? Write the answer to the nearest million, in characters.\n\nDOCUMENTS:\n\n[DOC 1] maint: multiple names: authors list ( link )\n\n[DOC 2] : authors list ( link )\n\n[DOC 3] 2006 video game Culdcept Saga Developers OmiyaSoft , Jamsworks Publisher Namco Bandai Games Writer Tow Ubukata Composer Kenji Ito Platform Xbox 360 Release JP : November 22, 2006 NA : February 5, 2008 Genres Board game , Collectible card game Modes Single-player , multiplayer Culdcept Saga ( カルドセプト サーガ , Karudoseputo Sāga ) is a video game in the Culdcept series developed exclusively for the Xbox 360 video game console . It is the first entry for a Microsoft console in the franchise. A demo of the title was made available to Xbox Live users on December 4, 2007. The demo includes two different pre-made card decks and supports both single player gameplay and local multiplayer for up to four players. Initially released in Japan in 2006, the full game wasn't rel\n\n[DOC 4] hs of Japan (1946–2019) Population pyramids by prefecture Population pyramids of Japan's prefectures in 2020 Tokyo Nagasaki Hiroshima Hokkaido Kyoto Aichi Fukushima Osaka Okinawa Aomori Akita Chiba Ibaraki Miyagi Yamagata Iwate Fukuoka Yamaguchi Saga Okayama Toyama Hyogo Ishikawa Niigata Fukui Ehime Tokushima Kagawa Miyazaki Kumamoto Kagoshima Kochi Yamanashi Oita Kanagawa Shizuoka Mie Wakayama Saitama Nara Tochigi Nagano Gunma Shiga Gifu Tottori Shimane Population estimates by sex and age group (01. VII.2020) (Because of rounding, totals are not in all cases the sum of the respective components. Estimates or projections based on the 2015 population census.): Age group Male Female Total % Total 61 226 000 64 610 000 125 836 000 100 0–4 2 406 000 2 288 000 4\n\n[DOC 5] itan Government • Governor Yuriko Koike ( indp. ) • Representatives 42 • Councilors 11 Area • Total 2,194 km 2 (847 sq mi) • Metro 13,452 km 2 (5,194 sq mi) • Rank 45th in Japan Highest elevation 2,017 m (6,617 ft) Lowest elevation 0 m (0 ft) Population (May 2025) • Total 14,254,039 • Rank 1st in Japan • Density 6,363/km 2 (16,480/sq mi) • Urban 39,105,000 • Metro 41,000,000 • Metro density 3,000/km 2 (7,900/sq mi) • Dialects Tokyo Tama Northern Izu Islands Demonym Tokyoite GDP (nominal, 2022) • Total ¥120.22 trillion (2022) ( US$ 914.23 billion ) • Metro ¥239.12 trillion (2022) ( US$ 1.82 trillion ) Time zone UTC+09:00 ( Japan Standard Time ) ISO 3166-2 JP-13 Climate Humid subtropical climate (Cfa) Flower Yoshino cherry Tree Ginkgo Bird Black-headed gull We\n\n[DOC 6] Japanese writer Tow Ubukata Ubukata in 2019 Native name 冲方 丁 Born Mineo Fujino (藤野峰男) ( 1977-02-14 ) February 14, 1977 (age 49) Gifu Prefecture , Japan Occupation Novelist screenwriter Language Japanese Mineo Fujino ( Japanese : 藤野峰男 , Hepburn : Fujino Mineo ; born February 14, 1977, in Gifu Prefecture ) , known by his pen name Tow Ubukata ( 冲方 丁 , Ubukata Tō ) , is a Japanese novelist and screenwriter. He primarily writes fantasy and science fiction. His major works include Mardock Scramble , Le Chevalier D'Eon and Heroic Age . He also did series composition for the Fafner in the Azure series, Ghost in the Shell: Arise , Psycho-Pass 2 and Psycho-Pass 3 . Early life Ubukata was raised in Singapore and Nepal. Career In high school, Ubukata received several wr\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1077, + "osl": 1, + "total_tokens": 1078, + "latency_ms": 2525.39, + "tokens_per_second": 0.4 + }, + "context": { + "num_documents_used": 6, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:05:15.404904Z", + "summary": { + "total_llm_calls": 12, + "total_hops": 5, + "total_input_tokens": 13991, + "total_output_tokens": 6073, + "total_tokens": 20064, + "total_latency_ms": 66057.52, + "average_tokens_per_second": 120.58, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Culdcept_Saga", + "https://en.wikipedia.org/wiki/Tow_Ubukata", + "https://en.wikipedia.org/wiki/Gifu_Prefecture" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "Two million." + } + }, + { + "query_id": "31", + "original_query": "What is the middle name of the U.S. president who died on the same day of the year as Virginia Woolf?", + "timestamp_start": "2026-05-16T03:05:15.433250Z", + "llm_calls": [ + { + "call_id": "ddfbec2b-7c2f-48d5-a096-98d9bb257980", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:05:44.598951Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What is the middle name of the U.S. president who died on the same day of the year as Virginia Woolf?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"U.S. presidents died March 28\",\n \"Dwight Eisenhower middle name\",\n \"Dwight D. Eisenhower full name\"\n ],\n \"feedback\": \"First query finds the president who died on March 28 (Dwight D. Eisenhower). Subsequent queries retrieve his middle name (David).\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 447, + "osl": 799, + "total_tokens": 1246, + "latency_ms": 29165.54, + "tokens_per_second": 27.4 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "57788460-6a78-4880-bf59-51878a535e69", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:06:14.413563Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What is the middle name of the U.S. president who died on the same day of the year as Virginia Woolf?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] ohnson Grant Hayes Garfield Cleveland B. Harrison T. Roosevelt Harding Coolidge F. D. Roosevelt Truman Kennedy L. B. Johnson Ford Carter Reagan G. H. W. Bush Map denoting where each of the 40 deceased presidents of the United States died Presidents that died in New York City (4): Monroe, Arthur, Hoover, Nixon Presidents that died in Washington, D. C. (7): J. Q. Adams, W. H. Harrison, Taylor, Lincoln, Taft, Wilson, Eisenhower Died same day, date, year, age Same day July 4, 1826: Thomas Jefferson at 12:50 p. m., and John Adams at 6:20 p. m. Same date March 8: Millard Fillmore in 1874 and William Howard Taft in 1930 July 4: John Adams and Thomas Jefferson in 1826, and James Monroe in 1831 December 26: Harry S. Truman in 1972 and Gerald Ford in 2006 Same calenda\n\n[DOC 2] Presidents of the United States who died in office William Henry Harrison April 4, 1841 Zachary Taylor July 9, 1850 Abraham Lincoln April 15, 1865 James A. Garfield September 19, 1881 William McKinley September 14, 1901 Warren G. Harding August 2, 1923 Franklin D. Roosevelt April 12, 1945 John F. Kennedy November 22, 1963 Since the office was established in 1789, 45 individuals have served as president of the United States . Of these, eight have died in office, of whom four were assassinated and four died of natural causes. In each of these instances, the vice president has succeeded to the presidency. This practice is now governed by Section One of the Twenty-fifth Amendment to the United States Constitution , ratified in 1967, which declares that, \"the Vic\n\n[DOC 3] dge 16 Abraham Lincoln (1809–1865) March 4, 1861 – April 15, 1865 Republican 1860 Hannibal Hamlin National Union 1864 Andrew Johnson 17 Andrew Johnson (1808–1875) April 15, 1865 – March 4, 1869 National Union – Vacant throughout presidency Democratic 18 Ulysses S. Grant (1822–1885) March 4, 1869 – March 4, 1877 Republican 1868 Schuyler Colfax 1872 Henry Wilson Vacant after November 22, 1875 19 Rutherford B. Hayes (1822–1893) March 4, 1877 – March 4, 1881 Republican 1876 William A. Wheeler 20 James A. Garfield (1831–1881) March 4, 1881 – September 19, 1881 Republican 1880 Chester A. Arthur 21 Chester A. Arthur (1829–1886) September 19, 1881 – March 4, 1885 Republican – Vacant throughout presidency 22 Grover Cleveland (1837–1908) 1st term March 4, 1885 – March\n\n[DOC 4] sons born to Ida and David. His mother soon reversed his two forenames after his birth to avoid the confusion of having two Davids in the family. He was named Dwight after the evangelist Dwight L. Moody . All of the boys were nicknamed \"Ike\", such as \"Big Ike\" ( Edgar ) and \"Little Ike\" (Dwight); the nickname was intended as an abbreviation of their last name. By World War II, only Dwight was still called \"Ike\". In 1892, the family moved to Abilene, Kansas , which Eisenhower considered his hometown. As a child, he was involved in an accident that cost his younger brother Earl an eye, for which he was remorseful for the remainder of his life. Eisenhower developed a keen and enduring interest in exploring the outdoors. He learned about hunting and fishing, co\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"sufficient\": false, \"reasoning\": \"Missing: Virginia Woolf's death date (March 28) and confirmation of a U.S. president who died on that same day (e.g., Dwight D. Eisenhower's death date March 28, 1969) as well as the president's middle name. The provided documents do not contain Virginia Woolf's death date, nor do they explicitly state Eisenhower's middle name \\\"David\\\".\"}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1099, + "osl": 93, + "total_tokens": 1192, + "latency_ms": 9217.78, + "tokens_per_second": 10.09 + }, + "context": { + "kept_docs_count": 4, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "06566a37-2af4-4f15-9a41-6eeb81ab5848", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:06:25.957222Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What is the middle name of the U.S. president who died on the same day of the year as Virginia Woolf?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] ohnson Grant Hayes Garfield Cleveland B. Harrison T. Roosevelt Harding Coolidge F. D. Roosevelt Truman Kennedy L. B. Johnson Ford Carter Reagan G. H. W. Bush Map denoting where each of the 40 deceased presidents of the United States died Presidents that died in New York City (4): Monroe, Arthur, Hoover, Nixon Presidents that died in Washington, D. C. (7): J. Q. Adams, W. H. Harrison, Taylor, Lincoln, Taft, Wilson, Eisenhower Died same day, date, year, age Same day July 4, 1826: Thomas Jefferson at 12:50 p. m., and John Adams at 6:20 p. m. Same date March 8: Millard Fillmore in 1874 and William Howard Taft in 1930 July 4: John Adams and Thomas Jefferson in 1826, and James Monroe in 1831 December 26: Harry S. Truman in 1972 and Gerald Ford in 2006 Same calenda\n\n[DOC 2] Presidents of the United States who died in office William Henry Harrison April 4, 1841 Zachary Taylor July 9, 1850 Abraham Lincoln April 15, 1865 James A. Garfield September 19, 1881 William McKinley September 14, 1901 Warren G. Harding August 2, 1923 Franklin D. Roosevelt April 12, 1945 John F. Kennedy November 22, 1963 Since the office was established in 1789, 45 individuals have served as president of the United States . Of these, eight have died in office, of whom four were assassinated and four died of natural causes. In each of these instances, the vice president has succeeded to the presidency. This practice is now governed by Section One of the Twenty-fifth Amendment to the United States Constitution , ratified in 1967, which declares that, \"the Vic\n\n[DOC 3] dge 16 Abraham Lincoln (1809–1865) March 4, 1861 – April 15, 1865 Republican 1860 Hannibal Hamlin National Union 1864 Andrew Johnson 17 Andrew Johnson (1808–1875) April 15, 1865 – March 4, 1869 National Union – Vacant throughout presidency Democratic 18 Ulysses S. Grant (1822–1885) March 4, 1869 – March 4, 1877 Republican 1868 Schuyler Colfax 1872 Henry Wilson Vacant after November 22, 1875 19 Rutherford B. Hayes (1822–1893) March 4, 1877 – March 4, 1881 Republican 1876 William A. Wheeler 20 James A. Garfield (1831–1881) March 4, 1881 – September 19, 1881 Republican 1880 Chester A. Arthur 21 Chester A. Arthur (1829–1886) September 19, 1881 – March 4, 1885 Republican – Vacant throughout presidency 22 Grover Cleveland (1837–1908) 1st term March 4, 1885 – March\n\n[DOC 4] sons born to Ida and David. His mother soon reversed his two forenames after his birth to avoid the confusion of having two Davids in the family. He was named Dwight after the evangelist Dwight L. Moody . All of the boys were nicknamed \"Ike\", such as \"Big Ike\" ( Edgar ) and \"Little Ike\" (Dwight); the nickname was intended as an abbreviation of their last name. By World War II, only Dwight was still called \"Ike\". In 1892, the family moved to Abilene, Kansas , which Eisenhower considered his hometown. As a child, he was involved in an accident that cost his younger brother Earl an eye, for which he was remorseful for the remainder of his life. Eisenhower developed a keen and enduring interest in exploring the outdoors. He learned about hunting and fishing, co\n\n\nSEARCH HISTORY: - Query: 'U.S. presidents died March 28' → 3 docs\n- Query: 'Dwight Eisenhower middle name' → 1 docs\n- Query: 'Dwight D. Eisenhower full name' → 0 docs\n\nFEEDBACK: First query finds the president who died on March 28 (Dwight D. Eisenhower). Subsequent queries retrieve his middle name (David).\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Dwight Eisenhower\",\n \"Eisenhower middle name\",\n \"President Eisenhower full name\"\n ],\n \"feedback\": \"Previous attempts to get Eisenhower's middle name failed with overly specific queries. These broader queries should retrieve the Wikipedia article where his full name (Dwight David Eisenhower) is listed.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1376, + "osl": 493, + "total_tokens": 1869, + "latency_ms": 11542.97, + "tokens_per_second": 42.71 + }, + "context": { + "kept_docs_count": 4, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "3c0d99a3-e4b9-4329-9632-70691ac8a769", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:07:12.449407Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What is the middle name of the U.S. president who died on the same day of the year as Virginia Woolf?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] ohnson Grant Hayes Garfield Cleveland B. Harrison T. Roosevelt Harding Coolidge F. D. Roosevelt Truman Kennedy L. B. Johnson Ford Carter Reagan G. H. W. Bush Map denoting where each of the 40 deceased presidents of the United States died Presidents that died in New York City (4): Monroe, Arthur, Hoover, Nixon Presidents that died in Washington, D. C. (7): J. Q. Adams, W. H. Harrison, Taylor, Lincoln, Taft, Wilson, Eisenhower Died same day, date, year, age Same day July 4, 1826: Thomas Jefferson at 12:50 p. m., and John Adams at 6:20 p. m. Same date March 8: Millard Fillmore in 1874 and William Howard Taft in 1930 July 4: John Adams and Thomas Jefferson in 1826, and James Monroe in 1831 December 26: Harry S. Truman in 1972 and Gerald Ford in 2006 Same calenda\n\n[DOC 2] Presidents of the United States who died in office William Henry Harrison April 4, 1841 Zachary Taylor July 9, 1850 Abraham Lincoln April 15, 1865 James A. Garfield September 19, 1881 William McKinley September 14, 1901 Warren G. Harding August 2, 1923 Franklin D. Roosevelt April 12, 1945 John F. Kennedy November 22, 1963 Since the office was established in 1789, 45 individuals have served as president of the United States . Of these, eight have died in office, of whom four were assassinated and four died of natural causes. In each of these instances, the vice president has succeeded to the presidency. This practice is now governed by Section One of the Twenty-fifth Amendment to the United States Constitution , ratified in 1967, which declares that, \"the Vic\n\n[DOC 3] dge 16 Abraham Lincoln (1809–1865) March 4, 1861 – April 15, 1865 Republican 1860 Hannibal Hamlin National Union 1864 Andrew Johnson 17 Andrew Johnson (1808–1875) April 15, 1865 – March 4, 1869 National Union – Vacant throughout presidency Democratic 18 Ulysses S. Grant (1822–1885) March 4, 1869 – March 4, 1877 Republican 1868 Schuyler Colfax 1872 Henry Wilson Vacant after November 22, 1875 19 Rutherford B. Hayes (1822–1893) March 4, 1877 – March 4, 1881 Republican 1876 William A. Wheeler 20 James A. Garfield (1831–1881) March 4, 1881 – September 19, 1881 Republican 1880 Chester A. Arthur 21 Chester A. Arthur (1829–1886) September 19, 1881 – March 4, 1885 Republican – Vacant throughout presidency 22 Grover Cleveland (1837–1908) 1st term March 4, 1885 – March\n\n[DOC 4] sons born to Ida and David. His mother soon reversed his two forenames after his birth to avoid the confusion of having two Davids in the family. He was named Dwight after the evangelist Dwight L. Moody . All of the boys were nicknamed \"Ike\", such as \"Big Ike\" ( Edgar ) and \"Little Ike\" (Dwight); the nickname was intended as an abbreviation of their last name. By World War II, only Dwight was still called \"Ike\". In 1892, the family moved to Abilene, Kansas , which Eisenhower considered his hometown. As a child, he was involved in an accident that cost his younger brother Earl an eye, for which he was remorseful for the remainder of his life. Eisenhower developed a keen and enduring interest in exploring the outdoors. He learned about hunting and fishing, co\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Documents provide Eisenhower's middle name (David) but do not contain any information linking a U.S. president's death date to March 28, the day Virginia Woolf died. Without a document showing that Eisenhower (or any other president) died on the same day of the year as Virginia Woolf, we cannot answer the question.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1146, + "osl": 1087, + "total_tokens": 2233, + "latency_ms": 45930.45, + "tokens_per_second": 23.67 + }, + "context": { + "kept_docs_count": 4, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "1b3614ff-c65b-4e9c-9f50-c7553bd0df28", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:07:16.938135Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What is the middle name of the U.S. president who died on the same day of the year as Virginia Woolf?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] ohnson Grant Hayes Garfield Cleveland B. Harrison T. Roosevelt Harding Coolidge F. D. Roosevelt Truman Kennedy L. B. Johnson Ford Carter Reagan G. H. W. Bush Map denoting where each of the 40 deceased presidents of the United States died Presidents that died in New York City (4): Monroe, Arthur, Hoover, Nixon Presidents that died in Washington, D. C. (7): J. Q. Adams, W. H. Harrison, Taylor, Lincoln, Taft, Wilson, Eisenhower Died same day, date, year, age Same day July 4, 1826: Thomas Jefferson at 12:50 p. m., and John Adams at 6:20 p. m. Same date March 8: Millard Fillmore in 1874 and William Howard Taft in 1930 July 4: John Adams and Thomas Jefferson in 1826, and James Monroe in 1831 December 26: Harry S. Truman in 1972 and Gerald Ford in 2006 Same calenda\n\n[DOC 2] Presidents of the United States who died in office William Henry Harrison April 4, 1841 Zachary Taylor July 9, 1850 Abraham Lincoln April 15, 1865 James A. Garfield September 19, 1881 William McKinley September 14, 1901 Warren G. Harding August 2, 1923 Franklin D. Roosevelt April 12, 1945 John F. Kennedy November 22, 1963 Since the office was established in 1789, 45 individuals have served as president of the United States . Of these, eight have died in office, of whom four were assassinated and four died of natural causes. In each of these instances, the vice president has succeeded to the presidency. This practice is now governed by Section One of the Twenty-fifth Amendment to the United States Constitution , ratified in 1967, which declares that, \"the Vic\n\n[DOC 3] dge 16 Abraham Lincoln (1809–1865) March 4, 1861 – April 15, 1865 Republican 1860 Hannibal Hamlin National Union 1864 Andrew Johnson 17 Andrew Johnson (1808–1875) April 15, 1865 – March 4, 1869 National Union – Vacant throughout presidency Democratic 18 Ulysses S. Grant (1822–1885) March 4, 1869 – March 4, 1877 Republican 1868 Schuyler Colfax 1872 Henry Wilson Vacant after November 22, 1875 19 Rutherford B. Hayes (1822–1893) March 4, 1877 – March 4, 1881 Republican 1876 William A. Wheeler 20 James A. Garfield (1831–1881) March 4, 1881 – September 19, 1881 Republican 1880 Chester A. Arthur 21 Chester A. Arthur (1829–1886) September 19, 1881 – March 4, 1885 Republican – Vacant throughout presidency 22 Grover Cleveland (1837–1908) 1st term March 4, 1885 – March\n\n[DOC 4] sons born to Ida and David. His mother soon reversed his two forenames after his birth to avoid the confusion of having two Davids in the family. He was named Dwight after the evangelist Dwight L. Moody . All of the boys were nicknamed \"Ike\", such as \"Big Ike\" ( Edgar ) and \"Little Ike\" (Dwight); the nickname was intended as an abbreviation of their last name. By World War II, only Dwight was still called \"Ike\". In 1892, the family moved to Abilene, Kansas , which Eisenhower considered his hometown. As a child, he was involved in an accident that cost his younger brother Earl an eye, for which he was remorseful for the remainder of his life. Eisenhower developed a keen and enduring interest in exploring the outdoors. He learned about hunting and fishing, co\n\n\nSEARCH HISTORY: - Query: 'U.S. presidents died March 28' → 3 docs\n- Query: 'Dwight Eisenhower middle name' → 1 docs\n- Query: 'Dwight D. Eisenhower full name' → 0 docs\n- Query: 'Dwight Eisenhower' → 0 docs\n- Query: 'Eisenhower middle name' → 0 docs\n- Query: 'President Eisenhower full name' → 0 docs\n\nFEEDBACK: First query finds the president who died on March 28 (Dwight D. Eisenhower). Subsequent queries retrieve his middle name (David).\nPrevious attempts to get Eisenhower's middle name failed with overly specific queries. These broader queries should retrieve the Wikipedia article where his full name (Dwight David Eisenhower) is listed.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Dwight Eisenhower biography\",\n \"Eisenhower full name\",\n \"Dwight David Eisenhower\"\n ],\n \"feedback\": \"Previous attempts with overly specific phrasing returned no results. These broader queries target Eisenhower's main article, where his full name (including middle name) is listed.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1468, + "osl": 495, + "total_tokens": 1963, + "latency_ms": 4487.67, + "tokens_per_second": 110.3 + }, + "context": { + "kept_docs_count": 4, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "425d8747-abdc-4383-8d8a-25044a0dca71", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:07:19.204063Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What is the middle name of the U.S. president who died on the same day of the year as Virginia Woolf?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] ohnson Grant Hayes Garfield Cleveland B. Harrison T. Roosevelt Harding Coolidge F. D. Roosevelt Truman Kennedy L. B. Johnson Ford Carter Reagan G. H. W. Bush Map denoting where each of the 40 deceased presidents of the United States died Presidents that died in New York City (4): Monroe, Arthur, Hoover, Nixon Presidents that died in Washington, D. C. (7): J. Q. Adams, W. H. Harrison, Taylor, Lincoln, Taft, Wilson, Eisenhower Died same day, date, year, age Same day July 4, 1826: Thomas Jefferson at 12:50 p. m., and John Adams at 6:20 p. m. Same date March 8: Millard Fillmore in 1874 and William Howard Taft in 1930 July 4: John Adams and Thomas Jefferson in 1826, and James Monroe in 1831 December 26: Harry S. Truman in 1972 and Gerald Ford in 2006 Same calenda\n\n[DOC 2] Presidents of the United States who died in office William Henry Harrison April 4, 1841 Zachary Taylor July 9, 1850 Abraham Lincoln April 15, 1865 James A. Garfield September 19, 1881 William McKinley September 14, 1901 Warren G. Harding August 2, 1923 Franklin D. Roosevelt April 12, 1945 John F. Kennedy November 22, 1963 Since the office was established in 1789, 45 individuals have served as president of the United States . Of these, eight have died in office, of whom four were assassinated and four died of natural causes. In each of these instances, the vice president has succeeded to the presidency. This practice is now governed by Section One of the Twenty-fifth Amendment to the United States Constitution , ratified in 1967, which declares that, \"the Vic\n\n[DOC 3] dge 16 Abraham Lincoln (1809–1865) March 4, 1861 – April 15, 1865 Republican 1860 Hannibal Hamlin National Union 1864 Andrew Johnson 17 Andrew Johnson (1808–1875) April 15, 1865 – March 4, 1869 National Union – Vacant throughout presidency Democratic 18 Ulysses S. Grant (1822–1885) March 4, 1869 – March 4, 1877 Republican 1868 Schuyler Colfax 1872 Henry Wilson Vacant after November 22, 1875 19 Rutherford B. Hayes (1822–1893) March 4, 1877 – March 4, 1881 Republican 1876 William A. Wheeler 20 James A. Garfield (1831–1881) March 4, 1881 – September 19, 1881 Republican 1880 Chester A. Arthur 21 Chester A. Arthur (1829–1886) September 19, 1881 – March 4, 1885 Republican – Vacant throughout presidency 22 Grover Cleveland (1837–1908) 1st term March 4, 1885 – March\n\n[DOC 4] sons born to Ida and David. His mother soon reversed his two forenames after his birth to avoid the confusion of having two Davids in the family. He was named Dwight after the evangelist Dwight L. Moody . All of the boys were nicknamed \"Ike\", such as \"Big Ike\" ( Edgar ) and \"Little Ike\" (Dwight); the nickname was intended as an abbreviation of their last name. By World War II, only Dwight was still called \"Ike\". In 1892, the family moved to Abilene, Kansas , which Eisenhower considered his hometown. As a child, he was involved in an accident that cost his younger brother Earl an eye, for which he was remorseful for the remainder of his life. Eisenhower developed a keen and enduring interest in exploring the outdoors. He learned about hunting and fishing, co\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents mention Eisenhower's biography but do not provide his middle name (David) nor his death date (March 28, 1969). Without both the death-date match to Virginia Woolf and the middle name, the answer cannot be derived from the available information.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1163, + "osl": 720, + "total_tokens": 1883, + "latency_ms": 1707.2, + "tokens_per_second": 421.74 + }, + "context": { + "kept_docs_count": 4, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "651749a6-d0be-4b3c-ae3c-1ef9c3793638", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:07:23.955243Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What is the middle name of the U.S. president who died on the same day of the year as Virginia Woolf?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] ohnson Grant Hayes Garfield Cleveland B. Harrison T. Roosevelt Harding Coolidge F. D. Roosevelt Truman Kennedy L. B. Johnson Ford Carter Reagan G. H. W. Bush Map denoting where each of the 40 deceased presidents of the United States died Presidents that died in New York City (4): Monroe, Arthur, Hoover, Nixon Presidents that died in Washington, D. C. (7): J. Q. Adams, W. H. Harrison, Taylor, Lincoln, Taft, Wilson, Eisenhower Died same day, date, year, age Same day July 4, 1826: Thomas Jefferson at 12:50 p. m., and John Adams at 6:20 p. m. Same date March 8: Millard Fillmore in 1874 and William Howard Taft in 1930 July 4: John Adams and Thomas Jefferson in 1826, and James Monroe in 1831 December 26: Harry S. Truman in 1972 and Gerald Ford in 2006 Same calenda\n\n[DOC 2] Presidents of the United States who died in office William Henry Harrison April 4, 1841 Zachary Taylor July 9, 1850 Abraham Lincoln April 15, 1865 James A. Garfield September 19, 1881 William McKinley September 14, 1901 Warren G. Harding August 2, 1923 Franklin D. Roosevelt April 12, 1945 John F. Kennedy November 22, 1963 Since the office was established in 1789, 45 individuals have served as president of the United States . Of these, eight have died in office, of whom four were assassinated and four died of natural causes. In each of these instances, the vice president has succeeded to the presidency. This practice is now governed by Section One of the Twenty-fifth Amendment to the United States Constitution , ratified in 1967, which declares that, \"the Vic\n\n[DOC 3] dge 16 Abraham Lincoln (1809–1865) March 4, 1861 – April 15, 1865 Republican 1860 Hannibal Hamlin National Union 1864 Andrew Johnson 17 Andrew Johnson (1808–1875) April 15, 1865 – March 4, 1869 National Union – Vacant throughout presidency Democratic 18 Ulysses S. Grant (1822–1885) March 4, 1869 – March 4, 1877 Republican 1868 Schuyler Colfax 1872 Henry Wilson Vacant after November 22, 1875 19 Rutherford B. Hayes (1822–1893) March 4, 1877 – March 4, 1881 Republican 1876 William A. Wheeler 20 James A. Garfield (1831–1881) March 4, 1881 – September 19, 1881 Republican 1880 Chester A. Arthur 21 Chester A. Arthur (1829–1886) September 19, 1881 – March 4, 1885 Republican – Vacant throughout presidency 22 Grover Cleveland (1837–1908) 1st term March 4, 1885 – March\n\n[DOC 4] sons born to Ida and David. His mother soon reversed his two forenames after his birth to avoid the confusion of having two Davids in the family. He was named Dwight after the evangelist Dwight L. Moody . All of the boys were nicknamed \"Ike\", such as \"Big Ike\" ( Edgar ) and \"Little Ike\" (Dwight); the nickname was intended as an abbreviation of their last name. By World War II, only Dwight was still called \"Ike\". In 1892, the family moved to Abilene, Kansas , which Eisenhower considered his hometown. As a child, he was involved in an accident that cost his younger brother Earl an eye, for which he was remorseful for the remainder of his life. Eisenhower developed a keen and enduring interest in exploring the outdoors. He learned about hunting and fishing, co\n\n\nSEARCH HISTORY: - Query: 'U.S. presidents died March 28' → 3 docs\n- Query: 'Dwight Eisenhower middle name' → 1 docs\n- Query: 'Dwight D. Eisenhower full name' → 0 docs\n- Query: 'Dwight Eisenhower' → 0 docs\n- Query: 'Eisenhower middle name' → 0 docs\n- Query: 'President Eisenhower full name' → 0 docs\n- Query: 'Dwight Eisenhower biography' → 0 docs\n- Query: 'Eisenhower full name' → 0 docs\n- Query: 'Dwight David Eisenhower' → 0 docs\n\nFEEDBACK: First query finds the president who died on March 28 (Dwight D. Eisenhower). Subsequent queries retrieve his middle name (David).\nPrevious attempts to get Eisenhower's middle name failed with overly specific queries. These broader queries should retrieve the Wikipedia article where his full name (Dwight David Eisenhower) is listed.\nPrevious attempts with overly specific phrasing returned no results. These broader queries target Eisenhower's main article, where his full name (including middle name) is listed.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Dwight D. Eisenhower\",\n \"Eisenhower\",\n \"Dwight Eisenhower\"\n ],\n \"feedback\": \"We need Eisenhower's full name to obtain his middle name (David). The previous attempts used overly specific phrases and returned no results. A simple query with his common name (Dwight D. Eisenhower) or just the surname should retrieve the main Wikipedia article where the infobox lists his full name, including the middle name.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1548, + "osl": 468, + "total_tokens": 2016, + "latency_ms": 4750.46, + "tokens_per_second": 98.52 + }, + "context": { + "kept_docs_count": 4, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "231269e2-82c0-47bf-9268-f036d0e681be", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:07:27.474168Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What is the middle name of the U.S. president who died on the same day of the year as Virginia Woolf?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] ohnson Grant Hayes Garfield Cleveland B. Harrison T. Roosevelt Harding Coolidge F. D. Roosevelt Truman Kennedy L. B. Johnson Ford Carter Reagan G. H. W. Bush Map denoting where each of the 40 deceased presidents of the United States died Presidents that died in New York City (4): Monroe, Arthur, Hoover, Nixon Presidents that died in Washington, D. C. (7): J. Q. Adams, W. H. Harrison, Taylor, Lincoln, Taft, Wilson, Eisenhower Died same day, date, year, age Same day July 4, 1826: Thomas Jefferson at 12:50 p. m., and John Adams at 6:20 p. m. Same date March 8: Millard Fillmore in 1874 and William Howard Taft in 1930 July 4: John Adams and Thomas Jefferson in 1826, and James Monroe in 1831 December 26: Harry S. Truman in 1972 and Gerald Ford in 2006 Same calenda\n\n[DOC 2] Presidents of the United States who died in office William Henry Harrison April 4, 1841 Zachary Taylor July 9, 1850 Abraham Lincoln April 15, 1865 James A. Garfield September 19, 1881 William McKinley September 14, 1901 Warren G. Harding August 2, 1923 Franklin D. Roosevelt April 12, 1945 John F. Kennedy November 22, 1963 Since the office was established in 1789, 45 individuals have served as president of the United States . Of these, eight have died in office, of whom four were assassinated and four died of natural causes. In each of these instances, the vice president has succeeded to the presidency. This practice is now governed by Section One of the Twenty-fifth Amendment to the United States Constitution , ratified in 1967, which declares that, \"the Vic\n\n[DOC 3] dge 16 Abraham Lincoln (1809–1865) March 4, 1861 – April 15, 1865 Republican 1860 Hannibal Hamlin National Union 1864 Andrew Johnson 17 Andrew Johnson (1808–1875) April 15, 1865 – March 4, 1869 National Union – Vacant throughout presidency Democratic 18 Ulysses S. Grant (1822–1885) March 4, 1869 – March 4, 1877 Republican 1868 Schuyler Colfax 1872 Henry Wilson Vacant after November 22, 1875 19 Rutherford B. Hayes (1822–1893) March 4, 1877 – March 4, 1881 Republican 1876 William A. Wheeler 20 James A. Garfield (1831–1881) March 4, 1881 – September 19, 1881 Republican 1880 Chester A. Arthur 21 Chester A. Arthur (1829–1886) September 19, 1881 – March 4, 1885 Republican – Vacant throughout presidency 22 Grover Cleveland (1837–1908) 1st term March 4, 1885 – March\n\n[DOC 4] sons born to Ida and David. His mother soon reversed his two forenames after his birth to avoid the confusion of having two Davids in the family. He was named Dwight after the evangelist Dwight L. Moody . All of the boys were nicknamed \"Ike\", such as \"Big Ike\" ( Edgar ) and \"Little Ike\" (Dwight); the nickname was intended as an abbreviation of their last name. By World War II, only Dwight was still called \"Ike\". In 1892, the family moved to Abilene, Kansas , which Eisenhower considered his hometown. As a child, he was involved in an accident that cost his younger brother Earl an eye, for which he was remorseful for the remainder of his life. Eisenhower developed a keen and enduring interest in exploring the outdoors. He learned about hunting and fishing, co\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents provide some dates of presidents' deaths (e.g., March 8, July 4, December 26) but do not include the death date of Virginia Woolf (March 28). Without knowing which president died on the same day of the year as Virginia Woolf, we cannot determine that president's middle name.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1170, + "osl": 670, + "total_tokens": 1840, + "latency_ms": 2969.98, + "tokens_per_second": 225.59 + }, + "context": { + "kept_docs_count": 4, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "3cb9a9f8-d87a-4cf2-8883-fef7c139f9e7", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:07:32.530227Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: What is the middle name of the U.S. president who died on the same day of the year as Virginia Woolf?\n\nDOCUMENTS:\n\n[DOC 1] ohnson Grant Hayes Garfield Cleveland B. Harrison T. Roosevelt Harding Coolidge F. D. Roosevelt Truman Kennedy L. B. Johnson Ford Carter Reagan G. H. W. Bush Map denoting where each of the 40 deceased presidents of the United States died Presidents that died in New York City (4): Monroe, Arthur, Hoover, Nixon Presidents that died in Washington, D. C. (7): J. Q. Adams, W. H. Harrison, Taylor, Lincoln, Taft, Wilson, Eisenhower Died same day, date, year, age Same day July 4, 1826: Thomas Jefferson at 12:50 p. m., and John Adams at 6:20 p. m. Same date March 8: Millard Fillmore in 1874 and William Howard Taft in 1930 July 4: John Adams and Thomas Jefferson in 1826, and James Monroe in 1831 December 26: Harry S. Truman in 1972 and Gerald Ford in 2006 Same calenda\n\n[DOC 2] Presidents of the United States who died in office William Henry Harrison April 4, 1841 Zachary Taylor July 9, 1850 Abraham Lincoln April 15, 1865 James A. Garfield September 19, 1881 William McKinley September 14, 1901 Warren G. Harding August 2, 1923 Franklin D. Roosevelt April 12, 1945 John F. Kennedy November 22, 1963 Since the office was established in 1789, 45 individuals have served as president of the United States . Of these, eight have died in office, of whom four were assassinated and four died of natural causes. In each of these instances, the vice president has succeeded to the presidency. This practice is now governed by Section One of the Twenty-fifth Amendment to the United States Constitution , ratified in 1967, which declares that, \"the Vic\n\n[DOC 3] dge 16 Abraham Lincoln (1809–1865) March 4, 1861 – April 15, 1865 Republican 1860 Hannibal Hamlin National Union 1864 Andrew Johnson 17 Andrew Johnson (1808–1875) April 15, 1865 – March 4, 1869 National Union – Vacant throughout presidency Democratic 18 Ulysses S. Grant (1822–1885) March 4, 1869 – March 4, 1877 Republican 1868 Schuyler Colfax 1872 Henry Wilson Vacant after November 22, 1875 19 Rutherford B. Hayes (1822–1893) March 4, 1877 – March 4, 1881 Republican 1876 William A. Wheeler 20 James A. Garfield (1831–1881) March 4, 1881 – September 19, 1881 Republican 1880 Chester A. Arthur 21 Chester A. Arthur (1829–1886) September 19, 1881 – March 4, 1885 Republican – Vacant throughout presidency 22 Grover Cleveland (1837–1908) 1st term March 4, 1885 – March\n\n[DOC 4] sons born to Ida and David. His mother soon reversed his two forenames after his birth to avoid the confusion of having two Davids in the family. He was named Dwight after the evangelist Dwight L. Moody . All of the boys were nicknamed \"Ike\", such as \"Big Ike\" ( Edgar ) and \"Little Ike\" (Dwight); the nickname was intended as an abbreviation of their last name. By World War II, only Dwight was still called \"Ike\". In 1892, the family moved to Abilene, Kansas , which Eisenhower considered his hometown. As a child, he was involved in an accident that cost his younger brother Earl an eye, for which he was remorseful for the remainder of his life. Eisenhower developed a keen and enduring interest in exploring the outdoors. He learned about hunting and fishing, co\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1000, + "osl": 238, + "total_tokens": 1238, + "latency_ms": 5055.42, + "tokens_per_second": 47.08 + }, + "context": { + "num_documents_used": 4, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:07:32.531004Z", + "summary": { + "total_llm_calls": 9, + "total_hops": 5, + "total_input_tokens": 10417, + "total_output_tokens": 5063, + "total_tokens": 15480, + "total_latency_ms": 114827.47, + "average_tokens_per_second": 111.9, + "components_used": [ + "check_sufficiency", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Virginia_Woolf", + "https://en.wikipedia.org/wiki/List_of_presidents_of_the_United_States_by_date_of_death", + "https://en.wikipedia.org/wiki/Dwight_D._Eisenhower" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "David" + } + }, + { + "query_id": "32", + "original_query": "As of 2010, if you added the number of times Brazil had won the World Cup to the amount of times the Chicago Bulls had won the NBA Championship and multiplied this number by the amount of times the Dallas Cowboys had won the Super Bowl, what number are you left with?", + "timestamp_start": "2026-05-16T03:07:32.557596Z", + "llm_calls": [ + { + "call_id": "4a5d93b8-0970-4585-9e0f-9116cdad3027", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:07:37.102833Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of 2010, if you added the number of times Brazil had won the World Cup to the amount of times the Chicago Bulls had won the NBA Championship and multiplied this number by the amount of times the Dallas Cowboys had won the Super Bowl, what number are you left with?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Brazil World Cup titles\",\n \"Chicago Bulls championships\",\n \"Dallas Cowboys Super Bowl wins\"\n ],\n \"feedback\": \"Need Brazil's total World Cup victories (as of 2010), Chicago Bulls' total NBA championships (as of 2010), and Dallas Cowboys' total Super Bowl victories (as of 2010) to compute (Brazil wins + Bulls wins) × Cowboys wins.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 432, + "osl": 90, + "total_tokens": 522, + "latency_ms": 4545.08, + "tokens_per_second": 19.8 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "ebe5beda-10ca-458b-b62f-92718637230b", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:07:44.666151Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of 2010, if you added the number of times Brazil had won the World Cup to the amount of times the Chicago Bulls had won the NBA Championship and multiplied this number by the amount of times the Dallas Cowboys had won the Super Bowl, what number are you left with?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Year N. o Brazil Copa América 1919 1o Brazil Copa América 1922 2o Brazil Copa América 1949 3o Chile Panamerican Championship 1952 4o Mexico Panamerican Championship 1956 5o Sweden FIFA World Cup 1958 6o Chile FIFA World Cup 1962 7o Mexico FIFA World Cup 1970 8o Brazil Copa América 1989 9o United States FIFA World Cup 1994 10o Bolivia Copa América 1997 11o Saudi Arabia FIFA Confederations Cup 1997 12o Paraguay Copa América 1999 13o South Korea–Japan FIFA World Cup 2002 14o Peru Copa América 2004 15o Germany FIFA Confederations Cup 2005 16o Venezuela Copa América 2007 17o South Africa FIFA Confederations Cup 2009 18o Brazil FIFA Confederations Cup 2013 19o Brazil Copa América 2019 20o Summary Competition Total FIFA World Cup 5 2 2 9 FIFA Confederations Cup 4\n\n[NEW 2] Cup victory. Uruguay, however, chose to display four stars on their badge , representing their two gold medals at the 1924 and 1928 Summer Olympics, which are recognized by FIFA as World Championships, and their two World Cup titles in 1930 and 1950. With five titles, Brazil are the most successful World Cup team and also the only nation to have played in every World Cup (22) to date. Brazil were also the first team to win the World Cup for the third (1970), fourth (1994) and fifth (2002) time. Italy (1934 and 1938) and Brazil (1958 and 1962) are the only nations to have won consecutive titles. West Germany (1982–1990) and Brazil (1994–2002) are the only nations to appear in three consecutive World Cup finals. Germany has made the most top-four finishes (13)\n\n[NEW 3] o Bulls are an American professional basketball team based in Chicago . The Bulls compete in the National Basketball Association (NBA) as a member of the Central Division of the Eastern Conference . The team was founded on January 16, 1966, and played its first game during the 1966–67 NBA season . The Bulls share their home arena with the National Hockey League 's Chicago Blackhawks , playing in the newer United Center which replaced the former Chicago Stadium . The Bulls saw their greatest success during the 1990s when they played a major part in popularizing the NBA worldwide. They are known for having one of the NBA's greatest dynasties, winning six NBA championships between 1991 and 1998 with two separate three-peats . All six of their championship teams\n\n[NEW 4] Blackhawks have won six Stanley Cups , including in 2010, 2013, and 2015 . Both the Bulls and the Blackhawks play at the United Center . Major league professional teams in Chicago (ranked by attendance) Club League Sport Venue Attendance Founded Championships Chicago Bears NFL Football Soldier Field 61,142 1919 9 Championships (1 Super Bowl ) Chicago Cubs MLB Baseball Wrigley Field 41,649 1870 3 World Series Chicago White Sox MLB Baseball Rate Field 40,615 1900 3 World Series Chicago Blackhawks NHL Ice hockey United Center 21,653 1926 6 Stanley Cups Chicago Bulls NBA Basketball 20,776 1966 6 NBA Championships Chicago Fire MLS Soccer Soldier Field 17,383 1997 1 MLS Cup , 1 Supporters Shield Chicago Sky WNBA Basketball Wintrust Arena 10,387 2006 1 WNBA Champi\n\n[NEW 5] R 2006–2008 2018 17 Harold Carmichael WR 1984 2020 43 Cliff Harris S 1970–1979 2020 88 Drew Pearson WR 1973–1983 2021 54 Chuck Howley LB 1961–1973 2023 55 Zach Thomas LB 2008 2023 94 DeMarcus Ware LB 2005–2013 2023 Coaches and Contributors Name Position Seasons Inducted Tom Landry Coach 1960–1988 1990 Tex Schramm President/GM 1960–1988 1991 Bill Parcells Coach 2003–2006 2013 Jerry Jones Owner/Executive 1989–present 2017 Gil Brandt Executive 1960–1988 2019 Jimmy Johnson Coach 1989–1993 2020 Super Bowl MVPs The Cowboys have had seven players win Super Bowl MVP. Super Bowl MVP Winners Super Bowl Player Position V Chuck Howley LB VI Roger Staubach QB XII Randy White DT Harvey Martin DE XXVII Troy Aikman QB XXVIII Emmitt Smith RB XXX Larry Brown CB Ring of Honor\n\n[NEW 6] nts. There were six Super Bowls where the Cowboys received MVP awards, including Super Bowl V , a game they lost to the Baltimore Colts . ^ a b The Colts won Super Bowl V but the MVP was awarded to Chuck Howley of the Cowboys . References General Specific ^ a b ^ ^ ^ ^ a b 2009 ESPN Sports Almanac , p. 246 . ^ ^ a b ^ ^ a b c d ^ a b c ^ a b c ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 6 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[1,1,1,1,1,1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1486, + "osl": 942, + "total_tokens": 2428, + "latency_ms": 7023.76, + "tokens_per_second": 134.12 + }, + "context": { + "num_documents_evaluated": 6, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "d8dc293d-92f6-483c-ab7b-795e396e08b5", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:07:52.793806Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of 2010, if you added the number of times Brazil had won the World Cup to the amount of times the Chicago Bulls had won the NBA Championship and multiplied this number by the amount of times the Dallas Cowboys had won the Super Bowl, what number are you left with?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Year N. o Brazil Copa América 1919 1o Brazil Copa América 1922 2o Brazil Copa América 1949 3o Chile Panamerican Championship 1952 4o Mexico Panamerican Championship 1956 5o Sweden FIFA World Cup 1958 6o Chile FIFA World Cup 1962 7o Mexico FIFA World Cup 1970 8o Brazil Copa América 1989 9o United States FIFA World Cup 1994 10o Bolivia Copa América 1997 11o Saudi Arabia FIFA Confederations Cup 1997 12o Paraguay Copa América 1999 13o South Korea–Japan FIFA World Cup 2002 14o Peru Copa América 2004 15o Germany FIFA Confederations Cup 2005 16o Venezuela Copa América 2007 17o South Africa FIFA Confederations Cup 2009 18o Brazil FIFA Confederations Cup 2013 19o Brazil Copa América 2019 20o Summary Competition Total FIFA World Cup 5 2 2 9 FIFA Confederations Cup 4\n\n[DOC 2] Cup victory. Uruguay, however, chose to display four stars on their badge , representing their two gold medals at the 1924 and 1928 Summer Olympics, which are recognized by FIFA as World Championships, and their two World Cup titles in 1930 and 1950. With five titles, Brazil are the most successful World Cup team and also the only nation to have played in every World Cup (22) to date. Brazil were also the first team to win the World Cup for the third (1970), fourth (1994) and fifth (2002) time. Italy (1934 and 1938) and Brazil (1958 and 1962) are the only nations to have won consecutive titles. West Germany (1982–1990) and Brazil (1994–2002) are the only nations to appear in three consecutive World Cup finals. Germany has made the most top-four finishes (13)\n\n[DOC 3] o Bulls are an American professional basketball team based in Chicago . The Bulls compete in the National Basketball Association (NBA) as a member of the Central Division of the Eastern Conference . The team was founded on January 16, 1966, and played its first game during the 1966–67 NBA season . The Bulls share their home arena with the National Hockey League 's Chicago Blackhawks , playing in the newer United Center which replaced the former Chicago Stadium . The Bulls saw their greatest success during the 1990s when they played a major part in popularizing the NBA worldwide. They are known for having one of the NBA's greatest dynasties, winning six NBA championships between 1991 and 1998 with two separate three-peats . All six of their championship teams\n\n[DOC 4] Blackhawks have won six Stanley Cups , including in 2010, 2013, and 2015 . Both the Bulls and the Blackhawks play at the United Center . Major league professional teams in Chicago (ranked by attendance) Club League Sport Venue Attendance Founded Championships Chicago Bears NFL Football Soldier Field 61,142 1919 9 Championships (1 Super Bowl ) Chicago Cubs MLB Baseball Wrigley Field 41,649 1870 3 World Series Chicago White Sox MLB Baseball Rate Field 40,615 1900 3 World Series Chicago Blackhawks NHL Ice hockey United Center 21,653 1926 6 Stanley Cups Chicago Bulls NBA Basketball 20,776 1966 6 NBA Championships Chicago Fire MLS Soccer Soldier Field 17,383 1997 1 MLS Cup , 1 Supporters Shield Chicago Sky WNBA Basketball Wintrust Arena 10,387 2006 1 WNBA Champi\n\n[DOC 5] R 2006–2008 2018 17 Harold Carmichael WR 1984 2020 43 Cliff Harris S 1970–1979 2020 88 Drew Pearson WR 1973–1983 2021 54 Chuck Howley LB 1961–1973 2023 55 Zach Thomas LB 2008 2023 94 DeMarcus Ware LB 2005–2013 2023 Coaches and Contributors Name Position Seasons Inducted Tom Landry Coach 1960–1988 1990 Tex Schramm President/GM 1960–1988 1991 Bill Parcells Coach 2003–2006 2013 Jerry Jones Owner/Executive 1989–present 2017 Gil Brandt Executive 1960–1988 2019 Jimmy Johnson Coach 1989–1993 2020 Super Bowl MVPs The Cowboys have had seven players win Super Bowl MVP. Super Bowl MVP Winners Super Bowl Player Position V Chuck Howley LB VI Roger Staubach QB XII Randy White DT Harvey Martin DE XXVII Troy Aikman QB XXVIII Emmitt Smith RB XXX Larry Brown CB Ring of Honor\n\n[DOC 6] nts. There were six Super Bowls where the Cowboys received MVP awards, including Super Bowl V , a game they lost to the Baltimore Colts . ^ a b The Colts won Super Bowl V but the MVP was awarded to Chuck Howley of the Cowboys . References General Specific ^ a b ^ ^ ^ ^ a b 2009 ESPN Sports Almanac , p. 246 . ^ ^ a b ^ ^ a b c d ^ a b c ^ a b c ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide Brazil's 5 World Cup titles and the Chicago Bulls' 6 NBA championships, but they do not explicitly state the number of Super Bowl victories the Dallas Cowboys had as of 2010. While there are references to Super Bowl MVPs and mentions of specific Super Bowls (including a loss in V), the exact count of Cowboys' Super Bowl wins is not directly given, making the information insufficient to compute the final number.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1466, + "osl": 104, + "total_tokens": 1570, + "latency_ms": 8126.69, + "tokens_per_second": 12.8 + }, + "context": { + "kept_docs_count": 6, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "0bcef756-62aa-4c01-a2d0-585195b2a9e9", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:07:56.297256Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of 2010, if you added the number of times Brazil had won the World Cup to the amount of times the Chicago Bulls had won the NBA Championship and multiplied this number by the amount of times the Dallas Cowboys had won the Super Bowl, what number are you left with?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Year N. o Brazil Copa América 1919 1o Brazil Copa América 1922 2o Brazil Copa América 1949 3o Chile Panamerican Championship 1952 4o Mexico Panamerican Championship 1956 5o Sweden FIFA World Cup 1958 6o Chile FIFA World Cup 1962 7o Mexico FIFA World Cup 1970 8o Brazil Copa América 1989 9o United States FIFA World Cup 1994 10o Bolivia Copa América 1997 11o Saudi Arabia FIFA Confederations Cup 1997 12o Paraguay Copa América 1999 13o South Korea–Japan FIFA World Cup 2002 14o Peru Copa América 2004 15o Germany FIFA Confederations Cup 2005 16o Venezuela Copa América 2007 17o South Africa FIFA Confederations Cup 2009 18o Brazil FIFA Confederations Cup 2013 19o Brazil Copa América 2019 20o Summary Competition Total FIFA World Cup 5 2 2 9 FIFA Confederations Cup 4\n\n[DOC 2] Cup victory. Uruguay, however, chose to display four stars on their badge , representing their two gold medals at the 1924 and 1928 Summer Olympics, which are recognized by FIFA as World Championships, and their two World Cup titles in 1930 and 1950. With five titles, Brazil are the most successful World Cup team and also the only nation to have played in every World Cup (22) to date. Brazil were also the first team to win the World Cup for the third (1970), fourth (1994) and fifth (2002) time. Italy (1934 and 1938) and Brazil (1958 and 1962) are the only nations to have won consecutive titles. West Germany (1982–1990) and Brazil (1994–2002) are the only nations to appear in three consecutive World Cup finals. Germany has made the most top-four finishes (13)\n\n[DOC 3] o Bulls are an American professional basketball team based in Chicago . The Bulls compete in the National Basketball Association (NBA) as a member of the Central Division of the Eastern Conference . The team was founded on January 16, 1966, and played its first game during the 1966–67 NBA season . The Bulls share their home arena with the National Hockey League 's Chicago Blackhawks , playing in the newer United Center which replaced the former Chicago Stadium . The Bulls saw their greatest success during the 1990s when they played a major part in popularizing the NBA worldwide. They are known for having one of the NBA's greatest dynasties, winning six NBA championships between 1991 and 1998 with two separate three-peats . All six of their championship teams\n\n[DOC 4] Blackhawks have won six Stanley Cups , including in 2010, 2013, and 2015 . Both the Bulls and the Blackhawks play at the United Center . Major league professional teams in Chicago (ranked by attendance) Club League Sport Venue Attendance Founded Championships Chicago Bears NFL Football Soldier Field 61,142 1919 9 Championships (1 Super Bowl ) Chicago Cubs MLB Baseball Wrigley Field 41,649 1870 3 World Series Chicago White Sox MLB Baseball Rate Field 40,615 1900 3 World Series Chicago Blackhawks NHL Ice hockey United Center 21,653 1926 6 Stanley Cups Chicago Bulls NBA Basketball 20,776 1966 6 NBA Championships Chicago Fire MLS Soccer Soldier Field 17,383 1997 1 MLS Cup , 1 Supporters Shield Chicago Sky WNBA Basketball Wintrust Arena 10,387 2006 1 WNBA Champi\n\n[DOC 5] R 2006–2008 2018 17 Harold Carmichael WR 1984 2020 43 Cliff Harris S 1970–1979 2020 88 Drew Pearson WR 1973–1983 2021 54 Chuck Howley LB 1961–1973 2023 55 Zach Thomas LB 2008 2023 94 DeMarcus Ware LB 2005–2013 2023 Coaches and Contributors Name Position Seasons Inducted Tom Landry Coach 1960–1988 1990 Tex Schramm President/GM 1960–1988 1991 Bill Parcells Coach 2003–2006 2013 Jerry Jones Owner/Executive 1989–present 2017 Gil Brandt Executive 1960–1988 2019 Jimmy Johnson Coach 1989–1993 2020 Super Bowl MVPs The Cowboys have had seven players win Super Bowl MVP. Super Bowl MVP Winners Super Bowl Player Position V Chuck Howley LB VI Roger Staubach QB XII Randy White DT Harvey Martin DE XXVII Troy Aikman QB XXVIII Emmitt Smith RB XXX Larry Brown CB Ring of Honor\n\n[DOC 6] nts. There were six Super Bowls where the Cowboys received MVP awards, including Super Bowl V , a game they lost to the Baltimore Colts . ^ a b The Colts won Super Bowl V but the MVP was awarded to Chuck Howley of the Cowboys . References General Specific ^ a b ^ ^ ^ ^ a b 2009 ESPN Sports Almanac , p. 246 . ^ ^ a b ^ ^ a b c d ^ a b c ^ a b c ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^\n\n\nSEARCH HISTORY: - Query: 'Brazil World Cup titles' → 2 docs\n- Query: 'Chicago Bulls championships' → 2 docs\n- Query: 'Dallas Cowboys Super Bowl wins' → 2 docs\n\nFEEDBACK: Need Brazil's total World Cup victories (as of 2010), Chicago Bulls' total NBA championships (as of 2010), and Dallas Cowboys' total Super Bowl victories (as of 2010) to compute (Brazil wins + Bulls wins) × Cowboys wins.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Brazil national football team World Cup titles\",\n \"Chicago Bulls NBA championships count\",\n \"Dallas Cowboys Super Bowl wins total\"\n ],\n \"feedback\": \"Needed precise counts: Brazil's World Cup victories (as of 2010), Chicago Bulls' NBA championship total (as of 2010), and Dallas Cowboys' Super Bowl victory total (as of 2010). The previous queries were too generic; these refined queries target the exact tally information.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1768, + "osl": 379, + "total_tokens": 2147, + "latency_ms": 3502.86, + "tokens_per_second": 108.2 + }, + "context": { + "kept_docs_count": 6, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "93bc9d90-693f-4b67-a43a-eff6c9cb606e", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T03:08:03.810285Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of 2010, if you added the number of times Brazil had won the World Cup to the amount of times the Chicago Bulls had won the NBA Championship and multiplied this number by the amount of times the Dallas Cowboys had won the Super Bowl, what number are you left with?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Super Bowls ( IX and X ); they also won two more consecutive Super Bowls ( XIII and XIV ) for four titles in six seasons. The Dallas Cowboys won two consecutive Super Bowls ( XXVII and XXVIII ); the Cowboys won Super Bowl XXX the following year for three titles in four seasons. The New England Patriots won Super Bowls XLIX , LI , and LIII for three titles in five seasons. They also appeared in and lost Super Bowl LII to the Philadelphia Eagles following the 2017 season, giving them four Super Bowl appearances in five years. The Kansas City Chiefs came the closest to a three-peat, winning back-to-back Super Bowls in LVII and LVIII but losing their third consecutive appearance in LIX . They additionally won LIV several years beforehand, and lost LV , for a tot\n\n\nKEPT DOCUMENTS (context only):\n[6 documents already kept as relevant]\n[KEPT 1] Year N. o Brazil Copa América 1919 1o Brazil Copa América 1922 2o Brazil Copa América 1949 3o Chile Panamerican Championship 1952 4o Mexico Panamerican Championship 1956 5o Sweden FIFA World Cup 1958 6o Chile FIFA World Cup 1962 7o Mexico FIFA World Cup 1970 8o Brazil Copa América 1989 9o United Sta...\n[KEPT 2] Cup victory. Uruguay, however, chose to display four stars on their badge , representing their two gold medals at the 1924 and 1928 Summer Olympics, which are recognized by FIFA as World Championships, and their two World Cup titles in 1930 and 1950. With five titles, Brazil are the most successful ...\n[KEPT 3] o Bulls are an American professional basketball team based in Chicago . The Bulls compete in the National Basketball Association (NBA) as a member of the Central Division of the Eastern Conference . The team was founded on January 16, 1966, and played its first game during the 1966–67 NBA season . T...\n[KEPT 4] Blackhawks have won six Stanley Cups , including in 2010, 2013, and 2015 . Both the Bulls and the Blackhawks play at the United Center . Major league professional teams in Chicago (ranked by attendance) Club League Sport Venue Attendance Founded Championships Chicago Bears NFL Football Soldier Field...\n[KEPT 5] R 2006–2008 2018 17 Harold Carmichael WR 1984 2020 43 Cliff Harris S 1970–1979 2020 88 Drew Pearson WR 1973–1983 2021 54 Chuck Howley LB 1961–1973 2023 55 Zach Thomas LB 2008 2023 94 DeMarcus Ware LB 2005–2013 2023 Coaches and Contributors Name Position Seasons Inducted Tom Landry Coach 1960–1988 19...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 886, + "osl": 293, + "total_tokens": 1179, + "latency_ms": 6934.65, + "tokens_per_second": 42.25 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 6 + } + }, + { + "call_id": "d200e39f-117e-4dc8-8c33-29e7accd5144", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:08:12.847600Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of 2010, if you added the number of times Brazil had won the World Cup to the amount of times the Chicago Bulls had won the NBA Championship and multiplied this number by the amount of times the Dallas Cowboys had won the Super Bowl, what number are you left with?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Year N. o Brazil Copa América 1919 1o Brazil Copa América 1922 2o Brazil Copa América 1949 3o Chile Panamerican Championship 1952 4o Mexico Panamerican Championship 1956 5o Sweden FIFA World Cup 1958 6o Chile FIFA World Cup 1962 7o Mexico FIFA World Cup 1970 8o Brazil Copa América 1989 9o United States FIFA World Cup 1994 10o Bolivia Copa América 1997 11o Saudi Arabia FIFA Confederations Cup 1997 12o Paraguay Copa América 1999 13o South Korea–Japan FIFA World Cup 2002 14o Peru Copa América 2004 15o Germany FIFA Confederations Cup 2005 16o Venezuela Copa América 2007 17o South Africa FIFA Confederations Cup 2009 18o Brazil FIFA Confederations Cup 2013 19o Brazil Copa América 2019 20o Summary Competition Total FIFA World Cup 5 2 2 9 FIFA Confederations Cup 4\n\n[DOC 2] Cup victory. Uruguay, however, chose to display four stars on their badge , representing their two gold medals at the 1924 and 1928 Summer Olympics, which are recognized by FIFA as World Championships, and their two World Cup titles in 1930 and 1950. With five titles, Brazil are the most successful World Cup team and also the only nation to have played in every World Cup (22) to date. Brazil were also the first team to win the World Cup for the third (1970), fourth (1994) and fifth (2002) time. Italy (1934 and 1938) and Brazil (1958 and 1962) are the only nations to have won consecutive titles. West Germany (1982–1990) and Brazil (1994–2002) are the only nations to appear in three consecutive World Cup finals. Germany has made the most top-four finishes (13)\n\n[DOC 3] o Bulls are an American professional basketball team based in Chicago . The Bulls compete in the National Basketball Association (NBA) as a member of the Central Division of the Eastern Conference . The team was founded on January 16, 1966, and played its first game during the 1966–67 NBA season . The Bulls share their home arena with the National Hockey League 's Chicago Blackhawks , playing in the newer United Center which replaced the former Chicago Stadium . The Bulls saw their greatest success during the 1990s when they played a major part in popularizing the NBA worldwide. They are known for having one of the NBA's greatest dynasties, winning six NBA championships between 1991 and 1998 with two separate three-peats . All six of their championship teams\n\n[DOC 4] Blackhawks have won six Stanley Cups , including in 2010, 2013, and 2015 . Both the Bulls and the Blackhawks play at the United Center . Major league professional teams in Chicago (ranked by attendance) Club League Sport Venue Attendance Founded Championships Chicago Bears NFL Football Soldier Field 61,142 1919 9 Championships (1 Super Bowl ) Chicago Cubs MLB Baseball Wrigley Field 41,649 1870 3 World Series Chicago White Sox MLB Baseball Rate Field 40,615 1900 3 World Series Chicago Blackhawks NHL Ice hockey United Center 21,653 1926 6 Stanley Cups Chicago Bulls NBA Basketball 20,776 1966 6 NBA Championships Chicago Fire MLS Soccer Soldier Field 17,383 1997 1 MLS Cup , 1 Supporters Shield Chicago Sky WNBA Basketball Wintrust Arena 10,387 2006 1 WNBA Champi\n\n[DOC 5] R 2006–2008 2018 17 Harold Carmichael WR 1984 2020 43 Cliff Harris S 1970–1979 2020 88 Drew Pearson WR 1973–1983 2021 54 Chuck Howley LB 1961–1973 2023 55 Zach Thomas LB 2008 2023 94 DeMarcus Ware LB 2005–2013 2023 Coaches and Contributors Name Position Seasons Inducted Tom Landry Coach 1960–1988 1990 Tex Schramm President/GM 1960–1988 1991 Bill Parcells Coach 2003–2006 2013 Jerry Jones Owner/Executive 1989–present 2017 Gil Brandt Executive 1960–1988 2019 Jimmy Johnson Coach 1989–1993 2020 Super Bowl MVPs The Cowboys have had seven players win Super Bowl MVP. Super Bowl MVP Winners Super Bowl Player Position V Chuck Howley LB VI Roger Staubach QB XII Randy White DT Harvey Martin DE XXVII Troy Aikman QB XXVIII Emmitt Smith RB XXX Larry Brown CB Ring of Honor\n\n[DOC 6] nts. There were six Super Bowls where the Cowboys received MVP awards, including Super Bowl V , a game they lost to the Baltimore Colts . ^ a b The Colts won Super Bowl V but the MVP was awarded to Chuck Howley of the Cowboys . References General Specific ^ a b ^ ^ ^ ^ a b 2009 ESPN Sports Almanac , p. 246 . ^ ^ a b ^ ^ a b c d ^ a b c ^ a b c ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^\n\n[DOC 7] Super Bowls ( IX and X ); they also won two more consecutive Super Bowls ( XIII and XIV ) for four titles in six seasons. The Dallas Cowboys won two consecutive Super Bowls ( XXVII and XXVIII ); the Cowboys won Super Bowl XXX the following year for three titles in four seasons. The New England Patriots won Super Bowls XLIX , LI , and LIII for three titles in five seasons. They also appeared in and lost Super Bowl LII to the Philadelphia Eagles following the 2017 season, giving them four Super Bowl appearances in five years. The Kansas City Chiefs came the closest to a three-peat, winning back-to-back Super Bowls in LVII and LVIII but losing their third consecutive appearance in LIX . They additionally won LIV several years beforehand, and lost LV , for a tot\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Documents provide Brazil's World Cup wins (5) and the Chicago Bulls' NBA championships (6), but they do not give a clear total count of Dallas Cowboys Super Bowl victories as of 2010. Only a partial mention of three Cowboys titles appears, which is insufficient to compute the required multiplication.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1698, + "osl": 655, + "total_tokens": 2353, + "latency_ms": 9036.38, + "tokens_per_second": 72.48 + }, + "context": { + "kept_docs_count": 7, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "db79aaeb-6e49-4fd4-abe3-dfb3d5f95798", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:08:20.559459Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of 2010, if you added the number of times Brazil had won the World Cup to the amount of times the Chicago Bulls had won the NBA Championship and multiplied this number by the amount of times the Dallas Cowboys had won the Super Bowl, what number are you left with?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Year N. o Brazil Copa América 1919 1o Brazil Copa América 1922 2o Brazil Copa América 1949 3o Chile Panamerican Championship 1952 4o Mexico Panamerican Championship 1956 5o Sweden FIFA World Cup 1958 6o Chile FIFA World Cup 1962 7o Mexico FIFA World Cup 1970 8o Brazil Copa América 1989 9o United States FIFA World Cup 1994 10o Bolivia Copa América 1997 11o Saudi Arabia FIFA Confederations Cup 1997 12o Paraguay Copa América 1999 13o South Korea–Japan FIFA World Cup 2002 14o Peru Copa América 2004 15o Germany FIFA Confederations Cup 2005 16o Venezuela Copa América 2007 17o South Africa FIFA Confederations Cup 2009 18o Brazil FIFA Confederations Cup 2013 19o Brazil Copa América 2019 20o Summary Competition Total FIFA World Cup 5 2 2 9 FIFA Confederations Cup 4\n\n[DOC 2] Cup victory. Uruguay, however, chose to display four stars on their badge , representing their two gold medals at the 1924 and 1928 Summer Olympics, which are recognized by FIFA as World Championships, and their two World Cup titles in 1930 and 1950. With five titles, Brazil are the most successful World Cup team and also the only nation to have played in every World Cup (22) to date. Brazil were also the first team to win the World Cup for the third (1970), fourth (1994) and fifth (2002) time. Italy (1934 and 1938) and Brazil (1958 and 1962) are the only nations to have won consecutive titles. West Germany (1982–1990) and Brazil (1994–2002) are the only nations to appear in three consecutive World Cup finals. Germany has made the most top-four finishes (13)\n\n[DOC 3] o Bulls are an American professional basketball team based in Chicago . The Bulls compete in the National Basketball Association (NBA) as a member of the Central Division of the Eastern Conference . The team was founded on January 16, 1966, and played its first game during the 1966–67 NBA season . The Bulls share their home arena with the National Hockey League 's Chicago Blackhawks , playing in the newer United Center which replaced the former Chicago Stadium . The Bulls saw their greatest success during the 1990s when they played a major part in popularizing the NBA worldwide. They are known for having one of the NBA's greatest dynasties, winning six NBA championships between 1991 and 1998 with two separate three-peats . All six of their championship teams\n\n[DOC 4] Blackhawks have won six Stanley Cups , including in 2010, 2013, and 2015 . Both the Bulls and the Blackhawks play at the United Center . Major league professional teams in Chicago (ranked by attendance) Club League Sport Venue Attendance Founded Championships Chicago Bears NFL Football Soldier Field 61,142 1919 9 Championships (1 Super Bowl ) Chicago Cubs MLB Baseball Wrigley Field 41,649 1870 3 World Series Chicago White Sox MLB Baseball Rate Field 40,615 1900 3 World Series Chicago Blackhawks NHL Ice hockey United Center 21,653 1926 6 Stanley Cups Chicago Bulls NBA Basketball 20,776 1966 6 NBA Championships Chicago Fire MLS Soccer Soldier Field 17,383 1997 1 MLS Cup , 1 Supporters Shield Chicago Sky WNBA Basketball Wintrust Arena 10,387 2006 1 WNBA Champi\n\n[DOC 5] R 2006–2008 2018 17 Harold Carmichael WR 1984 2020 43 Cliff Harris S 1970–1979 2020 88 Drew Pearson WR 1973–1983 2021 54 Chuck Howley LB 1961–1973 2023 55 Zach Thomas LB 2008 2023 94 DeMarcus Ware LB 2005–2013 2023 Coaches and Contributors Name Position Seasons Inducted Tom Landry Coach 1960–1988 1990 Tex Schramm President/GM 1960–1988 1991 Bill Parcells Coach 2003–2006 2013 Jerry Jones Owner/Executive 1989–present 2017 Gil Brandt Executive 1960–1988 2019 Jimmy Johnson Coach 1989–1993 2020 Super Bowl MVPs The Cowboys have had seven players win Super Bowl MVP. Super Bowl MVP Winners Super Bowl Player Position V Chuck Howley LB VI Roger Staubach QB XII Randy White DT Harvey Martin DE XXVII Troy Aikman QB XXVIII Emmitt Smith RB XXX Larry Brown CB Ring of Honor\n\n[DOC 6] nts. There were six Super Bowls where the Cowboys received MVP awards, including Super Bowl V , a game they lost to the Baltimore Colts . ^ a b The Colts won Super Bowl V but the MVP was awarded to Chuck Howley of the Cowboys . References General Specific ^ a b ^ ^ ^ ^ a b 2009 ESPN Sports Almanac , p. 246 . ^ ^ a b ^ ^ a b c d ^ a b c ^ a b c ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^\n\n[DOC 7] Super Bowls ( IX and X ); they also won two more consecutive Super Bowls ( XIII and XIV ) for four titles in six seasons. The Dallas Cowboys won two consecutive Super Bowls ( XXVII and XXVIII ); the Cowboys won Super Bowl XXX the following year for three titles in four seasons. The New England Patriots won Super Bowls XLIX , LI , and LIII for three titles in five seasons. They also appeared in and lost Super Bowl LII to the Philadelphia Eagles following the 2017 season, giving them four Super Bowl appearances in five years. The Kansas City Chiefs came the closest to a three-peat, winning back-to-back Super Bowls in LVII and LVIII but losing their third consecutive appearance in LIX . They additionally won LIV several years beforehand, and lost LV , for a tot\n\n\nSEARCH HISTORY: - Query: 'Brazil World Cup titles' → 2 docs\n- Query: 'Chicago Bulls championships' → 2 docs\n- Query: 'Dallas Cowboys Super Bowl wins' → 2 docs\n- Query: 'Brazil national football team World Cup titles' → 0 docs\n- Query: 'Chicago Bulls NBA championships count' → 0 docs\n- Query: 'Dallas Cowboys Super Bowl wins total' → 1 docs\n\nFEEDBACK: Need Brazil's total World Cup victories (as of 2010), Chicago Bulls' total NBA championships (as of 2010), and Dallas Cowboys' total Super Bowl victories (as of 2010) to compute (Brazil wins + Bulls wins) × Cowboys wins.\nNeeded precise counts: Brazil's World Cup victories (as of 2010), Chicago Bulls' NBA championship total (as of 2010), and Dallas Cowboys' Super Bowl victory total (as of 2010). The previous queries were too generic; these refined queries target the exact tally information.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Brazil World Cup victories\",\n \"Chicago Bulls NBA championships total\",\n \"Dallas Cowboys Super Bowl wins total\"\n ],\n \"feedback\": \"Need exact counts: Brazil's World Cup titles (as of 2010), Chicago Bulls' NBA championship tally (as of 2010), and Dallas Cowboys' Super Bowl win total (as of 2010).\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1995, + "osl": 83, + "total_tokens": 2078, + "latency_ms": 7711.28, + "tokens_per_second": 10.76 + }, + "context": { + "kept_docs_count": 7, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "74cc14a9-a9f3-47d7-b051-5678d143fd92", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:08:23.039091Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of 2010, if you added the number of times Brazil had won the World Cup to the amount of times the Chicago Bulls had won the NBA Championship and multiplied this number by the amount of times the Dallas Cowboys had won the Super Bowl, what number are you left with?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Year N. o Brazil Copa América 1919 1o Brazil Copa América 1922 2o Brazil Copa América 1949 3o Chile Panamerican Championship 1952 4o Mexico Panamerican Championship 1956 5o Sweden FIFA World Cup 1958 6o Chile FIFA World Cup 1962 7o Mexico FIFA World Cup 1970 8o Brazil Copa América 1989 9o United States FIFA World Cup 1994 10o Bolivia Copa América 1997 11o Saudi Arabia FIFA Confederations Cup 1997 12o Paraguay Copa América 1999 13o South Korea–Japan FIFA World Cup 2002 14o Peru Copa América 2004 15o Germany FIFA Confederations Cup 2005 16o Venezuela Copa América 2007 17o South Africa FIFA Confederations Cup 2009 18o Brazil FIFA Confederations Cup 2013 19o Brazil Copa América 2019 20o Summary Competition Total FIFA World Cup 5 2 2 9 FIFA Confederations Cup 4\n\n[DOC 2] Cup victory. Uruguay, however, chose to display four stars on their badge , representing their two gold medals at the 1924 and 1928 Summer Olympics, which are recognized by FIFA as World Championships, and their two World Cup titles in 1930 and 1950. With five titles, Brazil are the most successful World Cup team and also the only nation to have played in every World Cup (22) to date. Brazil were also the first team to win the World Cup for the third (1970), fourth (1994) and fifth (2002) time. Italy (1934 and 1938) and Brazil (1958 and 1962) are the only nations to have won consecutive titles. West Germany (1982–1990) and Brazil (1994–2002) are the only nations to appear in three consecutive World Cup finals. Germany has made the most top-four finishes (13)\n\n[DOC 3] o Bulls are an American professional basketball team based in Chicago . The Bulls compete in the National Basketball Association (NBA) as a member of the Central Division of the Eastern Conference . The team was founded on January 16, 1966, and played its first game during the 1966–67 NBA season . The Bulls share their home arena with the National Hockey League 's Chicago Blackhawks , playing in the newer United Center which replaced the former Chicago Stadium . The Bulls saw their greatest success during the 1990s when they played a major part in popularizing the NBA worldwide. They are known for having one of the NBA's greatest dynasties, winning six NBA championships between 1991 and 1998 with two separate three-peats . All six of their championship teams\n\n[DOC 4] Blackhawks have won six Stanley Cups , including in 2010, 2013, and 2015 . Both the Bulls and the Blackhawks play at the United Center . Major league professional teams in Chicago (ranked by attendance) Club League Sport Venue Attendance Founded Championships Chicago Bears NFL Football Soldier Field 61,142 1919 9 Championships (1 Super Bowl ) Chicago Cubs MLB Baseball Wrigley Field 41,649 1870 3 World Series Chicago White Sox MLB Baseball Rate Field 40,615 1900 3 World Series Chicago Blackhawks NHL Ice hockey United Center 21,653 1926 6 Stanley Cups Chicago Bulls NBA Basketball 20,776 1966 6 NBA Championships Chicago Fire MLS Soccer Soldier Field 17,383 1997 1 MLS Cup , 1 Supporters Shield Chicago Sky WNBA Basketball Wintrust Arena 10,387 2006 1 WNBA Champi\n\n[DOC 5] R 2006–2008 2018 17 Harold Carmichael WR 1984 2020 43 Cliff Harris S 1970–1979 2020 88 Drew Pearson WR 1973–1983 2021 54 Chuck Howley LB 1961–1973 2023 55 Zach Thomas LB 2008 2023 94 DeMarcus Ware LB 2005–2013 2023 Coaches and Contributors Name Position Seasons Inducted Tom Landry Coach 1960–1988 1990 Tex Schramm President/GM 1960–1988 1991 Bill Parcells Coach 2003–2006 2013 Jerry Jones Owner/Executive 1989–present 2017 Gil Brandt Executive 1960–1988 2019 Jimmy Johnson Coach 1989–1993 2020 Super Bowl MVPs The Cowboys have had seven players win Super Bowl MVP. Super Bowl MVP Winners Super Bowl Player Position V Chuck Howley LB VI Roger Staubach QB XII Randy White DT Harvey Martin DE XXVII Troy Aikman QB XXVIII Emmitt Smith RB XXX Larry Brown CB Ring of Honor\n\n[DOC 6] nts. There were six Super Bowls where the Cowboys received MVP awards, including Super Bowl V , a game they lost to the Baltimore Colts . ^ a b The Colts won Super Bowl V but the MVP was awarded to Chuck Howley of the Cowboys . References General Specific ^ a b ^ ^ ^ ^ a b 2009 ESPN Sports Almanac , p. 246 . ^ ^ a b ^ ^ a b c d ^ a b c ^ a b c ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^\n\n[DOC 7] Super Bowls ( IX and X ); they also won two more consecutive Super Bowls ( XIII and XIV ) for four titles in six seasons. The Dallas Cowboys won two consecutive Super Bowls ( XXVII and XXVIII ); the Cowboys won Super Bowl XXX the following year for three titles in four seasons. The New England Patriots won Super Bowls XLIX , LI , and LIII for three titles in five seasons. They also appeared in and lost Super Bowl LII to the Philadelphia Eagles following the 2017 season, giving them four Super Bowl appearances in five years. The Kansas City Chiefs came the closest to a three-peat, winning back-to-back Super Bowls in LVII and LVIII but losing their third consecutive appearance in LIX . They additionally won LIV several years beforehand, and lost LV , for a tot\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": true,\n \"reasoning\": \"Document 2 states Brazil have won the FIFA World Cup five times (five titles). Document 3 states the Chicago Bulls have won six NBA championships. Document 7 three facts provide the exact counts needed to compute (5 + 6) * 3 = states the Dallas Cowboys have won three Super Bowls (XXVII, XXVIII, XXX). These33, allowing the question to be answered.\"\n}", + "finish_reason": null + }, + "metrics": { + "isl": 1696, + "osl": 711, + "total_tokens": 2407, + "latency_ms": 1928.13, + "tokens_per_second": 368.75 + }, + "context": { + "kept_docs_count": 7, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "a39612e3-4ab5-42e5-b0ae-ab5991700df1", + "component": "answer_generator", + "hop_count": 4, + "timestamp": "2026-05-16T03:08:29.752015Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: As of 2010, if you added the number of times Brazil had won the World Cup to the amount of times the Chicago Bulls had won the NBA Championship and multiplied this number by the amount of times the Dallas Cowboys had won the Super Bowl, what number are you left with?\n\nDOCUMENTS:\n\n[DOC 1] Year N. o Brazil Copa América 1919 1o Brazil Copa América 1922 2o Brazil Copa América 1949 3o Chile Panamerican Championship 1952 4o Mexico Panamerican Championship 1956 5o Sweden FIFA World Cup 1958 6o Chile FIFA World Cup 1962 7o Mexico FIFA World Cup 1970 8o Brazil Copa América 1989 9o United States FIFA World Cup 1994 10o Bolivia Copa América 1997 11o Saudi Arabia FIFA Confederations Cup 1997 12o Paraguay Copa América 1999 13o South Korea–Japan FIFA World Cup 2002 14o Peru Copa América 2004 15o Germany FIFA Confederations Cup 2005 16o Venezuela Copa América 2007 17o South Africa FIFA Confederations Cup 2009 18o Brazil FIFA Confederations Cup 2013 19o Brazil Copa América 2019 20o Summary Competition Total FIFA World Cup 5 2 2 9 FIFA Confederations Cup 4\n\n[DOC 2] Cup victory. Uruguay, however, chose to display four stars on their badge , representing their two gold medals at the 1924 and 1928 Summer Olympics, which are recognized by FIFA as World Championships, and their two World Cup titles in 1930 and 1950. With five titles, Brazil are the most successful World Cup team and also the only nation to have played in every World Cup (22) to date. Brazil were also the first team to win the World Cup for the third (1970), fourth (1994) and fifth (2002) time. Italy (1934 and 1938) and Brazil (1958 and 1962) are the only nations to have won consecutive titles. West Germany (1982–1990) and Brazil (1994–2002) are the only nations to appear in three consecutive World Cup finals. Germany has made the most top-four finishes (13)\n\n[DOC 3] o Bulls are an American professional basketball team based in Chicago . The Bulls compete in the National Basketball Association (NBA) as a member of the Central Division of the Eastern Conference . The team was founded on January 16, 1966, and played its first game during the 1966–67 NBA season . The Bulls share their home arena with the National Hockey League 's Chicago Blackhawks , playing in the newer United Center which replaced the former Chicago Stadium . The Bulls saw their greatest success during the 1990s when they played a major part in popularizing the NBA worldwide. They are known for having one of the NBA's greatest dynasties, winning six NBA championships between 1991 and 1998 with two separate three-peats . All six of their championship teams\n\n[DOC 4] Blackhawks have won six Stanley Cups , including in 2010, 2013, and 2015 . Both the Bulls and the Blackhawks play at the United Center . Major league professional teams in Chicago (ranked by attendance) Club League Sport Venue Attendance Founded Championships Chicago Bears NFL Football Soldier Field 61,142 1919 9 Championships (1 Super Bowl ) Chicago Cubs MLB Baseball Wrigley Field 41,649 1870 3 World Series Chicago White Sox MLB Baseball Rate Field 40,615 1900 3 World Series Chicago Blackhawks NHL Ice hockey United Center 21,653 1926 6 Stanley Cups Chicago Bulls NBA Basketball 20,776 1966 6 NBA Championships Chicago Fire MLS Soccer Soldier Field 17,383 1997 1 MLS Cup , 1 Supporters Shield Chicago Sky WNBA Basketball Wintrust Arena 10,387 2006 1 WNBA Champi\n\n[DOC 5] R 2006–2008 2018 17 Harold Carmichael WR 1984 2020 43 Cliff Harris S 1970–1979 2020 88 Drew Pearson WR 1973–1983 2021 54 Chuck Howley LB 1961–1973 2023 55 Zach Thomas LB 2008 2023 94 DeMarcus Ware LB 2005–2013 2023 Coaches and Contributors Name Position Seasons Inducted Tom Landry Coach 1960–1988 1990 Tex Schramm President/GM 1960–1988 1991 Bill Parcells Coach 2003–2006 2013 Jerry Jones Owner/Executive 1989–present 2017 Gil Brandt Executive 1960–1988 2019 Jimmy Johnson Coach 1989–1993 2020 Super Bowl MVPs The Cowboys have had seven players win Super Bowl MVP. Super Bowl MVP Winners Super Bowl Player Position V Chuck Howley LB VI Roger Staubach QB XII Randy White DT Harvey Martin DE XXVII Troy Aikman QB XXVIII Emmitt Smith RB XXX Larry Brown CB Ring of Honor\n\n[DOC 6] nts. There were six Super Bowls where the Cowboys received MVP awards, including Super Bowl V , a game they lost to the Baltimore Colts . ^ a b The Colts won Super Bowl V but the MVP was awarded to Chuck Howley of the Cowboys . References General Specific ^ a b ^ ^ ^ ^ a b 2009 ESPN Sports Almanac , p. 246 . ^ ^ a b ^ ^ a b c d ^ a b c ^ a b c ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^\n\n[DOC 7] Super Bowls ( IX and X ); they also won two more consecutive Super Bowls ( XIII and XIV ) for four titles in six seasons. The Dallas Cowboys won two consecutive Super Bowls ( XXVII and XXVIII ); the Cowboys won Super Bowl XXX the following year for three titles in four seasons. The New England Patriots won Super Bowls XLIX , LI , and LIII for three titles in five seasons. They also appeared in and lost Super Bowl LII to the Philadelphia Eagles following the 2017 season, giving them four Super Bowl appearances in five years. The Kansas City Chiefs came the closest to a three-peat, winning back-to-back Super Bowls in LVII and LVIII but losing their third consecutive appearance in LIX . They additionally won LIV several years beforehand, and lost LV , for a tot\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "33", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1496, + "osl": 1, + "total_tokens": 1497, + "latency_ms": 6712.26, + "tokens_per_second": 0.15 + }, + "context": { + "num_documents_used": 7, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:08:29.752770Z", + "summary": { + "total_llm_calls": 9, + "total_hops": 4, + "total_input_tokens": 12923, + "total_output_tokens": 3258, + "total_tokens": 16181, + "total_latency_ms": 55521.09, + "average_tokens_per_second": 85.48, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/FIFA_World_Cup", + "https://en.wikipedia.org/wiki/Chicago_Bulls", + "https://en.wikipedia.org/wiki/Dallas_Cowboys" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "33", + "ground_truth_answer": "55" + } + }, + { + "query_id": "33", + "original_query": "How old would the founder of the publishing company of the magazine that serialized the manga series Raw Hero have been the year the magazine ended publication?", + "timestamp_start": "2026-05-16T03:08:29.779878Z", + "llm_calls": [ + { + "call_id": "00bd6b78-5045-4619-808c-c8a1b48e0057", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:08:34.621680Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: How old would the founder of the publishing company of the magazine that serialized the manga series Raw Hero have been the year the magazine ended publication?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Raw Hero manga\",\n \"Raw Hero serialized magazine\",\n \"Raw Hero magazine publisher\"\n ],\n \"feedback\": \"We need to identify the magazine that serialized Raw Hero, then find the publishing company that owns that magazine, and finally locate the founder of that publishing company and the year the magazine ceased publication. The three short queries above start the chain: first locate the series, then the magazine, then the publisher.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 403, + "osl": 94, + "total_tokens": 497, + "latency_ms": 4841.68, + "tokens_per_second": 19.41 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "d4bf0759-5c2d-4cf5-94b3-240d437bf666", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:08:58.944643Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: How old would the founder of the publishing company of the magazine that serialized the manga series Raw Hero have been the year the magazine ended publication?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] tion of Hiromu Arakawa's Fullmetal Alchemist manga. 2009 Tokyo Magnitude 8.0 Masaki Tachibana 11 N/A Original work (co-production with Kinema Citrus ). Darker than Black: Gemini of the Meteor Tensai Okamura 12 C Sequel to Darker Than Black . 2010 Heroman Hitoshi Nanba 26 A Original work in collaboration with Stan Lee . 2010–2011 Star Driver Takuya Igarashi 25 C Original work. 2011 Gosick Hitoshi Nanba 24 A Based on a light novel by Kazuki Sakuraba . No. 6 Kenji Nagasaki 11 D Based on the novels by Atsuko Asano . Un-Go Seiji Mizushima 12 C Based on the works of Ango Sakaguchi . 2012 Eureka Seven: AO Tomoki Kyoda 24 A/B Sequel to Eureka Seven . 2012–2013 Blast of Tempest Masahiro Andō 24 D Based on a manga by Kyō Shirodaira. 2013 Otona Joshi no Anime Time: Lif\n\n[NEW 2] Japanese manga series by Hiromu Arakawa Fullmetal Alchemist First tankōbon volume cover, featuring siblings Edward (right) and Alphonse Elric (left) 鋼の錬金術師 ( Hagane no Renkinjutsushi ) Genre Adventure Dark fantasy Steampunk Manga Written by Hiromu Arakawa Published by Enix (2001–03) Square Enix (2003–10) English publisher AUS : Madman Entertainment NA : Viz Media Yen Press (digital) SG : Chuang Yi Imprint Gangan Comics Magazine Monthly Shōnen Gangan Original run July 12, 2001 – June 11, 2010 Volumes 27 ( List of volumes ) Light novel Written by Makoto Inoue Illustrated by Hiromu Arakawa Published by Square Enix English publisher NA : Viz Media Original run February 28, 2003 – April 22, 2010 Volumes 10 ( List of volumes ) Anime television series Fullmetal Alc\n\n[NEW 3] pletion. Series There is currently one manga title being serialized in Jump GIGA . Series title Author(s) Premiered Ref. Black Clover ( ブラッククローバー , Burakku Kurōbā ) Yūki Tabata December 2023 V Jump V Jump ( Vジャンプ , Bui Janpu ) was originally an offshoot of the Weekly Shōnen Jump magazine in a special issue called Weekly Shōnen Jump Tokubetsu Henshū Zōkan V Jump ( 週刊少年ジャンプ特別編集増刊 V JUMP ) . The special issues lasted from 1992 through 1993. V Jump became its own independent anthology in 1993 for coverage of games, including video and card games, in addition to its own run of manga series. Super Jump Super Jump ( スーパージャンプ , Sūpā Janpu ) was also originally an offshoot of the Weekly Shōnen Jump magazine in a special issue called Weekly Shōnen Jump Tokubetsu Hensh\n\n[NEW 4] strated by Hounori, released on Kodansha's Manga Box smartphone and tablet application from December 2013 to December 30, 2014, in both Japanese and English. The first tankōbon volume was published on August 8, 2014, and the second on April 9, 2015. No. Original release date Original ISBN English release date English ISBN 1 August 8, 2014 978-4-06-395152-3 September 13, 2016 978-1-63236-408-1 2 April 9, 2015 978-4-06-395360-2 December 13, 2016 978-1-63236-409-8 Lost Girls A manga adaptation by Ryōsuke Fuji of the light novel of the same name, that began serialization in Kodansha's magazine Bessatsu Shōnen Magazine on August 9, 2015. The series ended in the June 2016 issue of the magazine on May 9, 2016. In North America, the series has been licensed in Engli\n\n[NEW 5] n Weekly Shōnen Jump ) Level E (1995–1997, serialized in Weekly Shōnen Jump ) Hunter × Hunter (1998–present), serialized in Weekly Shōnen Jump ) Akuten Wars (2017, published in Grand Jump Premium , story only, illustrated by Hachi Mizuno) Other Yoshirin de Pon! (1994, YuYu Hakusho dōjinshi distributed at 1994 summer Comic Market ) Biohazard 3: The Last Escape Official Guidebook (1999, published by ASCII ) Official Hunter × Hunter Guide (2004, published by Shueisha) YuYu Hakusho Who's Who Underworld Character Book (2005, published by Shueisha) YuYu Hakusho Illustrations (2005, published by Shueisha) Oobo— Nu— To Chiibo— Nu— (2005, published by Kodansha ) Hetappi Manga Kenkyūjo R (2011, published by Shueisha) References ^ a b c d e ^ a b c d e ^ a b ^ a b c d\n\n[NEW 6] aphics Books with its publications Amazing Heroes and The Comics Journal racking up seven wins in total, and the British publication Speakeasy with four wins. 1977 Pro: House of Hammer Fan: Comic Media News 1978 Pro: Starburst Fan: Comic Media News 1980 U. K. (fan): BEM U. S. (fan): The Comics Journal 1981 U. K.: BEM U. S.: The Comics Journal 1985 U. K.: Fantasy Advertiser U. S.: Amazing Heroes 1986 U. K.: Speakeasy U. S.: Amazing Heroes 1987 U. K.: Speakeasy U. S.: Amazing Heroes 1988 U. K.: Speakeasy U. S.: Amazing Heroes 1990 U. K.: Speakeasy U. S.: Marvel Age 2000 Wizard 2002 Wizard 2004 The Comics Journal ( Fantagraphics Books ) 2006 The Comics Journal (Fantagraphics Books) 2007 Wizard 2008 Wizard 2010 Wizard 2011 Wizard 2012 DC Comics Super Hero Collec\n\n[NEW 7] wne (Summer 1952 – August 1956). Paul Fairman (October 1956 – November 1958). Cele Goldsmith (December 1958 – June 1965). Goldsmith used her married name, Cele G. Lalli, from July 1964. Joseph Ross (September 1965 – November 1967). Harry Harrison (January 1968 – October 1968). Barry N. Malzberg (December 1968 – April 1969). Ted White (June 1969 – January 1979) Elinor Mavor (April 1979 – October 1980) Other bibliographic details The title changed multiple times, and was frequently inconsistently given between the cover, spine, indicia, and masthead. Start month End month Cover Spine Indicia Masthead Number of issues Summer–52 Feb–55 Fantastic Fantastic Fantastic Fantastic 16 Apr–55 Oct–55 Fantastic Science-Fiction 4 Dec–55 Dec–55 Fantastic 1 Feb–56 Aug–56 Fan\n\n[NEW 8] pendent Journal Review InfoWars Jihad Watch LifeZette RedState The Dispatch Washington Examiner The Washington Free Beacon The Western Journal WorldNetDaily Other Blaze Media Encounter Books Evie Magazine The First Imprimis The Political Cesspool PragerU RealClearPolitics Regnery Publishing RSBN The Rubin Report Sinclair Broadcast Group White House Wire Other organizations Congressional caucuses Second Amendment Caucus RSC Freedom Caucus Republican Main Street Partnership Economics ATR Club for Growth FreedomWorks (defunct) NFIB NTU Tea Party Patriots USCC Gun rights GOA NAGR NRA Identity politics ACT! CWA GAG IWF LCR Moms for Liberty Nativist CIS FAIR Immigration Restriction League NumbersUSA Oath Keepers Patriot Prayer Proud Boys Three Percenters Religion\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 8 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[0,0,0,0,0,0,0,0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2019, + "osl": 1113, + "total_tokens": 3132, + "latency_ms": 23710.18, + "tokens_per_second": 46.94 + }, + "context": { + "num_documents_evaluated": 8, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "c8d8dea1-71ba-4e29-a3d4-ffdd5bd8d904", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:09:08.216588Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: How old would the founder of the publishing company of the magazine that serialized the manga series Raw Hero have been the year the magazine ended publication?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: - Query: 'Raw Hero manga' → 5 docs\n- Query: 'Raw Hero serialized magazine' → 0 docs\n- Query: 'Raw Hero magazine publisher' → 3 docs\n\nFEEDBACK: We need to identify the magazine that serialized Raw Hero, then find the publishing company that owns that magazine, and finally locate the founder of that publishing company and the year the magazine ceased publication. The three short queries above start the chain: first locate the series, then the magazine, then the publisher.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Raw Hero manga serialization\",\n \"Raw Hero magazine name\",\n \"Publisher name founder birth year\"\n ],\n \"feedback\": \"First query aims to identify the specific magazine that serialized Raw Hero. Second query retrieves the exact name of that magazine (if not already clear). Third query, using the publisher identified from the previous step, obtains the founder's birth year (or age) and the year the magazine ceased publication, allowing calculation of the founder's age when the magazine ended.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 492, + "osl": 106, + "total_tokens": 598, + "latency_ms": 9270.98, + "tokens_per_second": 11.43 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "fd03d825-09e0-4167-98eb-9af335e8eca4", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T03:09:15.985400Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: How old would the founder of the publishing company of the magazine that serialized the manga series Raw Hero have been the year the magazine ended publication?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] se #1 and Immortal Hulk: The Best Defense #1, Doctor Strange: The Best Defense #1, Silver Surfer: The Best Defense #1, Defenders: The Best Defense #1. March 2019 978-1302916145 King In Black: Namor King In Black: Namor #1-5 July 2021 978-1804910511 See also List of Marvel Comics superhero debuts Aquaman - a similar character from DC Comics References ^ ^ ^ a b c d ^ a b ^ a b DeFalco et al. 2008 , p. 11, chpt. \"1939\": \"Writer/artist Bill Everett originally conceived Namor the Sub-Mariner in 1939 for an eight-page title called Motion Picture Funnies Weekly. Produced by Funnies Inc., this black-and-white magazine was intended to be handed out in movie theaters, but this idea fell through. So when Funnies Inc. packaged Marvel Comics #1 for Martin Goodman, Evere\n\n[NEW 2] ol. 1, no. 1 (December 2009). ^ Pak, Greg ( w ), Pagulayan, Carlo ( p ), Huet, Jeffrey ( i ). \"Warbound -- Part IV\" The Incredible Hulk , vol. 2, no. 109 (October 2007). ^ Achenbach, Joel (June 19, 2003). \"All the Rage: The Hulk in Us All\" . The Washington Post . ^ a b {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ Lobdell, Scott ; Waid, Mark ( w ), Kubert, Adam ; Bennett, Joe ( p ), Green, Dan ; Thibert, Art ; Townsend, Tim; Delpergang, Jesse ( i ). \"With Great Power ...\" Onslaught: Marvel , no. 1 (October 1996). ^ ^ a b c Pak, Greg ( w ), Pelletier, Paul ( p ), Miki, Danny ( i ). \"Heart of the Monster Part Six\" Incredible Hulks , no. 635 (October 2011). ^ a b Pak, Greg ( w ), Pelletier, Paul ( p ), Miki, Danny ( i ). \"Heart of the Monst\n\n[NEW 3] American publisher Alfred A. Knopf Sr. Knopf in 1935; photograph by Carl van Vechten Born Alfred Abraham Knopf ( 1892-09-12 ) September 12, 1892 New York, New York , U. S. Died August 11, 1984 (1984-08-11) (aged 91) Purchase, New York , U. S. Education Columbia University Spouse Blanche Wolf Knopf 1916–1966 (her death) Children Alfred A. Knopf Jr. Relatives Edwin H. Knopf (half-brother) Alfred Abraham Knopf Sr. (September 12, 1892 – August 11, 1984) was an American publisher of the 20th century, and co-founder of Alfred A. Knopf, Inc. His contemporaries included the likes of Bennett Cerf and Donald Klopfer , and (of the previous generation) Frank Nelson Doubleday , J. Henry Harper and Henry Holt . Knopf paid special attention to the quality of printing, bind\n\n[NEW 4] American publisher, photographer and programmer Andrew Fluegelman Born Andrew Cardozo Fluegelman ( 1943-11-27 ) November 27, 1943 Disappeared c. July 6, 1985 (1985-07-06) (aged 41) Occupation Publisher Andrew Cardozo Fluegelman (November 27, 1943 – c. July 6, 1985) was an American publisher, photographer, programmer and attorney best known as a pioneer of what is now known as the shareware business model for software marketing. He was also the founding editor of both PC World and Macworld and the leader of the 1970s New Games movement, which advocated the development of noncompetitive games . Early life Fluegelman was raised in White Plains, New York . He graduated from Yale University in 1969. Career Attorney Following graduation, Fluegelman worked in Midto\n\n[NEW 5] American publishing house Alfred A. Knopf, Inc. Parent company Penguin Random House Founded 1915 ; 111 years ago ( 1915 ) Founder Blanche Wolf Knopf and Alfred A. Knopf Sr. Country of origin United States Headquarters location New York City , U. S. Official website knopfdoubleday . com /imprint /knopf / Alfred A. Knopf, Inc. ( / k n ɒ p f / ) is an American publishing house that was founded by Blanche Knopf and Alfred A. Knopf Sr. in 1915. Blanche and Alfred traveled abroad regularly and were known for publishing European, Asian, and Latin American writers in addition to leading American literary trends. It was acquired by Random House in 1960, and is now part of the Knopf Doubleday Publishing Group division of Penguin Random House which is owned by the Germ\n\n[NEW 6] maint: multiple names: authors list ( link )\n\n[NEW 7] : authors list ( link )\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 7 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1375, + "osl": 1255, + "total_tokens": 2630, + "latency_ms": 7181.3, + "tokens_per_second": 174.76 + }, + "context": { + "num_documents_evaluated": 7, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "99745cf7-b58f-4a4c-b8ba-e2fa8821386f", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:09:36.521984Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: How old would the founder of the publishing company of the magazine that serialized the manga series Raw Hero have been the year the magazine ended publication?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: - Query: 'Raw Hero manga' → 5 docs\n- Query: 'Raw Hero serialized magazine' → 0 docs\n- Query: 'Raw Hero magazine publisher' → 3 docs\n- Query: 'Raw Hero manga serialization' → 0 docs\n- Query: 'Raw Hero magazine name' → 2 docs\n- Query: 'Publisher name founder birth year' → 5 docs\n\nFEEDBACK: We need to identify the magazine that serialized Raw Hero, then find the publishing company that owns that magazine, and finally locate the founder of that publishing company and the year the magazine ceased publication. The three short queries above start the chain: first locate the series, then the magazine, then the publisher.\nFirst query aims to identify the specific magazine that serialized Raw Hero. Second query retrieves the exact name of that magazine (if not already clear). Third query, using the publisher identified from the previous step, obtains the founder's birth year (or age) and the year the magazine ceased publication, allowing calculation of the founder's age when the magazine ended.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Raw Hero manga Wikipedia\",\n \"Raw Hero magazine\",\n \"Magazine publisher founder birth year\"\n ],\n \"feedback\": \"First query should retrieve the Raw Hero Wikipedia page which lists the serialization magazine. Second query aims to get the specific magazine article (its name was not yet identified). Third query uses the magazine's publishing company to find the founder's birth year, enabling calculation of the founder's age at the magazine's termination.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 676, + "osl": 1284, + "total_tokens": 1960, + "latency_ms": 20535.63, + "tokens_per_second": 62.53 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "6fe68df3-18f8-41d5-8cb9-8f2cdbb9af73", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:09:54.566943Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: How old would the founder of the publishing company of the magazine that serialized the manga series Raw Hero have been the year the magazine ended publication?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] rior, Takes Action!!\" / \"Silent Warrior\" Transliteration: \" Nokosareta Yuiitsu no Nozomi... Mugon no Senshi Jūrokugō Tatsu!! \" ( Japanese : 残された唯一の望み...無言の戦士16号立つ!! ) Mitsuo Hashimoto Storyboarded by : Kazuhito Kikuchi Aya Matsui Masayuki Uchiyama August 5, 1992 ( 1992-08-05 ) October 19, 2000 (FUNimation) May 15, 2001 (Ocean) 152 137 \"No. 17 Swallowed... The Transforming Cell is a Super Gourmet\" / \"Say Goodbye, 17\" Transliteration: \" Jūnanagō o Nomikonda... Henshin Seru wa Chōgurume \" ( Japanese : 17号を飲み込んだ...変身セルは超グルメ ) Yoshihiro Ueda Aya Matsui Tadayoshi Yamamuro August 12, 1992 ( 1992-08-12 ) October 20, 2000 (FUNimation) May 16, 2001 (Ocean) 153 138 \"Tomorrow, I Am Going to Pulverize You!! Goku's Challenge\" / \"Sacrifice\" Transliteration: \" Ashita wa Ome\n\n[DOC 2] article.\n\n[DOC 3] rtainment Tag team Weight classes Wrestling ring Culture Backyard wrestling Cauliflower Alley Club Cauliflower ear Comic books Fantasy wrestling Films Luchador films Magazines Paintings Rob Schamberger Gustave Courbet Thomas Eakins William Etty George Luks Rib Ribera Steakhouse Ten-bell salute Video games Wrestling personalities in politics Zubaz Media outlets Botchamania Box y Lucha Dark Side of the Ring Fighting Spirit Magazine Live Audio Wrestling OSW Review Power Slam Pro Wrestling Illustrated Solowrestling Súper Luchas WrestleCrap Wrestling Observer Newsletter Controversies Animals in professional wrestling Chris Benoit double-murder and suicide David Arquette in WCW Death of Owen Hart Fingerpoke of Doom Killing of Bruiser Brody Killing of Rikidōzan Mas\n\n[DOC 4] )\n\n[DOC 5] )\n\n[DOC 6] a b ^ ^ ^ ^ ^ ^ ^ a b c edition of February 14, 2004 of Raw ^ ^ ^ a b c ^ ^ a b c d e f g h i j k l m ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ External links The Official Website of WrestleMania XX\n\n[DOC 7] American magazine publisher (1926–2017) Hugh Hefner Hefner in 1970 Born Hugh Marston Hefner ( 1926-04-09 ) April 9, 1926 Chicago, Illinois, U. S. Died September 27, 2017 (2017-09-27) (aged 91) Los Angeles, California, U. S. Resting place Westwood Village Memorial Park Cemetery Other names Hef Alma mater University of Illinois at Urbana Champaign ( BA ) Occupations Businessman magazine publisher Years active 1953–2017 Title Editor-in-chief of Playboy Chief creative officer of Playboy Enterprises Spouses Mildred Williams ( m. 1949; div. 1959) Kimberley Conrad ( m. 1989; div. 2010) Crystal Harris ( m. 2012) Partners See list Barbi Benton (1969–1976) Sondra Theodore (1976–1981) Carrie Leigh (1983–1988) Brande Roderick (1999–2000) Holly Madison (2001–2008) Bridge\n\n[DOC 8] American magazine and publisher The Atlantic Cover of the September 2020 issue Editor-in-chief Jeffrey Goldberg Former editors James Bennet Categories Literature political science foreign affairs lifestyle Frequency Monthly (1857–2000, 2025–) Eleven issues a year (2001–2002) Ten issues a year (2003–2024) Publisher Laurene Powell Jobs Total circulation 1,107,293 (2024) Founder Moses Dresser Phillips Francis H. Underwood Ralph Waldo Emerson Henry Wadsworth Longfellow Founded 1857 ; 169 years ago ( 1857 ) First issue November 1, 1857 ; 168 years ago ( 1857-11-01 ) (as The Atlantic Monthly ) Company Emerson Collective Country United States Based in Boston until 2005; Washington, D. C. , U. S. since 2005 Language English Website theatlantic. com ISSN 1072-7825 (p\n\n[DOC 9] . Stimson Alfred Thomas Story Susan Straight John Jeremiah Sullivan Booth Tarkington Sara Teasdale Hunter S. Thompson Leon Trotsky Mark Twain John Updike Kurt Vonnegut William T. Vollmann Adelaide Cilley Waldron David Foster Wallace H. G. Wells E. B. White Woodrow Wilson Owen Wister Tom Wolfe Howard Zinn Slavoj Žižek Gallery Posters by Edward Penfield January 1894 January 1895 June 1896 December 1896 May 1897 Notes ^ While Scientific American , founded in 1845, is older, it did not become monthly until 1921. References ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ Facts on File 1980 Yearbook , pp.501, 582 ^ ^ ^ An American Album: One Hundred and Fifty Years of Harper's Magazine , a 712-page illustrated anthology, with an introduction by Lewis H. Lapham and a foreword by Arthur Schles\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The retained documents do not mention the manga series \\\"Raw Hero\\\", the magazine that serialized it, the publishing company of that magazine, nor the founder of that publishing company. Consequently, we lack the necessary facts (magazine end year, founder's birth date) to compute the founder's age at the magazine's cessation.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1490, + "osl": 412, + "total_tokens": 1902, + "latency_ms": 7982.63, + "tokens_per_second": 51.61 + }, + "context": { + "kept_docs_count": 9, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "36f62a99-8c0f-4834-8a8c-721ca55dc79d", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:10:08.318892Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: How old would the founder of the publishing company of the magazine that serialized the manga series Raw Hero have been the year the magazine ended publication?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] rior, Takes Action!!\" / \"Silent Warrior\" Transliteration: \" Nokosareta Yuiitsu no Nozomi... Mugon no Senshi Jūrokugō Tatsu!! \" ( Japanese : 残された唯一の望み...無言の戦士16号立つ!! ) Mitsuo Hashimoto Storyboarded by : Kazuhito Kikuchi Aya Matsui Masayuki Uchiyama August 5, 1992 ( 1992-08-05 ) October 19, 2000 (FUNimation) May 15, 2001 (Ocean) 152 137 \"No. 17 Swallowed... The Transforming Cell is a Super Gourmet\" / \"Say Goodbye, 17\" Transliteration: \" Jūnanagō o Nomikonda... Henshin Seru wa Chōgurume \" ( Japanese : 17号を飲み込んだ...変身セルは超グルメ ) Yoshihiro Ueda Aya Matsui Tadayoshi Yamamuro August 12, 1992 ( 1992-08-12 ) October 20, 2000 (FUNimation) May 16, 2001 (Ocean) 153 138 \"Tomorrow, I Am Going to Pulverize You!! Goku's Challenge\" / \"Sacrifice\" Transliteration: \" Ashita wa Ome\n\n[DOC 2] article.\n\n[DOC 3] rtainment Tag team Weight classes Wrestling ring Culture Backyard wrestling Cauliflower Alley Club Cauliflower ear Comic books Fantasy wrestling Films Luchador films Magazines Paintings Rob Schamberger Gustave Courbet Thomas Eakins William Etty George Luks Rib Ribera Steakhouse Ten-bell salute Video games Wrestling personalities in politics Zubaz Media outlets Botchamania Box y Lucha Dark Side of the Ring Fighting Spirit Magazine Live Audio Wrestling OSW Review Power Slam Pro Wrestling Illustrated Solowrestling Súper Luchas WrestleCrap Wrestling Observer Newsletter Controversies Animals in professional wrestling Chris Benoit double-murder and suicide David Arquette in WCW Death of Owen Hart Fingerpoke of Doom Killing of Bruiser Brody Killing of Rikidōzan Mas\n\n[DOC 4] )\n\n[DOC 5] )\n\n[DOC 6] a b ^ ^ ^ ^ ^ ^ ^ a b c edition of February 14, 2004 of Raw ^ ^ ^ a b c ^ ^ a b c d e f g h i j k l m ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ External links The Official Website of WrestleMania XX\n\n[DOC 7] American magazine publisher (1926–2017) Hugh Hefner Hefner in 1970 Born Hugh Marston Hefner ( 1926-04-09 ) April 9, 1926 Chicago, Illinois, U. S. Died September 27, 2017 (2017-09-27) (aged 91) Los Angeles, California, U. S. Resting place Westwood Village Memorial Park Cemetery Other names Hef Alma mater University of Illinois at Urbana Champaign ( BA ) Occupations Businessman magazine publisher Years active 1953–2017 Title Editor-in-chief of Playboy Chief creative officer of Playboy Enterprises Spouses Mildred Williams ( m. 1949; div. 1959) Kimberley Conrad ( m. 1989; div. 2010) Crystal Harris ( m. 2012) Partners See list Barbi Benton (1969–1976) Sondra Theodore (1976–1981) Carrie Leigh (1983–1988) Brande Roderick (1999–2000) Holly Madison (2001–2008) Bridge\n\n[DOC 8] American magazine and publisher The Atlantic Cover of the September 2020 issue Editor-in-chief Jeffrey Goldberg Former editors James Bennet Categories Literature political science foreign affairs lifestyle Frequency Monthly (1857–2000, 2025–) Eleven issues a year (2001–2002) Ten issues a year (2003–2024) Publisher Laurene Powell Jobs Total circulation 1,107,293 (2024) Founder Moses Dresser Phillips Francis H. Underwood Ralph Waldo Emerson Henry Wadsworth Longfellow Founded 1857 ; 169 years ago ( 1857 ) First issue November 1, 1857 ; 168 years ago ( 1857-11-01 ) (as The Atlantic Monthly ) Company Emerson Collective Country United States Based in Boston until 2005; Washington, D. C. , U. S. since 2005 Language English Website theatlantic. com ISSN 1072-7825 (p\n\n[DOC 9] . Stimson Alfred Thomas Story Susan Straight John Jeremiah Sullivan Booth Tarkington Sara Teasdale Hunter S. Thompson Leon Trotsky Mark Twain John Updike Kurt Vonnegut William T. Vollmann Adelaide Cilley Waldron David Foster Wallace H. G. Wells E. B. White Woodrow Wilson Owen Wister Tom Wolfe Howard Zinn Slavoj Žižek Gallery Posters by Edward Penfield January 1894 January 1895 June 1896 December 1896 May 1897 Notes ^ While Scientific American , founded in 1845, is older, it did not become monthly until 1921. References ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ Facts on File 1980 Yearbook , pp.501, 582 ^ ^ ^ An American Album: One Hundred and Fifty Years of Harper's Magazine , a 712-page illustrated anthology, with an introduction by Lewis H. Lapham and a foreword by Arthur Schles\n\n\nSEARCH HISTORY: - Query: 'Raw Hero manga' → 5 docs\n- Query: 'Raw Hero serialized magazine' → 0 docs\n- Query: 'Raw Hero magazine publisher' → 3 docs\n- Query: 'Raw Hero manga serialization' → 0 docs\n- Query: 'Raw Hero magazine name' → 2 docs\n- Query: 'Publisher name founder birth year' → 5 docs\n- Query: 'Raw Hero manga Wikipedia' → 1 docs\n- Query: 'Raw Hero magazine' → 5 docs\n- Query: 'Magazine publisher founder birth year' → 3 docs\n\nFEEDBACK: We need to identify the magazine that serialized Raw Hero, then find the publishing company that owns that magazine, and finally locate the founder of that publishing company and the year the magazine ceased publication. The three short queries above start the chain: first locate the series, then the magazine, then the publisher.\nFirst query aims to identify the specific magazine that serialized Raw Hero. Second query retrieves the exact name of that magazine (if not already clear). Third query, using the publisher identified from the previous step, obtains the founder's birth year (or age) and the year the magazine ceased publication, allowing calculation of the founder's age when the magazine ended.\nFirst query should retrieve the Raw Hero Wikipedia page which lists the serialization magazine. Second query aims to get the specific magazine article (its name was not yet identified). Third query uses the magazine's publishing company to find the founder's birth year, enabling calculation of the founder's age at the magazine's termination.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Raw Hero manga Wikipedia\",\n \"Raw Hero magazine\",\n \"publisher founder of Raw Hero magazine\"\n ],\n \"feedback\": \"First query to locate the Raw Hero Wikipedia page and identify the magazine it was serialized in. Second query to confirm the exact magazine name. Third query to retrieve the publishing company’s founder (including birth year) and the year the magazine ceased publication, enabling calculation of the founder’s age at that time.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1971, + "osl": 990, + "total_tokens": 2961, + "latency_ms": 13751.26, + "tokens_per_second": 71.99 + }, + "context": { + "kept_docs_count": 9, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "f8526027-b4c6-4476-b0c4-8eec470fc9bf", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T03:10:17.463969Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: How old would the founder of the publishing company of the magazine that serialized the manga series Raw Hero have been the year the magazine ended publication?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] American writer (born 1938) Stewart Brand Brand in 2020 Born ( 1938-12-14 ) December 14, 1938 (age 87) Rockford, Illinois , United States Alma mater Stanford University Occupations Writer editor entrepreneur Known for Whole Earth Catalog The WELL Long Now Foundation Spouse(s) Lois Jennings (1966–1973) Ryan Phelan (1983–present) Website sb . longnow . org Stewart Brand (born December 14, 1938) is an American project developer and writer, best known as the co-founder and editor of the Whole Earth Catalog . Other organizations he has founded include the WELL , the Global Business Network , and the Long Now Foundation . He is the author of several books, most recently Whole Earth Discipline: An Ecopragmatist Manifesto and Maintenance: Of Everything . Life Brand\n\n\nKEPT DOCUMENTS (context only):\n[9 documents already kept as relevant]\n[KEPT 1] rior, Takes Action!!\" / \"Silent Warrior\" Transliteration: \" Nokosareta Yuiitsu no Nozomi... Mugon no Senshi Jūrokugō Tatsu!! \" ( Japanese : 残された唯一の望み...無言の戦士16号立つ!! ) Mitsuo Hashimoto Storyboarded by : Kazuhito Kikuchi Aya Matsui Masayuki Uchiyama August 5, 1992 ( 1992-08-05 ) October 19, 2000 (FUNi...\n[KEPT 2] article....\n[KEPT 3] rtainment Tag team Weight classes Wrestling ring Culture Backyard wrestling Cauliflower Alley Club Cauliflower ear Comic books Fantasy wrestling Films Luchador films Magazines Paintings Rob Schamberger Gustave Courbet Thomas Eakins William Etty George Luks Rib Ribera Steakhouse Ten-bell salute Video...\n[KEPT 4] )...\n[KEPT 5] )...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 649, + "osl": 442, + "total_tokens": 1091, + "latency_ms": 8531.59, + "tokens_per_second": 51.81 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 9 + } + }, + { + "call_id": "570010b4-fddc-4f25-b0d1-713f3d96c0bd", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:10:24.576903Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: How old would the founder of the publishing company of the magazine that serialized the manga series Raw Hero have been the year the magazine ended publication?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] rior, Takes Action!!\" / \"Silent Warrior\" Transliteration: \" Nokosareta Yuiitsu no Nozomi... Mugon no Senshi Jūrokugō Tatsu!! \" ( Japanese : 残された唯一の望み...無言の戦士16号立つ!! ) Mitsuo Hashimoto Storyboarded by : Kazuhito Kikuchi Aya Matsui Masayuki Uchiyama August 5, 1992 ( 1992-08-05 ) October 19, 2000 (FUNimation) May 15, 2001 (Ocean) 152 137 \"No. 17 Swallowed... The Transforming Cell is a Super Gourmet\" / \"Say Goodbye, 17\" Transliteration: \" Jūnanagō o Nomikonda... Henshin Seru wa Chōgurume \" ( Japanese : 17号を飲み込んだ...変身セルは超グルメ ) Yoshihiro Ueda Aya Matsui Tadayoshi Yamamuro August 12, 1992 ( 1992-08-12 ) October 20, 2000 (FUNimation) May 16, 2001 (Ocean) 153 138 \"Tomorrow, I Am Going to Pulverize You!! Goku's Challenge\" / \"Sacrifice\" Transliteration: \" Ashita wa Ome\n\n[DOC 2] article.\n\n[DOC 3] rtainment Tag team Weight classes Wrestling ring Culture Backyard wrestling Cauliflower Alley Club Cauliflower ear Comic books Fantasy wrestling Films Luchador films Magazines Paintings Rob Schamberger Gustave Courbet Thomas Eakins William Etty George Luks Rib Ribera Steakhouse Ten-bell salute Video games Wrestling personalities in politics Zubaz Media outlets Botchamania Box y Lucha Dark Side of the Ring Fighting Spirit Magazine Live Audio Wrestling OSW Review Power Slam Pro Wrestling Illustrated Solowrestling Súper Luchas WrestleCrap Wrestling Observer Newsletter Controversies Animals in professional wrestling Chris Benoit double-murder and suicide David Arquette in WCW Death of Owen Hart Fingerpoke of Doom Killing of Bruiser Brody Killing of Rikidōzan Mas\n\n[DOC 4] )\n\n[DOC 5] )\n\n[DOC 6] a b ^ ^ ^ ^ ^ ^ ^ a b c edition of February 14, 2004 of Raw ^ ^ ^ a b c ^ ^ a b c d e f g h i j k l m ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ External links The Official Website of WrestleMania XX\n\n[DOC 7] American magazine publisher (1926–2017) Hugh Hefner Hefner in 1970 Born Hugh Marston Hefner ( 1926-04-09 ) April 9, 1926 Chicago, Illinois, U. S. Died September 27, 2017 (2017-09-27) (aged 91) Los Angeles, California, U. S. Resting place Westwood Village Memorial Park Cemetery Other names Hef Alma mater University of Illinois at Urbana Champaign ( BA ) Occupations Businessman magazine publisher Years active 1953–2017 Title Editor-in-chief of Playboy Chief creative officer of Playboy Enterprises Spouses Mildred Williams ( m. 1949; div. 1959) Kimberley Conrad ( m. 1989; div. 2010) Crystal Harris ( m. 2012) Partners See list Barbi Benton (1969–1976) Sondra Theodore (1976–1981) Carrie Leigh (1983–1988) Brande Roderick (1999–2000) Holly Madison (2001–2008) Bridge\n\n[DOC 8] American magazine and publisher The Atlantic Cover of the September 2020 issue Editor-in-chief Jeffrey Goldberg Former editors James Bennet Categories Literature political science foreign affairs lifestyle Frequency Monthly (1857–2000, 2025–) Eleven issues a year (2001–2002) Ten issues a year (2003–2024) Publisher Laurene Powell Jobs Total circulation 1,107,293 (2024) Founder Moses Dresser Phillips Francis H. Underwood Ralph Waldo Emerson Henry Wadsworth Longfellow Founded 1857 ; 169 years ago ( 1857 ) First issue November 1, 1857 ; 168 years ago ( 1857-11-01 ) (as The Atlantic Monthly ) Company Emerson Collective Country United States Based in Boston until 2005; Washington, D. C. , U. S. since 2005 Language English Website theatlantic. com ISSN 1072-7825 (p\n\n[DOC 9] . Stimson Alfred Thomas Story Susan Straight John Jeremiah Sullivan Booth Tarkington Sara Teasdale Hunter S. Thompson Leon Trotsky Mark Twain John Updike Kurt Vonnegut William T. Vollmann Adelaide Cilley Waldron David Foster Wallace H. G. Wells E. B. White Woodrow Wilson Owen Wister Tom Wolfe Howard Zinn Slavoj Žižek Gallery Posters by Edward Penfield January 1894 January 1895 June 1896 December 1896 May 1897 Notes ^ While Scientific American , founded in 1845, is older, it did not become monthly until 1921. References ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ Facts on File 1980 Yearbook , pp.501, 582 ^ ^ ^ An American Album: One Hundred and Fifty Years of Harper's Magazine , a 712-page illustrated anthology, with an introduction by Lewis H. Lapham and a foreword by Arthur Schles\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents do not provide the name of the magazine that serialized the manga series 'Raw Hero', nor the publishing company of that magazine, nor the founder of that publishing company, nor the year the magazine ended publication. Without these critical facts, the founder's age at the magazine's end cannot be calculated.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1501, + "osl": 607, + "total_tokens": 2108, + "latency_ms": 7112.08, + "tokens_per_second": 85.35 + }, + "context": { + "kept_docs_count": 9, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "9e38197e-361c-4031-be45-5698c7474f37", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:10:31.166047Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: How old would the founder of the publishing company of the magazine that serialized the manga series Raw Hero have been the year the magazine ended publication?\n\nDOCUMENTS:\n\n[DOC 1] rior, Takes Action!!\" / \"Silent Warrior\" Transliteration: \" Nokosareta Yuiitsu no Nozomi... Mugon no Senshi Jūrokugō Tatsu!! \" ( Japanese : 残された唯一の望み...無言の戦士16号立つ!! ) Mitsuo Hashimoto Storyboarded by : Kazuhito Kikuchi Aya Matsui Masayuki Uchiyama August 5, 1992 ( 1992-08-05 ) October 19, 2000 (FUNimation) May 15, 2001 (Ocean) 152 137 \"No. 17 Swallowed... The Transforming Cell is a Super Gourmet\" / \"Say Goodbye, 17\" Transliteration: \" Jūnanagō o Nomikonda... Henshin Seru wa Chōgurume \" ( Japanese : 17号を飲み込んだ...変身セルは超グルメ ) Yoshihiro Ueda Aya Matsui Tadayoshi Yamamuro August 12, 1992 ( 1992-08-12 ) October 20, 2000 (FUNimation) May 16, 2001 (Ocean) 153 138 \"Tomorrow, I Am Going to Pulverize You!! Goku's Challenge\" / \"Sacrifice\" Transliteration: \" Ashita wa Ome\n\n[DOC 2] article.\n\n[DOC 3] rtainment Tag team Weight classes Wrestling ring Culture Backyard wrestling Cauliflower Alley Club Cauliflower ear Comic books Fantasy wrestling Films Luchador films Magazines Paintings Rob Schamberger Gustave Courbet Thomas Eakins William Etty George Luks Rib Ribera Steakhouse Ten-bell salute Video games Wrestling personalities in politics Zubaz Media outlets Botchamania Box y Lucha Dark Side of the Ring Fighting Spirit Magazine Live Audio Wrestling OSW Review Power Slam Pro Wrestling Illustrated Solowrestling Súper Luchas WrestleCrap Wrestling Observer Newsletter Controversies Animals in professional wrestling Chris Benoit double-murder and suicide David Arquette in WCW Death of Owen Hart Fingerpoke of Doom Killing of Bruiser Brody Killing of Rikidōzan Mas\n\n[DOC 4] )\n\n[DOC 5] )\n\n[DOC 6] a b ^ ^ ^ ^ ^ ^ ^ a b c edition of February 14, 2004 of Raw ^ ^ ^ a b c ^ ^ a b c d e f g h i j k l m ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ External links The Official Website of WrestleMania XX\n\n[DOC 7] American magazine publisher (1926–2017) Hugh Hefner Hefner in 1970 Born Hugh Marston Hefner ( 1926-04-09 ) April 9, 1926 Chicago, Illinois, U. S. Died September 27, 2017 (2017-09-27) (aged 91) Los Angeles, California, U. S. Resting place Westwood Village Memorial Park Cemetery Other names Hef Alma mater University of Illinois at Urbana Champaign ( BA ) Occupations Businessman magazine publisher Years active 1953–2017 Title Editor-in-chief of Playboy Chief creative officer of Playboy Enterprises Spouses Mildred Williams ( m. 1949; div. 1959) Kimberley Conrad ( m. 1989; div. 2010) Crystal Harris ( m. 2012) Partners See list Barbi Benton (1969–1976) Sondra Theodore (1976–1981) Carrie Leigh (1983–1988) Brande Roderick (1999–2000) Holly Madison (2001–2008) Bridge\n\n[DOC 8] American magazine and publisher The Atlantic Cover of the September 2020 issue Editor-in-chief Jeffrey Goldberg Former editors James Bennet Categories Literature political science foreign affairs lifestyle Frequency Monthly (1857–2000, 2025–) Eleven issues a year (2001–2002) Ten issues a year (2003–2024) Publisher Laurene Powell Jobs Total circulation 1,107,293 (2024) Founder Moses Dresser Phillips Francis H. Underwood Ralph Waldo Emerson Henry Wadsworth Longfellow Founded 1857 ; 169 years ago ( 1857 ) First issue November 1, 1857 ; 168 years ago ( 1857-11-01 ) (as The Atlantic Monthly ) Company Emerson Collective Country United States Based in Boston until 2005; Washington, D. C. , U. S. since 2005 Language English Website theatlantic. com ISSN 1072-7825 (p\n\n[DOC 9] . Stimson Alfred Thomas Story Susan Straight John Jeremiah Sullivan Booth Tarkington Sara Teasdale Hunter S. Thompson Leon Trotsky Mark Twain John Updike Kurt Vonnegut William T. Vollmann Adelaide Cilley Waldron David Foster Wallace H. G. Wells E. B. White Woodrow Wilson Owen Wister Tom Wolfe Howard Zinn Slavoj Žižek Gallery Posters by Edward Penfield January 1894 January 1895 June 1896 December 1896 May 1897 Notes ^ While Scientific American , founded in 1845, is older, it did not become monthly until 1921. References ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ Facts on File 1980 Yearbook , pp.501, 582 ^ ^ ^ An American Album: One Hundred and Fifty Years of Harper's Magazine , a 712-page illustrated anthology, with an introduction by Lewis H. Lapham and a foreword by Arthur Schles\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1344, + "osl": 331, + "total_tokens": 1675, + "latency_ms": 6588.6, + "tokens_per_second": 50.24 + }, + "context": { + "num_documents_used": 9, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:10:31.166798Z", + "summary": { + "total_llm_calls": 10, + "total_hops": 5, + "total_input_tokens": 11920, + "total_output_tokens": 6634, + "total_tokens": 18554, + "total_latency_ms": 109505.93, + "average_tokens_per_second": 62.61, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.m.wikipedia.org/wiki/Raw_Hero", + "https://en.m.wikipedia.org/wiki/Evening_(magazine)", + "https://en.m.wikipedia.org/wiki/Kodansha", + "https://en.m.wikipedia.org/wiki/Seiji_Noma" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "145" + } + }, + { + "query_id": "34", + "original_query": "The oldest extant football team in Italy plays in a stadium. The stadium is named after a person. Who was the emperor of China when that person was 5 years old?", + "timestamp_start": "2026-05-16T03:10:31.195559Z", + "llm_calls": [ + { + "call_id": "66749ee8-cf8a-4950-ad12-86537d45c614", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:10:43.472710Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: The oldest extant football team in Italy plays in a stadium. The stadium is named after a person. Who was the emperor of China when that person was 5 years old?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"oldest football club Italy\",\n \"Stadio Luigi Ferraris named after\",\n \"Luigi Ferraris birth year\"\n ],\n \"feedback\": \"Need to identify Italy's oldest extant football team, the stadium it uses, and the person the stadium is named for. These queries retrieve the club, the stadium's namesake, and that person's birth year, enabling determination of the Chinese emperor reigning when the namesake was five.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 460, + "osl": 615, + "total_tokens": 1075, + "latency_ms": 12276.99, + "tokens_per_second": 50.09 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "e26d2f08-1309-46d3-9e00-ac59e8cd2ecb", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:10:55.969983Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: The oldest extant football team in Italy plays in a stadium. The stadium is named after a person. Who was the emperor of China when that person was 5 years old?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Association football club in Italy Football club Genoa Full name Genoa Cricket and Football Club S. p. A. Nicknames Il Grifone (The Griffin ) I Rossoblù (The Red and Blues) Il Vecchio Balordo (The Old Fool) Founded 7 September 1893 ; 132 years ago ( 7 September 1893 ) Ground Stadio Luigi Ferraris Capacity 33,205 Owner Dan Șucu President Dan Șucu Head coach Daniele De Rossi League Serie A 2024–25 Serie A, 13th of 20 Website genoacfc . it Home colours Away colours Third colours Current season The performance of Genoa in the Italian football league structure since the first season of a unified Serie A (1929–30). Their Scudetti lie before this era. Genoa Cricket and Football Club ( Italian pronunciation: [ˈdʒeːnoa] ) is an Italian professional football club base\n\n[NEW 2] III. Kerületi TVE , which whose football section was founded officially in 1899 but stemmed from the 1897-founded Budai Football Csapat. The first ever football club to be founded in Hungary was Budapesti Torna Club having founded its football section in February 1897, dissolved in 1945–46. [ citation needed ] Italy In Italy, Genoa C. F. C. is the oldest active football club: it was founded by Charles De Grave Sells, S. Green, George Blake, W. Rilley, George Dormer Fawcus, H. M. Sandys, E. De Thierry, Johnathan Summerhill Sr., Johnathan Summerhill Jr. and Sir Charles Alfred Payton in Genoa on 7 September 1893. However, Genoa C. F. C. was not the first Italian football club, being Torino Football & Cricket Club (1887) but its history lasted only for 4 years.\n\n[NEW 3] 2, and played there his entire career, where he won the reserve championship ( it ) 4–0 against Juventus in 1904. He studied engineering at the Polytechnic University of Milan from 1906 to 1911. Afterwards, he worked at the Officine Elettriche Genovesi (OEG) in San Fruttuoso , then at Pirelli in Milan . During the World War I , Ferraris served as a volunteer then reached the rank of lieutenant. On 23 August 1915, he died due to a 152mm shrapnel artillery shell which killed him instantly, during a mission in Val Posina, a minor valley of the Val d'Astico [ it ] at the municipality of Posina , and was buried by his comrades in arms at Monte Maggio [ it ] . In 1933, the stadium, Stadio Luigi Ferraris , was named after him. His Silver Medal of Military Valor was\n\n[NEW 4] Football stadium in Genoa, Italy 44°24′59′′N 8°57′9′′E  /  44.41639°N 8.95250°E  / 44.41639; 8.95250 Luigi Ferraris Marassi Interactive map of Luigi Ferraris Location Via Giovanni De Prà 1, Genoa , Italy Owner Municipality of Genoa Capacity 33,205 Surface Grass 105 × 68 meters Construction Opened 22 January 1911 Renovated 1987–1989, 2015, 2018 Architect Vittorio Gregotti (1987–1989) Tenants Genoa CFC (1911–present) UC Sampdoria (1946–present) Italy national football team (selected matches) The Stadio Luigi Ferraris , also known as the Marassi from the name of the neighbourhood where it is located, is a multi-use stadium in Genoa , Italy. The home of Genoa CFC and UC Sampdoria football clubs, it opened in 1911 and is the oldest stadium still in use for foo\n\n[NEW 5] 6 March 2013) was a racing driver from Italy . He participated in two non-championship Formula One Grands Prix driving a Ferrari 312T for Giancarlo Minardi . He was the uncle of the racing drivers Pierluigi Martini and Oliver Martini . Racing record Complete European Formula Two Championship results ( key ) (Races in bold indicate pole position; races in italics indicate fastest lap) Year Entrant Chassis Engine 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Pos. Pts 1974 Trivellato Racing March 742 BMW BAR 7 HOC PAU 9 SAL HOC Ret MUG 5 KAR PER 16th 2 Scuderia Everest HOC 16 VAL Ret 1975 Scuderia Everest March 752 BMW EST 6 THR 3 HOC 14 NÜR 9 PAU DNQ HOC 10 SAL 16 ROU MUG Ret PER 7 SIL 6 ZOL NOG 13 VAL 5 15th 8 1976 Scuderia Everest March 762 BMW HOC THR 19 VAL 15 SAL Ret\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 5 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 1, 1, 1, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1362, + "osl": 688, + "total_tokens": 2050, + "latency_ms": 11959.95, + "tokens_per_second": 57.53 + }, + "context": { + "num_documents_evaluated": 5, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "e62fd4b4-df09-425d-b013-3fc27c2597b4", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:10:56.720848Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: The oldest extant football team in Italy plays in a stadium. The stadium is named after a person. Who was the emperor of China when that person was 5 years old?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Association football club in Italy Football club Genoa Full name Genoa Cricket and Football Club S. p. A. Nicknames Il Grifone (The Griffin ) I Rossoblù (The Red and Blues) Il Vecchio Balordo (The Old Fool) Founded 7 September 1893 ; 132 years ago ( 7 September 1893 ) Ground Stadio Luigi Ferraris Capacity 33,205 Owner Dan Șucu President Dan Șucu Head coach Daniele De Rossi League Serie A 2024–25 Serie A, 13th of 20 Website genoacfc . it Home colours Away colours Third colours Current season The performance of Genoa in the Italian football league structure since the first season of a unified Serie A (1929–30). Their Scudetti lie before this era. Genoa Cricket and Football Club ( Italian pronunciation: [ˈdʒeːnoa] ) is an Italian professional football club base\n\n[DOC 2] III. Kerületi TVE , which whose football section was founded officially in 1899 but stemmed from the 1897-founded Budai Football Csapat. The first ever football club to be founded in Hungary was Budapesti Torna Club having founded its football section in February 1897, dissolved in 1945–46. [ citation needed ] Italy In Italy, Genoa C. F. C. is the oldest active football club: it was founded by Charles De Grave Sells, S. Green, George Blake, W. Rilley, George Dormer Fawcus, H. M. Sandys, E. De Thierry, Johnathan Summerhill Sr., Johnathan Summerhill Jr. and Sir Charles Alfred Payton in Genoa on 7 September 1893. However, Genoa C. F. C. was not the first Italian football club, being Torino Football & Cricket Club (1887) but its history lasted only for 4 years.\n\n[DOC 3] 2, and played there his entire career, where he won the reserve championship ( it ) 4–0 against Juventus in 1904. He studied engineering at the Polytechnic University of Milan from 1906 to 1911. Afterwards, he worked at the Officine Elettriche Genovesi (OEG) in San Fruttuoso , then at Pirelli in Milan . During the World War I , Ferraris served as a volunteer then reached the rank of lieutenant. On 23 August 1915, he died due to a 152mm shrapnel artillery shell which killed him instantly, during a mission in Val Posina, a minor valley of the Val d'Astico [ it ] at the municipality of Posina , and was buried by his comrades in arms at Monte Maggio [ it ] . In 1933, the stadium, Stadio Luigi Ferraris , was named after him. His Silver Medal of Military Valor was\n\n[DOC 4] Football stadium in Genoa, Italy 44°24′59′′N 8°57′9′′E  /  44.41639°N 8.95250°E  / 44.41639; 8.95250 Luigi Ferraris Marassi Interactive map of Luigi Ferraris Location Via Giovanni De Prà 1, Genoa , Italy Owner Municipality of Genoa Capacity 33,205 Surface Grass 105 × 68 meters Construction Opened 22 January 1911 Renovated 1987–1989, 2015, 2018 Architect Vittorio Gregotti (1987–1989) Tenants Genoa CFC (1911–present) UC Sampdoria (1946–present) Italy national football team (selected matches) The Stadio Luigi Ferraris , also known as the Marassi from the name of the neighbourhood where it is located, is a multi-use stadium in Genoa , Italy. The home of Genoa CFC and UC Sampdoria football clubs, it opened in 1911 and is the oldest stadium still in use for foo\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents identify the oldest extant Italian football team (Genoa CFC) and its stadium (Stadio Luigi Ferraris), and name the person the stadium is named after (Luigi Ferraris). However, they do not provide Luigi Ferraris' birth year (or date), which is required to determine the year when he was 5 years old and thus identify the Chinese emperor at that time. Therefore, a key fact is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1155, + "osl": 359, + "total_tokens": 1514, + "latency_ms": 750.06, + "tokens_per_second": 478.63 + }, + "context": { + "kept_docs_count": 4, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "2835e129-493f-40f6-9851-d2fa8708c622", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:11:03.469562Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: The oldest extant football team in Italy plays in a stadium. The stadium is named after a person. Who was the emperor of China when that person was 5 years old?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Association football club in Italy Football club Genoa Full name Genoa Cricket and Football Club S. p. A. Nicknames Il Grifone (The Griffin ) I Rossoblù (The Red and Blues) Il Vecchio Balordo (The Old Fool) Founded 7 September 1893 ; 132 years ago ( 7 September 1893 ) Ground Stadio Luigi Ferraris Capacity 33,205 Owner Dan Șucu President Dan Șucu Head coach Daniele De Rossi League Serie A 2024–25 Serie A, 13th of 20 Website genoacfc . it Home colours Away colours Third colours Current season The performance of Genoa in the Italian football league structure since the first season of a unified Serie A (1929–30). Their Scudetti lie before this era. Genoa Cricket and Football Club ( Italian pronunciation: [ˈdʒeːnoa] ) is an Italian professional football club base\n\n[DOC 2] III. Kerületi TVE , which whose football section was founded officially in 1899 but stemmed from the 1897-founded Budai Football Csapat. The first ever football club to be founded in Hungary was Budapesti Torna Club having founded its football section in February 1897, dissolved in 1945–46. [ citation needed ] Italy In Italy, Genoa C. F. C. is the oldest active football club: it was founded by Charles De Grave Sells, S. Green, George Blake, W. Rilley, George Dormer Fawcus, H. M. Sandys, E. De Thierry, Johnathan Summerhill Sr., Johnathan Summerhill Jr. and Sir Charles Alfred Payton in Genoa on 7 September 1893. However, Genoa C. F. C. was not the first Italian football club, being Torino Football & Cricket Club (1887) but its history lasted only for 4 years.\n\n[DOC 3] 2, and played there his entire career, where he won the reserve championship ( it ) 4–0 against Juventus in 1904. He studied engineering at the Polytechnic University of Milan from 1906 to 1911. Afterwards, he worked at the Officine Elettriche Genovesi (OEG) in San Fruttuoso , then at Pirelli in Milan . During the World War I , Ferraris served as a volunteer then reached the rank of lieutenant. On 23 August 1915, he died due to a 152mm shrapnel artillery shell which killed him instantly, during a mission in Val Posina, a minor valley of the Val d'Astico [ it ] at the municipality of Posina , and was buried by his comrades in arms at Monte Maggio [ it ] . In 1933, the stadium, Stadio Luigi Ferraris , was named after him. His Silver Medal of Military Valor was\n\n[DOC 4] Football stadium in Genoa, Italy 44°24′59′′N 8°57′9′′E  /  44.41639°N 8.95250°E  / 44.41639; 8.95250 Luigi Ferraris Marassi Interactive map of Luigi Ferraris Location Via Giovanni De Prà 1, Genoa , Italy Owner Municipality of Genoa Capacity 33,205 Surface Grass 105 × 68 meters Construction Opened 22 January 1911 Renovated 1987–1989, 2015, 2018 Architect Vittorio Gregotti (1987–1989) Tenants Genoa CFC (1911–present) UC Sampdoria (1946–present) Italy national football team (selected matches) The Stadio Luigi Ferraris , also known as the Marassi from the name of the neighbourhood where it is located, is a multi-use stadium in Genoa , Italy. The home of Genoa CFC and UC Sampdoria football clubs, it opened in 1911 and is the oldest stadium still in use for foo\n\n\nSEARCH HISTORY: - Query: 'oldest football club Italy' → 2 docs\n- Query: 'Stadio Luigi Ferraris named after' → 2 docs\n- Query: 'Luigi Ferraris birth year' → 1 docs\n\nFEEDBACK: Need to identify Italy's oldest extant football team, the stadium it uses, and the person the stadium is named for. These queries retrieve the club, the stadium's namesake, and that person's birth year, enabling determination of the Chinese emperor reigning when the namesake was five.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Luigi Ferraris birth year\",\n \"Guangxu Emperor reign\",\n \"Chinese emperor 1892\"\n ],\n \"feedback\": \"We have identified the oldest extant Italian football club (Genoa CFC) and its stadium (Stadio Luigi Ferraris) named after Luigi Ferraris. Missing information: Ferraris' birth year to calculate his age 5, and the Chinese emperor ruling in that year. The above queries aim to retrieve Ferraris' birth year and the relevant Chinese emperor.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1364, + "osl": 113, + "total_tokens": 1477, + "latency_ms": 6748.05, + "tokens_per_second": 16.75 + }, + "context": { + "kept_docs_count": 4, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "8e0f12cf-c9cd-4560-8caa-f2b3e3e0227a", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T03:11:09.720667Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: The oldest extant football team in Italy plays in a stadium. The stadium is named after a person. Who was the emperor of China when that person was 5 years old?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] (s) Guangxu ( 光緒 ) 25 February 1875 – 14 November 1908 Cousin of Tongzhi 14 August 1871 – 14 November 1908 (aged 37) Initiated the failed Hundred Days' Reform , and from 1898 on, his reign was completely dominated by Empress Dowager Cixi . Died of poisoning, possibly from Cixi. Xuantong 宣統 Other names Temple name : Gongzong ( 恭宗 ) Posthumous name (short) : Emperor Min ( 愍帝 ) Posthumous name (long) : Emperor Peitian Tongyun Fagu Shaotong Cuiwen Jingfu Kuanrui Zhengmu Tiren Lixiao Min ( 配天同運法古紹統粹文敬孚寬睿正穆體仁立孝愍皇帝 ) Puyi 溥儀 2 December 1908 – 12 February 1912 (3 years, 2 months and 10 days) Era(s) Xuantong ( 宣統 ) 2 December 1908 – 12 February 1912 Nephew of Guangxu 7 February 1906 – 17 October 1967 (aged 61) Reigned as a young child, but was forced to abdicate in 1\n\n\nKEPT DOCUMENTS (context only):\n[4 documents already kept as relevant]\n[KEPT 1] Association football club in Italy Football club Genoa Full name Genoa Cricket and Football Club S. p. A. Nicknames Il Grifone (The Griffin ) I Rossoblù (The Red and Blues) Il Vecchio Balordo (The Old Fool) Founded 7 September 1893 ; 132 years ago ( 7 September 1893 ) Ground Stadio Luigi Ferraris Ca...\n[KEPT 2] III. Kerületi TVE , which whose football section was founded officially in 1899 but stemmed from the 1897-founded Budai Football Csapat. The first ever football club to be founded in Hungary was Budapesti Torna Club having founded its football section in February 1897, dissolved in 1945–46. [ citati...\n[KEPT 3] 2, and played there his entire career, where he won the reserve championship ( it ) 4–0 against Juventus in 1904. He studied engineering at the Polytechnic University of Milan from 1906 to 1911. Afterwards, he worked at the Officine Elettriche Genovesi (OEG) in San Fruttuoso , then at Pirelli in Mil...\n[KEPT 4] Football stadium in Genoa, Italy 44°24′59′′N 8°57′9′′E  /  44.41639°N 8.95250°E  / 44.41639; 8.95250 Luigi Ferraris Marassi Interactive map of Luigi Ferraris Location Via Giovanni De Prà 1, Genoa , Italy Owner Municipality of Genoa Capacity 33,205 Surface Grass 105 × 68 meters Construction Opened...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 920, + "osl": 668, + "total_tokens": 1588, + "latency_ms": 5672.99, + "tokens_per_second": 117.75 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 4 + } + }, + { + "call_id": "df01a3f0-d686-452b-b181-177fe90170c2", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:11:13.420122Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: The oldest extant football team in Italy plays in a stadium. The stadium is named after a person. Who was the emperor of China when that person was 5 years old?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Association football club in Italy Football club Genoa Full name Genoa Cricket and Football Club S. p. A. Nicknames Il Grifone (The Griffin ) I Rossoblù (The Red and Blues) Il Vecchio Balordo (The Old Fool) Founded 7 September 1893 ; 132 years ago ( 7 September 1893 ) Ground Stadio Luigi Ferraris Capacity 33,205 Owner Dan Șucu President Dan Șucu Head coach Daniele De Rossi League Serie A 2024–25 Serie A, 13th of 20 Website genoacfc . it Home colours Away colours Third colours Current season The performance of Genoa in the Italian football league structure since the first season of a unified Serie A (1929–30). Their Scudetti lie before this era. Genoa Cricket and Football Club ( Italian pronunciation: [ˈdʒeːnoa] ) is an Italian professional football club base\n\n[DOC 2] III. Kerületi TVE , which whose football section was founded officially in 1899 but stemmed from the 1897-founded Budai Football Csapat. The first ever football club to be founded in Hungary was Budapesti Torna Club having founded its football section in February 1897, dissolved in 1945–46. [ citation needed ] Italy In Italy, Genoa C. F. C. is the oldest active football club: it was founded by Charles De Grave Sells, S. Green, George Blake, W. Rilley, George Dormer Fawcus, H. M. Sandys, E. De Thierry, Johnathan Summerhill Sr., Johnathan Summerhill Jr. and Sir Charles Alfred Payton in Genoa on 7 September 1893. However, Genoa C. F. C. was not the first Italian football club, being Torino Football & Cricket Club (1887) but its history lasted only for 4 years.\n\n[DOC 3] 2, and played there his entire career, where he won the reserve championship ( it ) 4–0 against Juventus in 1904. He studied engineering at the Polytechnic University of Milan from 1906 to 1911. Afterwards, he worked at the Officine Elettriche Genovesi (OEG) in San Fruttuoso , then at Pirelli in Milan . During the World War I , Ferraris served as a volunteer then reached the rank of lieutenant. On 23 August 1915, he died due to a 152mm shrapnel artillery shell which killed him instantly, during a mission in Val Posina, a minor valley of the Val d'Astico [ it ] at the municipality of Posina , and was buried by his comrades in arms at Monte Maggio [ it ] . In 1933, the stadium, Stadio Luigi Ferraris , was named after him. His Silver Medal of Military Valor was\n\n[DOC 4] Football stadium in Genoa, Italy 44°24′59′′N 8°57′9′′E  /  44.41639°N 8.95250°E  / 44.41639; 8.95250 Luigi Ferraris Marassi Interactive map of Luigi Ferraris Location Via Giovanni De Prà 1, Genoa , Italy Owner Municipality of Genoa Capacity 33,205 Surface Grass 105 × 68 meters Construction Opened 22 January 1911 Renovated 1987–1989, 2015, 2018 Architect Vittorio Gregotti (1987–1989) Tenants Genoa CFC (1911–present) UC Sampdoria (1946–present) Italy national football team (selected matches) The Stadio Luigi Ferraris , also known as the Marassi from the name of the neighbourhood where it is located, is a multi-use stadium in Genoa , Italy. The home of Genoa CFC and UC Sampdoria football clubs, it opened in 1911 and is the oldest stadium still in use for foo\n\n[DOC 5] (s) Guangxu ( 光緒 ) 25 February 1875 – 14 November 1908 Cousin of Tongzhi 14 August 1871 – 14 November 1908 (aged 37) Initiated the failed Hundred Days' Reform , and from 1898 on, his reign was completely dominated by Empress Dowager Cixi . Died of poisoning, possibly from Cixi. Xuantong 宣統 Other names Temple name : Gongzong ( 恭宗 ) Posthumous name (short) : Emperor Min ( 愍帝 ) Posthumous name (long) : Emperor Peitian Tongyun Fagu Shaotong Cuiwen Jingfu Kuanrui Zhengmu Tiren Lixiao Min ( 配天同運法古紹統粹文敬孚寬睿正穆體仁立孝愍皇帝 ) Puyi 溥儀 2 December 1908 – 12 February 1912 (3 years, 2 months and 10 days) Era(s) Xuantong ( 宣統 ) 2 December 1908 – 12 February 1912 Nephew of Guangxu 7 February 1906 – 17 October 1967 (aged 61) Reigned as a young child, but was forced to abdicate in 1\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"We can identify the oldest extant Italian football team (Genoa C.F.C.) and its stadium (Stadio Luigi Ferraris) named after Luigi Ferraris. However, the documents do not provide Luigi Ferraris's birth year (or the year when he was 5 years old), which is needed to determine the Chinese emperor at that time. Therefore, the necessary information is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1423, + "osl": 93, + "total_tokens": 1516, + "latency_ms": 3698.77, + "tokens_per_second": 25.14 + }, + "context": { + "kept_docs_count": 5, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "14d7dc5a-71ef-486d-a4ec-98a7b4ce9de2", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:11:19.323151Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: The oldest extant football team in Italy plays in a stadium. The stadium is named after a person. Who was the emperor of China when that person was 5 years old?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Association football club in Italy Football club Genoa Full name Genoa Cricket and Football Club S. p. A. Nicknames Il Grifone (The Griffin ) I Rossoblù (The Red and Blues) Il Vecchio Balordo (The Old Fool) Founded 7 September 1893 ; 132 years ago ( 7 September 1893 ) Ground Stadio Luigi Ferraris Capacity 33,205 Owner Dan Șucu President Dan Șucu Head coach Daniele De Rossi League Serie A 2024–25 Serie A, 13th of 20 Website genoacfc . it Home colours Away colours Third colours Current season The performance of Genoa in the Italian football league structure since the first season of a unified Serie A (1929–30). Their Scudetti lie before this era. Genoa Cricket and Football Club ( Italian pronunciation: [ˈdʒeːnoa] ) is an Italian professional football club base\n\n[DOC 2] III. Kerületi TVE , which whose football section was founded officially in 1899 but stemmed from the 1897-founded Budai Football Csapat. The first ever football club to be founded in Hungary was Budapesti Torna Club having founded its football section in February 1897, dissolved in 1945–46. [ citation needed ] Italy In Italy, Genoa C. F. C. is the oldest active football club: it was founded by Charles De Grave Sells, S. Green, George Blake, W. Rilley, George Dormer Fawcus, H. M. Sandys, E. De Thierry, Johnathan Summerhill Sr., Johnathan Summerhill Jr. and Sir Charles Alfred Payton in Genoa on 7 September 1893. However, Genoa C. F. C. was not the first Italian football club, being Torino Football & Cricket Club (1887) but its history lasted only for 4 years.\n\n[DOC 3] 2, and played there his entire career, where he won the reserve championship ( it ) 4–0 against Juventus in 1904. He studied engineering at the Polytechnic University of Milan from 1906 to 1911. Afterwards, he worked at the Officine Elettriche Genovesi (OEG) in San Fruttuoso , then at Pirelli in Milan . During the World War I , Ferraris served as a volunteer then reached the rank of lieutenant. On 23 August 1915, he died due to a 152mm shrapnel artillery shell which killed him instantly, during a mission in Val Posina, a minor valley of the Val d'Astico [ it ] at the municipality of Posina , and was buried by his comrades in arms at Monte Maggio [ it ] . In 1933, the stadium, Stadio Luigi Ferraris , was named after him. His Silver Medal of Military Valor was\n\n[DOC 4] Football stadium in Genoa, Italy 44°24′59′′N 8°57′9′′E  /  44.41639°N 8.95250°E  / 44.41639; 8.95250 Luigi Ferraris Marassi Interactive map of Luigi Ferraris Location Via Giovanni De Prà 1, Genoa , Italy Owner Municipality of Genoa Capacity 33,205 Surface Grass 105 × 68 meters Construction Opened 22 January 1911 Renovated 1987–1989, 2015, 2018 Architect Vittorio Gregotti (1987–1989) Tenants Genoa CFC (1911–present) UC Sampdoria (1946–present) Italy national football team (selected matches) The Stadio Luigi Ferraris , also known as the Marassi from the name of the neighbourhood where it is located, is a multi-use stadium in Genoa , Italy. The home of Genoa CFC and UC Sampdoria football clubs, it opened in 1911 and is the oldest stadium still in use for foo\n\n[DOC 5] (s) Guangxu ( 光緒 ) 25 February 1875 – 14 November 1908 Cousin of Tongzhi 14 August 1871 – 14 November 1908 (aged 37) Initiated the failed Hundred Days' Reform , and from 1898 on, his reign was completely dominated by Empress Dowager Cixi . Died of poisoning, possibly from Cixi. Xuantong 宣統 Other names Temple name : Gongzong ( 恭宗 ) Posthumous name (short) : Emperor Min ( 愍帝 ) Posthumous name (long) : Emperor Peitian Tongyun Fagu Shaotong Cuiwen Jingfu Kuanrui Zhengmu Tiren Lixiao Min ( 配天同運法古紹統粹文敬孚寬睿正穆體仁立孝愍皇帝 ) Puyi 溥儀 2 December 1908 – 12 February 1912 (3 years, 2 months and 10 days) Era(s) Xuantong ( 宣統 ) 2 December 1908 – 12 February 1912 Nephew of Guangxu 7 February 1906 – 17 October 1967 (aged 61) Reigned as a young child, but was forced to abdicate in 1\n\n\nSEARCH HISTORY: - Query: 'oldest football club Italy' → 2 docs\n- Query: 'Stadio Luigi Ferraris named after' → 2 docs\n- Query: 'Luigi Ferraris birth year' → 1 docs\n- Query: 'Luigi Ferraris birth year' → 0 docs\n- Query: 'Guangxu Emperor reign' → 1 docs\n- Query: 'Chinese emperor 1892' → 0 docs\n\nFEEDBACK: Need to identify Italy's oldest extant football team, the stadium it uses, and the person the stadium is named for. These queries retrieve the club, the stadium's namesake, and that person's birth year, enabling determination of the Chinese emperor reigning when the namesake was five.\nWe have identified the oldest extant Italian football club (Genoa CFC) and its stadium (Stadio Luigi Ferraris) named after Luigi Ferraris. Missing information: Ferraris' birth year to calculate his age 5, and the Chinese emperor ruling in that year. The above queries aim to retrieve Ferraris' birth year and the relevant Chinese emperor.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Luigi Ferraris (footballer) Wikipedia\",\n \"Luigi Ferraris born\",\n \"China emperor 1892\"\n ],\n \"feedback\": \"We have identified the oldest extant Italian football club (Genoa CFC) and its stadium (Stadio Luigi Ferraris) named after Luigi Ferraris. Missing information: Ferraris' birth year to determine his age‑5 year, and which Chinese emperor ruled in that year. The first two queries aim to retrieve Ferraris' birth date; the third query seeks the Chinese emperor in 1892 (the likely age‑5 year).\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1798, + "osl": 132, + "total_tokens": 1930, + "latency_ms": 5902.51, + "tokens_per_second": 22.36 + }, + "context": { + "kept_docs_count": 5, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "a2d07488-bb25-46e5-ac21-8ad38e62e783", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:11:25.866430Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: The oldest extant football team in Italy plays in a stadium. The stadium is named after a person. Who was the emperor of China when that person was 5 years old?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Association football club in Italy Football club Genoa Full name Genoa Cricket and Football Club S. p. A. Nicknames Il Grifone (The Griffin ) I Rossoblù (The Red and Blues) Il Vecchio Balordo (The Old Fool) Founded 7 September 1893 ; 132 years ago ( 7 September 1893 ) Ground Stadio Luigi Ferraris Capacity 33,205 Owner Dan Șucu President Dan Șucu Head coach Daniele De Rossi League Serie A 2024–25 Serie A, 13th of 20 Website genoacfc . it Home colours Away colours Third colours Current season The performance of Genoa in the Italian football league structure since the first season of a unified Serie A (1929–30). Their Scudetti lie before this era. Genoa Cricket and Football Club ( Italian pronunciation: [ˈdʒeːnoa] ) is an Italian professional football club base\n\n[DOC 2] III. Kerületi TVE , which whose football section was founded officially in 1899 but stemmed from the 1897-founded Budai Football Csapat. The first ever football club to be founded in Hungary was Budapesti Torna Club having founded its football section in February 1897, dissolved in 1945–46. [ citation needed ] Italy In Italy, Genoa C. F. C. is the oldest active football club: it was founded by Charles De Grave Sells, S. Green, George Blake, W. Rilley, George Dormer Fawcus, H. M. Sandys, E. De Thierry, Johnathan Summerhill Sr., Johnathan Summerhill Jr. and Sir Charles Alfred Payton in Genoa on 7 September 1893. However, Genoa C. F. C. was not the first Italian football club, being Torino Football & Cricket Club (1887) but its history lasted only for 4 years.\n\n[DOC 3] 2, and played there his entire career, where he won the reserve championship ( it ) 4–0 against Juventus in 1904. He studied engineering at the Polytechnic University of Milan from 1906 to 1911. Afterwards, he worked at the Officine Elettriche Genovesi (OEG) in San Fruttuoso , then at Pirelli in Milan . During the World War I , Ferraris served as a volunteer then reached the rank of lieutenant. On 23 August 1915, he died due to a 152mm shrapnel artillery shell which killed him instantly, during a mission in Val Posina, a minor valley of the Val d'Astico [ it ] at the municipality of Posina , and was buried by his comrades in arms at Monte Maggio [ it ] . In 1933, the stadium, Stadio Luigi Ferraris , was named after him. His Silver Medal of Military Valor was\n\n[DOC 4] Football stadium in Genoa, Italy 44°24′59′′N 8°57′9′′E  /  44.41639°N 8.95250°E  / 44.41639; 8.95250 Luigi Ferraris Marassi Interactive map of Luigi Ferraris Location Via Giovanni De Prà 1, Genoa , Italy Owner Municipality of Genoa Capacity 33,205 Surface Grass 105 × 68 meters Construction Opened 22 January 1911 Renovated 1987–1989, 2015, 2018 Architect Vittorio Gregotti (1987–1989) Tenants Genoa CFC (1911–present) UC Sampdoria (1946–present) Italy national football team (selected matches) The Stadio Luigi Ferraris , also known as the Marassi from the name of the neighbourhood where it is located, is a multi-use stadium in Genoa , Italy. The home of Genoa CFC and UC Sampdoria football clubs, it opened in 1911 and is the oldest stadium still in use for foo\n\n[DOC 5] (s) Guangxu ( 光緒 ) 25 February 1875 – 14 November 1908 Cousin of Tongzhi 14 August 1871 – 14 November 1908 (aged 37) Initiated the failed Hundred Days' Reform , and from 1898 on, his reign was completely dominated by Empress Dowager Cixi . Died of poisoning, possibly from Cixi. Xuantong 宣統 Other names Temple name : Gongzong ( 恭宗 ) Posthumous name (short) : Emperor Min ( 愍帝 ) Posthumous name (long) : Emperor Peitian Tongyun Fagu Shaotong Cuiwen Jingfu Kuanrui Zhengmu Tiren Lixiao Min ( 配天同運法古紹統粹文敬孚寬睿正穆體仁立孝愍皇帝 ) Puyi 溥儀 2 December 1908 – 12 February 1912 (3 years, 2 months and 10 days) Era(s) Xuantong ( 宣統 ) 2 December 1908 – 12 February 1912 Nephew of Guangxu 7 February 1906 – 17 October 1967 (aged 61) Reigned as a young child, but was forced to abdicate in 1\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide the name of the oldest extant Italian football team (Genoa CFC), its stadium (Stadio Luigi Ferraris), and the person the stadium is named after (Luigi Ferraris). However, they do not give Luigi Ferraris's birth year, which is required to determine the year when he was 5 years old and thus identify the Chinese emperor at that time. Without Ferraris's birth date, the answer cannot be derived.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1423, + "osl": 107, + "total_tokens": 1530, + "latency_ms": 5969.5, + "tokens_per_second": 17.92 + }, + "context": { + "kept_docs_count": 5, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "5df030c0-ef78-48d4-962c-9b7922b5dbcf", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:11:29.143375Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: The oldest extant football team in Italy plays in a stadium. The stadium is named after a person. Who was the emperor of China when that person was 5 years old?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Association football club in Italy Football club Genoa Full name Genoa Cricket and Football Club S. p. A. Nicknames Il Grifone (The Griffin ) I Rossoblù (The Red and Blues) Il Vecchio Balordo (The Old Fool) Founded 7 September 1893 ; 132 years ago ( 7 September 1893 ) Ground Stadio Luigi Ferraris Capacity 33,205 Owner Dan Șucu President Dan Șucu Head coach Daniele De Rossi League Serie A 2024–25 Serie A, 13th of 20 Website genoacfc . it Home colours Away colours Third colours Current season The performance of Genoa in the Italian football league structure since the first season of a unified Serie A (1929–30). Their Scudetti lie before this era. Genoa Cricket and Football Club ( Italian pronunciation: [ˈdʒeːnoa] ) is an Italian professional football club base\n\n[DOC 2] III. Kerületi TVE , which whose football section was founded officially in 1899 but stemmed from the 1897-founded Budai Football Csapat. The first ever football club to be founded in Hungary was Budapesti Torna Club having founded its football section in February 1897, dissolved in 1945–46. [ citation needed ] Italy In Italy, Genoa C. F. C. is the oldest active football club: it was founded by Charles De Grave Sells, S. Green, George Blake, W. Rilley, George Dormer Fawcus, H. M. Sandys, E. De Thierry, Johnathan Summerhill Sr., Johnathan Summerhill Jr. and Sir Charles Alfred Payton in Genoa on 7 September 1893. However, Genoa C. F. C. was not the first Italian football club, being Torino Football & Cricket Club (1887) but its history lasted only for 4 years.\n\n[DOC 3] 2, and played there his entire career, where he won the reserve championship ( it ) 4–0 against Juventus in 1904. He studied engineering at the Polytechnic University of Milan from 1906 to 1911. Afterwards, he worked at the Officine Elettriche Genovesi (OEG) in San Fruttuoso , then at Pirelli in Milan . During the World War I , Ferraris served as a volunteer then reached the rank of lieutenant. On 23 August 1915, he died due to a 152mm shrapnel artillery shell which killed him instantly, during a mission in Val Posina, a minor valley of the Val d'Astico [ it ] at the municipality of Posina , and was buried by his comrades in arms at Monte Maggio [ it ] . In 1933, the stadium, Stadio Luigi Ferraris , was named after him. His Silver Medal of Military Valor was\n\n[DOC 4] Football stadium in Genoa, Italy 44°24′59′′N 8°57′9′′E  /  44.41639°N 8.95250°E  / 44.41639; 8.95250 Luigi Ferraris Marassi Interactive map of Luigi Ferraris Location Via Giovanni De Prà 1, Genoa , Italy Owner Municipality of Genoa Capacity 33,205 Surface Grass 105 × 68 meters Construction Opened 22 January 1911 Renovated 1987–1989, 2015, 2018 Architect Vittorio Gregotti (1987–1989) Tenants Genoa CFC (1911–present) UC Sampdoria (1946–present) Italy national football team (selected matches) The Stadio Luigi Ferraris , also known as the Marassi from the name of the neighbourhood where it is located, is a multi-use stadium in Genoa , Italy. The home of Genoa CFC and UC Sampdoria football clubs, it opened in 1911 and is the oldest stadium still in use for foo\n\n[DOC 5] (s) Guangxu ( 光緒 ) 25 February 1875 – 14 November 1908 Cousin of Tongzhi 14 August 1871 – 14 November 1908 (aged 37) Initiated the failed Hundred Days' Reform , and from 1898 on, his reign was completely dominated by Empress Dowager Cixi . Died of poisoning, possibly from Cixi. Xuantong 宣統 Other names Temple name : Gongzong ( 恭宗 ) Posthumous name (short) : Emperor Min ( 愍帝 ) Posthumous name (long) : Emperor Peitian Tongyun Fagu Shaotong Cuiwen Jingfu Kuanrui Zhengmu Tiren Lixiao Min ( 配天同運法古紹統粹文敬孚寬睿正穆體仁立孝愍皇帝 ) Puyi 溥儀 2 December 1908 – 12 February 1912 (3 years, 2 months and 10 days) Era(s) Xuantong ( 宣統 ) 2 December 1908 – 12 February 1912 Nephew of Guangxu 7 February 1906 – 17 October 1967 (aged 61) Reigned as a young child, but was forced to abdicate in 1\n\n\nSEARCH HISTORY: - Query: 'oldest football club Italy' → 2 docs\n- Query: 'Stadio Luigi Ferraris named after' → 2 docs\n- Query: 'Luigi Ferraris birth year' → 1 docs\n- Query: 'Luigi Ferraris birth year' → 0 docs\n- Query: 'Guangxu Emperor reign' → 1 docs\n- Query: 'Chinese emperor 1892' → 0 docs\n- Query: 'Luigi Ferraris (footballer) Wikipedia' → 0 docs\n- Query: 'Luigi Ferraris born' → 0 docs\n- Query: 'China emperor 1892' → 0 docs\n\nFEEDBACK: Need to identify Italy's oldest extant football team, the stadium it uses, and the person the stadium is named for. These queries retrieve the club, the stadium's namesake, and that person's birth year, enabling determination of the Chinese emperor reigning when the namesake was five.\nWe have identified the oldest extant Italian football club (Genoa CFC) and its stadium (Stadio Luigi Ferraris) named after Luigi Ferraris. Missing information: Ferraris' birth year to calculate his age 5, and the Chinese emperor ruling in that year. The above queries aim to retrieve Ferraris' birth year and the relevant Chinese emperor.\nWe have identified the oldest extant Italian football club (Genoa CFC) and its stadium (Stadio Luigi Ferraris) named after Luigi Ferraris. Missing information: Ferraris' birth year to determine his age‑5 year, and which Chinese emperor ruled in that year. The first two queries aim to retrieve Ferraris' birth date; the third query seeks the Chinese emperor in 1892 (the likely age‑5 year).\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Luigi Ferraris\",\n \"Luigi Ferraris birth\",\n \"Chinese emperor 1892\"\n ],\n \"feedback\": \"We need Luigi Ferraris' birth year to compute the year when he was 5, then identify the Chinese emperor ruling that year. The first two queries target Ferraris' biography and birth date; the third query seeks the emperor in the resulting year.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1949, + "osl": 516, + "total_tokens": 2465, + "latency_ms": 3276.12, + "tokens_per_second": 157.5 + }, + "context": { + "kept_docs_count": 5, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "1ee18962-275e-4a8c-a682-e26d52b835eb", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T03:11:34.546702Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: The oldest extant football team in Italy plays in a stadium. The stadium is named after a person. Who was the emperor of China when that person was 5 years old?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] t GOO 1952 Scuderia Ferrari Ferrari 500 Ferrari 500 2.0 L4 SYR 1 PAU 1 IBS MAR 1 AST INT ELÄ NAP EIF PAR ALB FRO ULS MNZ Ret LAC ESS MAR 3* SAB Ret CAE DMT COM 1† NAT BAU 1 MOD 3‡ CAD SKA MAD AVU JOE NEW Ferrari 375 Ferrari 375 4.5 V12 VAL 5 RIC LAV 1953 Scuderia Ferrari Ferrari 500 Ferrari 500 2.0 L4 SYR Ret PAU 1 LAV AST BOR 1 INT ELÄ NAP 5 ULS WIN FRO COR EIF Ferrari 375 Ferrari 375 4.5 V12 ALB DNQ PRI ESS MID ROU CRY AVU USF LAC BRI CHE SAB NEW CAD RED SKA LON MOD MAD JOE CUR 1955 Scuderia Lancia Lancia D50 Lancia DS50 2.5 V8 VAL 1 PAU 5 GLO BOR INT NAP 1 ALB CUR COR LON DRT RED DTT OUL AVO SYR Source: * Indicates shared drive with Luigi Villoresi † Indicates shared drive with André Simon ‡ Indicates shared drive with Sergio Sighinolfi Complete 24 Hours\n\n\nKEPT DOCUMENTS (context only):\n[5 documents already kept as relevant]\n[KEPT 1] Association football club in Italy Football club Genoa Full name Genoa Cricket and Football Club S. p. A. Nicknames Il Grifone (The Griffin ) I Rossoblù (The Red and Blues) Il Vecchio Balordo (The Old Fool) Founded 7 September 1893 ; 132 years ago ( 7 September 1893 ) Ground Stadio Luigi Ferraris Ca...\n[KEPT 2] III. Kerületi TVE , which whose football section was founded officially in 1899 but stemmed from the 1897-founded Budai Football Csapat. The first ever football club to be founded in Hungary was Budapesti Torna Club having founded its football section in February 1897, dissolved in 1945–46. [ citati...\n[KEPT 3] 2, and played there his entire career, where he won the reserve championship ( it ) 4–0 against Juventus in 1904. He studied engineering at the Polytechnic University of Milan from 1906 to 1911. Afterwards, he worked at the Officine Elettriche Genovesi (OEG) in San Fruttuoso , then at Pirelli in Mil...\n[KEPT 4] Football stadium in Genoa, Italy 44°24′59′′N 8°57′9′′E  /  44.41639°N 8.95250°E  / 44.41639; 8.95250 Luigi Ferraris Marassi Interactive map of Luigi Ferraris Location Via Giovanni De Prà 1, Genoa , Italy Owner Municipality of Genoa Capacity 33,205 Surface Grass 105 × 68 meters Construction Opened...\n[KEPT 5] (s) Guangxu ( 光緒 ) 25 February 1875 – 14 November 1908 Cousin of Tongzhi 14 August 1871 – 14 November 1908 (aged 37) Initiated the failed Hundred Days' Reform , and from 1898 on, his reign was completely dominated by Empress Dowager Cixi . Died of poisoning, possibly from Cixi. Xuantong 宣統 Other nam...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1014, + "osl": 285, + "total_tokens": 1299, + "latency_ms": 4814.97, + "tokens_per_second": 59.19 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 5 + } + }, + { + "call_id": "ef449427-071c-4566-b8e9-c6aef4acf872", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:11:39.232372Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: The oldest extant football team in Italy plays in a stadium. The stadium is named after a person. Who was the emperor of China when that person was 5 years old?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Association football club in Italy Football club Genoa Full name Genoa Cricket and Football Club S. p. A. Nicknames Il Grifone (The Griffin ) I Rossoblù (The Red and Blues) Il Vecchio Balordo (The Old Fool) Founded 7 September 1893 ; 132 years ago ( 7 September 1893 ) Ground Stadio Luigi Ferraris Capacity 33,205 Owner Dan Șucu President Dan Șucu Head coach Daniele De Rossi League Serie A 2024–25 Serie A, 13th of 20 Website genoacfc . it Home colours Away colours Third colours Current season The performance of Genoa in the Italian football league structure since the first season of a unified Serie A (1929–30). Their Scudetti lie before this era. Genoa Cricket and Football Club ( Italian pronunciation: [ˈdʒeːnoa] ) is an Italian professional football club base\n\n[DOC 2] III. Kerületi TVE , which whose football section was founded officially in 1899 but stemmed from the 1897-founded Budai Football Csapat. The first ever football club to be founded in Hungary was Budapesti Torna Club having founded its football section in February 1897, dissolved in 1945–46. [ citation needed ] Italy In Italy, Genoa C. F. C. is the oldest active football club: it was founded by Charles De Grave Sells, S. Green, George Blake, W. Rilley, George Dormer Fawcus, H. M. Sandys, E. De Thierry, Johnathan Summerhill Sr., Johnathan Summerhill Jr. and Sir Charles Alfred Payton in Genoa on 7 September 1893. However, Genoa C. F. C. was not the first Italian football club, being Torino Football & Cricket Club (1887) but its history lasted only for 4 years.\n\n[DOC 3] 2, and played there his entire career, where he won the reserve championship ( it ) 4–0 against Juventus in 1904. He studied engineering at the Polytechnic University of Milan from 1906 to 1911. Afterwards, he worked at the Officine Elettriche Genovesi (OEG) in San Fruttuoso , then at Pirelli in Milan . During the World War I , Ferraris served as a volunteer then reached the rank of lieutenant. On 23 August 1915, he died due to a 152mm shrapnel artillery shell which killed him instantly, during a mission in Val Posina, a minor valley of the Val d'Astico [ it ] at the municipality of Posina , and was buried by his comrades in arms at Monte Maggio [ it ] . In 1933, the stadium, Stadio Luigi Ferraris , was named after him. His Silver Medal of Military Valor was\n\n[DOC 4] Football stadium in Genoa, Italy 44°24′59′′N 8°57′9′′E  /  44.41639°N 8.95250°E  / 44.41639; 8.95250 Luigi Ferraris Marassi Interactive map of Luigi Ferraris Location Via Giovanni De Prà 1, Genoa , Italy Owner Municipality of Genoa Capacity 33,205 Surface Grass 105 × 68 meters Construction Opened 22 January 1911 Renovated 1987–1989, 2015, 2018 Architect Vittorio Gregotti (1987–1989) Tenants Genoa CFC (1911–present) UC Sampdoria (1946–present) Italy national football team (selected matches) The Stadio Luigi Ferraris , also known as the Marassi from the name of the neighbourhood where it is located, is a multi-use stadium in Genoa , Italy. The home of Genoa CFC and UC Sampdoria football clubs, it opened in 1911 and is the oldest stadium still in use for foo\n\n[DOC 5] (s) Guangxu ( 光緒 ) 25 February 1875 – 14 November 1908 Cousin of Tongzhi 14 August 1871 – 14 November 1908 (aged 37) Initiated the failed Hundred Days' Reform , and from 1898 on, his reign was completely dominated by Empress Dowager Cixi . Died of poisoning, possibly from Cixi. Xuantong 宣統 Other names Temple name : Gongzong ( 恭宗 ) Posthumous name (short) : Emperor Min ( 愍帝 ) Posthumous name (long) : Emperor Peitian Tongyun Fagu Shaotong Cuiwen Jingfu Kuanrui Zhengmu Tiren Lixiao Min ( 配天同運法古紹統粹文敬孚寬睿正穆體仁立孝愍皇帝 ) Puyi 溥儀 2 December 1908 – 12 February 1912 (3 years, 2 months and 10 days) Era(s) Xuantong ( 宣統 ) 2 December 1908 – 12 February 1912 Nephew of Guangxu 7 February 1906 – 17 October 1967 (aged 61) Reigned as a young child, but was forced to abdicate in 1\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents identify the oldest extant Italian football team (Genoa CFC) and its stadium (Stadio Luigi Ferraris) named after Luigi Ferraris. However, they do not provide Luigi Ferraris's birth date, so we cannot determine the year when he was 5 years old, nor can we identify the Chinese emperor reigning at that time.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1434, + "osl": 405, + "total_tokens": 1839, + "latency_ms": 4684.83, + "tokens_per_second": 86.45 + }, + "context": { + "kept_docs_count": 5, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "73a1daf6-7407-49d9-b01e-c8257febc1ff", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:11:43.724825Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: The oldest extant football team in Italy plays in a stadium. The stadium is named after a person. Who was the emperor of China when that person was 5 years old?\n\nDOCUMENTS:\n\n[DOC 1] Association football club in Italy Football club Genoa Full name Genoa Cricket and Football Club S. p. A. Nicknames Il Grifone (The Griffin ) I Rossoblù (The Red and Blues) Il Vecchio Balordo (The Old Fool) Founded 7 September 1893 ; 132 years ago ( 7 September 1893 ) Ground Stadio Luigi Ferraris Capacity 33,205 Owner Dan Șucu President Dan Șucu Head coach Daniele De Rossi League Serie A 2024–25 Serie A, 13th of 20 Website genoacfc . it Home colours Away colours Third colours Current season The performance of Genoa in the Italian football league structure since the first season of a unified Serie A (1929–30). Their Scudetti lie before this era. Genoa Cricket and Football Club ( Italian pronunciation: [ˈdʒeːnoa] ) is an Italian professional football club base\n\n[DOC 2] III. Kerületi TVE , which whose football section was founded officially in 1899 but stemmed from the 1897-founded Budai Football Csapat. The first ever football club to be founded in Hungary was Budapesti Torna Club having founded its football section in February 1897, dissolved in 1945–46. [ citation needed ] Italy In Italy, Genoa C. F. C. is the oldest active football club: it was founded by Charles De Grave Sells, S. Green, George Blake, W. Rilley, George Dormer Fawcus, H. M. Sandys, E. De Thierry, Johnathan Summerhill Sr., Johnathan Summerhill Jr. and Sir Charles Alfred Payton in Genoa on 7 September 1893. However, Genoa C. F. C. was not the first Italian football club, being Torino Football & Cricket Club (1887) but its history lasted only for 4 years.\n\n[DOC 3] 2, and played there his entire career, where he won the reserve championship ( it ) 4–0 against Juventus in 1904. He studied engineering at the Polytechnic University of Milan from 1906 to 1911. Afterwards, he worked at the Officine Elettriche Genovesi (OEG) in San Fruttuoso , then at Pirelli in Milan . During the World War I , Ferraris served as a volunteer then reached the rank of lieutenant. On 23 August 1915, he died due to a 152mm shrapnel artillery shell which killed him instantly, during a mission in Val Posina, a minor valley of the Val d'Astico [ it ] at the municipality of Posina , and was buried by his comrades in arms at Monte Maggio [ it ] . In 1933, the stadium, Stadio Luigi Ferraris , was named after him. His Silver Medal of Military Valor was\n\n[DOC 4] Football stadium in Genoa, Italy 44°24′59′′N 8°57′9′′E  /  44.41639°N 8.95250°E  / 44.41639; 8.95250 Luigi Ferraris Marassi Interactive map of Luigi Ferraris Location Via Giovanni De Prà 1, Genoa , Italy Owner Municipality of Genoa Capacity 33,205 Surface Grass 105 × 68 meters Construction Opened 22 January 1911 Renovated 1987–1989, 2015, 2018 Architect Vittorio Gregotti (1987–1989) Tenants Genoa CFC (1911–present) UC Sampdoria (1946–present) Italy national football team (selected matches) The Stadio Luigi Ferraris , also known as the Marassi from the name of the neighbourhood where it is located, is a multi-use stadium in Genoa , Italy. The home of Genoa CFC and UC Sampdoria football clubs, it opened in 1911 and is the oldest stadium still in use for foo\n\n[DOC 5] (s) Guangxu ( 光緒 ) 25 February 1875 – 14 November 1908 Cousin of Tongzhi 14 August 1871 – 14 November 1908 (aged 37) Initiated the failed Hundred Days' Reform , and from 1898 on, his reign was completely dominated by Empress Dowager Cixi . Died of poisoning, possibly from Cixi. Xuantong 宣統 Other names Temple name : Gongzong ( 恭宗 ) Posthumous name (short) : Emperor Min ( 愍帝 ) Posthumous name (long) : Emperor Peitian Tongyun Fagu Shaotong Cuiwen Jingfu Kuanrui Zhengmu Tiren Lixiao Min ( 配天同運法古紹統粹文敬孚寬睿正穆體仁立孝愍皇帝 ) Puyi 溥儀 2 December 1908 – 12 February 1912 (3 years, 2 months and 10 days) Era(s) Xuantong ( 宣統 ) 2 December 1908 – 12 February 1912 Nephew of Guangxu 7 February 1906 – 17 October 1967 (aged 61) Reigned as a young child, but was forced to abdicate in 1\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Guangxu Emperor.", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1292, + "osl": 325, + "total_tokens": 1617, + "latency_ms": 4491.78, + "tokens_per_second": 72.35 + }, + "context": { + "num_documents_used": 5, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:11:43.725653Z", + "summary": { + "total_llm_calls": 12, + "total_hops": 5, + "total_input_tokens": 15594, + "total_output_tokens": 4306, + "total_tokens": 19900, + "total_latency_ms": 70246.52, + "average_tokens_per_second": 96.81, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Oldest_football_clubs", + "https://en.wikipedia.org/wiki/Genoa_CFC", + "https://en.wikipedia.org/wiki/Stadio_Luigi_Ferraris", + "https://en.wikipedia.org/wiki/Luigi_Ferraris_(footballer)", + "https://en.wikipedia.org/wiki/List_of_Chinese_monarchs" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Guangxu Emperor.", + "ground_truth_answer": "Guangxu" + } + }, + { + "query_id": "35", + "original_query": "Of the four main characters on Seinfeld, which actor is the oldest?", + "timestamp_start": "2026-05-16T03:11:43.755380Z", + "llm_calls": [ + { + "call_id": "1ddea114-539f-4913-8a6b-9da15417a7bf", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:11:48.317022Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: Of the four main characters on Seinfeld, which actor is the oldest?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Seinfeld cast birthdays\",\n \"Jerry Seinfeld birthdate\",\n \"Jason Alexander birthdate\"\n ],\n \"feedback\": \"First query aims to retrieve a list with all four main actors and their birth dates. If incomplete, the second and third queries target the two actors not yet covered.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 461, + "osl": 425, + "total_tokens": 886, + "latency_ms": 4561.51, + "tokens_per_second": 93.17 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "14544c6e-05f6-4e57-98d0-6ec3dfc20cd7", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:11:56.917997Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: Of the four main characters on Seinfeld, which actor is the oldest?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] American comedian and actor (born 1954) Jerry Seinfeld Seinfeld in 2023 Born Jerome Allen Seinfeld ( 1954-04-29 ) April 29, 1954 (age 71) Brooklyn, New York City, U. S. Education Queens College ( BA ) Spouse Jessica Sklar ( m. 1999) Children 3 Comedy career Years active 1976–present Medium Stand-up television film Genres Observational comedy clean comedy cringe comedy surreal humor deadpan satire Subjects American culture everyday life gender differences human behavior social awkwardness pop culture current events Website jerryseinfeld . com Signature Jerome Allen Seinfeld ( / ˈ s aɪ n f ɛ l d / SYNE -feld ; born April 29, 1954) is an American stand-up comedian , actor, writer, filmmaker, and television producer specializing in observational comedy . Seinfel\n\n[NEW 2] American television sitcom (1989–1998) Seinfeld Genre Sitcom Slice of life Dark comedy Postmodernism Created by Larry David Jerry Seinfeld Showrunners Larry David (seasons 1–7) Jerry Seinfeld (season 8–9) Directed by Tom Cherones (seasons 1–5) Andy Ackerman (seasons 6–9) Various (seasons 1, 3, 6 & 8) Starring Jerry Seinfeld Julia Louis-Dreyfus Michael Richards Jason Alexander Theme music composer Jonathan Wolff Composer Jonathan Wolff Country of origin United States Original language English No. of seasons 9 No. of episodes 180 ( list of episodes ) Production Executive producers Larry David (1990–1996) George Shapiro Howard West Andrew Scheinman (1991–1993) Jerry Seinfeld (1996–1998) Alec Berg (1997–1998) Jeff Schaffer (1997–1998) Camera setup Multi-camera R\n\n[NEW 3] American actor (born 1959) Jason Alexander Alexander in 2009 Born Jay Scott Greenspan ( 1959-09-23 ) September 23, 1959 (age 66) Newark, New Jersey , U. S. Occupations Actor comedian director podcaster singer presenter Years active 1981–present Spouse Daena Title ( m. 1982) Children 2 Alexander's speaking voice Celebrating the retirement of Cantor Jack of Parsippany-Troy Hills, New Jersey's Adath Shalom. Jay Scott Greenspan (born September 23, 1959), known professionally as Jason Alexander , is an American actor and comedian. Over the course of his career, he has received an Emmy Award and a Tony Award as well as nominations for four Golden Globe Awards . He gained stardom for his role as George Costanza in the NBC sitcom Seinfeld (1989–1998), for which he w\n\n[NEW 4] American actor and comedian (born 1949) Michael Richards Richards in 1993 Born Michael Anthony Richards ( 1949-07-24 ) July 24, 1949 (age 76) Culver City, California , U. S. Alma mater The Evergreen State College (BA) Occupations Actor comedian Years active 1979–present Spouses Cathleen Lyons ( m. 1974; div. 1993) Beth Skipp ( m. 2010) Children 2 Military career Allegiance United States Branch U. S. Army Service years 1970–1972 Michael Anthony Richards (born July 24, 1949) is an American actor and comedian. He achieved global recognition for starring as Cosmo Kramer on the NBC television sitcom Seinfeld from 1989 to 1998. He began his career as a stand-up comedian, first entering the national spotlight when he was featured on Billy Crystal 's first cable TV\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 4 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 1, 1, 1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1048, + "osl": 245, + "total_tokens": 1293, + "latency_ms": 8067.14, + "tokens_per_second": 30.37 + }, + "context": { + "num_documents_evaluated": 4, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "52c5f92e-cab4-4399-b082-8aea11b14830", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:11:59.202505Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: Of the four main characters on Seinfeld, which actor is the oldest?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American comedian and actor (born 1954) Jerry Seinfeld Seinfeld in 2023 Born Jerome Allen Seinfeld ( 1954-04-29 ) April 29, 1954 (age 71) Brooklyn, New York City, U. S. Education Queens College ( BA ) Spouse Jessica Sklar ( m. 1999) Children 3 Comedy career Years active 1976–present Medium Stand-up television film Genres Observational comedy clean comedy cringe comedy surreal humor deadpan satire Subjects American culture everyday life gender differences human behavior social awkwardness pop culture current events Website jerryseinfeld . com Signature Jerome Allen Seinfeld ( / ˈ s aɪ n f ɛ l d / SYNE -feld ; born April 29, 1954) is an American stand-up comedian , actor, writer, filmmaker, and television producer specializing in observational comedy . Seinfel\n\n[DOC 2] American television sitcom (1989–1998) Seinfeld Genre Sitcom Slice of life Dark comedy Postmodernism Created by Larry David Jerry Seinfeld Showrunners Larry David (seasons 1–7) Jerry Seinfeld (season 8–9) Directed by Tom Cherones (seasons 1–5) Andy Ackerman (seasons 6–9) Various (seasons 1, 3, 6 & 8) Starring Jerry Seinfeld Julia Louis-Dreyfus Michael Richards Jason Alexander Theme music composer Jonathan Wolff Composer Jonathan Wolff Country of origin United States Original language English No. of seasons 9 No. of episodes 180 ( list of episodes ) Production Executive producers Larry David (1990–1996) George Shapiro Howard West Andrew Scheinman (1991–1993) Jerry Seinfeld (1996–1998) Alec Berg (1997–1998) Jeff Schaffer (1997–1998) Camera setup Multi-camera R\n\n[DOC 3] American actor (born 1959) Jason Alexander Alexander in 2009 Born Jay Scott Greenspan ( 1959-09-23 ) September 23, 1959 (age 66) Newark, New Jersey , U. S. Occupations Actor comedian director podcaster singer presenter Years active 1981–present Spouse Daena Title ( m. 1982) Children 2 Alexander's speaking voice Celebrating the retirement of Cantor Jack of Parsippany-Troy Hills, New Jersey's Adath Shalom. Jay Scott Greenspan (born September 23, 1959), known professionally as Jason Alexander , is an American actor and comedian. Over the course of his career, he has received an Emmy Award and a Tony Award as well as nominations for four Golden Globe Awards . He gained stardom for his role as George Costanza in the NBC sitcom Seinfeld (1989–1998), for which he w\n\n[DOC 4] American actor and comedian (born 1949) Michael Richards Richards in 1993 Born Michael Anthony Richards ( 1949-07-24 ) July 24, 1949 (age 76) Culver City, California , U. S. Alma mater The Evergreen State College (BA) Occupations Actor comedian Years active 1979–present Spouses Cathleen Lyons ( m. 1974; div. 1993) Beth Skipp ( m. 2010) Children 2 Military career Allegiance United States Branch U. S. Army Service years 1970–1972 Michael Anthony Richards (born July 24, 1949) is an American actor and comedian. He achieved global recognition for starring as Cosmo Kramer on the NBC television sitcom Seinfeld from 1989 to 1998. He began his career as a stand-up comedian, first entering the national spotlight when he was featured on Billy Crystal 's first cable TV\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide birth years for three of the four main Seinfeld characters: Jerry Seinfeld (1954), Michael Richards (1949), and Jason Alexander (1959). However, the birth year for Julia Louis‑Dreyfus (the fourth main character) is not present in any kept document, so we cannot determine which actor is the oldest among all four.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1030, + "osl": 90, + "total_tokens": 1120, + "latency_ms": 2283.58, + "tokens_per_second": 39.41 + }, + "context": { + "kept_docs_count": 4, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "35fa5e6b-25e4-4168-b9f3-2f87c79b8cf3", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:12:03.825453Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: Of the four main characters on Seinfeld, which actor is the oldest?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American comedian and actor (born 1954) Jerry Seinfeld Seinfeld in 2023 Born Jerome Allen Seinfeld ( 1954-04-29 ) April 29, 1954 (age 71) Brooklyn, New York City, U. S. Education Queens College ( BA ) Spouse Jessica Sklar ( m. 1999) Children 3 Comedy career Years active 1976–present Medium Stand-up television film Genres Observational comedy clean comedy cringe comedy surreal humor deadpan satire Subjects American culture everyday life gender differences human behavior social awkwardness pop culture current events Website jerryseinfeld . com Signature Jerome Allen Seinfeld ( / ˈ s aɪ n f ɛ l d / SYNE -feld ; born April 29, 1954) is an American stand-up comedian , actor, writer, filmmaker, and television producer specializing in observational comedy . Seinfel\n\n[DOC 2] American television sitcom (1989–1998) Seinfeld Genre Sitcom Slice of life Dark comedy Postmodernism Created by Larry David Jerry Seinfeld Showrunners Larry David (seasons 1–7) Jerry Seinfeld (season 8–9) Directed by Tom Cherones (seasons 1–5) Andy Ackerman (seasons 6–9) Various (seasons 1, 3, 6 & 8) Starring Jerry Seinfeld Julia Louis-Dreyfus Michael Richards Jason Alexander Theme music composer Jonathan Wolff Composer Jonathan Wolff Country of origin United States Original language English No. of seasons 9 No. of episodes 180 ( list of episodes ) Production Executive producers Larry David (1990–1996) George Shapiro Howard West Andrew Scheinman (1991–1993) Jerry Seinfeld (1996–1998) Alec Berg (1997–1998) Jeff Schaffer (1997–1998) Camera setup Multi-camera R\n\n[DOC 3] American actor (born 1959) Jason Alexander Alexander in 2009 Born Jay Scott Greenspan ( 1959-09-23 ) September 23, 1959 (age 66) Newark, New Jersey , U. S. Occupations Actor comedian director podcaster singer presenter Years active 1981–present Spouse Daena Title ( m. 1982) Children 2 Alexander's speaking voice Celebrating the retirement of Cantor Jack of Parsippany-Troy Hills, New Jersey's Adath Shalom. Jay Scott Greenspan (born September 23, 1959), known professionally as Jason Alexander , is an American actor and comedian. Over the course of his career, he has received an Emmy Award and a Tony Award as well as nominations for four Golden Globe Awards . He gained stardom for his role as George Costanza in the NBC sitcom Seinfeld (1989–1998), for which he w\n\n[DOC 4] American actor and comedian (born 1949) Michael Richards Richards in 1993 Born Michael Anthony Richards ( 1949-07-24 ) July 24, 1949 (age 76) Culver City, California , U. S. Alma mater The Evergreen State College (BA) Occupations Actor comedian Years active 1979–present Spouses Cathleen Lyons ( m. 1974; div. 1993) Beth Skipp ( m. 2010) Children 2 Military career Allegiance United States Branch U. S. Army Service years 1970–1972 Michael Anthony Richards (born July 24, 1949) is an American actor and comedian. He achieved global recognition for starring as Cosmo Kramer on the NBC television sitcom Seinfeld from 1989 to 1998. He began his career as a stand-up comedian, first entering the national spotlight when he was featured on Billy Crystal 's first cable TV\n\n\nSEARCH HISTORY: - Query: 'Seinfeld cast birthdays' → 4 docs\n- Query: 'Jerry Seinfeld birthdate' → 0 docs\n- Query: 'Jason Alexander birthdate' → 0 docs\n\nFEEDBACK: First query aims to retrieve a list with all four main actors and their birth dates. If incomplete, the second and third queries target the two actors not yet covered.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Julia Louis-Dreyfus\",\n \"Julia Louis-Dreyfus birthdate\",\n \"Seinfeld main cast ages\"\n ],\n \"feedback\": \"We have birth dates for Jerry Seinfeld (1954), Jason Alexander (1959), and Michael Richards (1949). We still need Julia Louis‑Dreyfus' birth date to compare all four and identify the oldest.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1313, + "osl": 330, + "total_tokens": 1643, + "latency_ms": 4622.25, + "tokens_per_second": 71.39 + }, + "context": { + "kept_docs_count": 4, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "9a1d31ca-a76a-4f75-b14e-0f226a4d96d7", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T03:12:18.733176Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: Of the four main characters on Seinfeld, which actor is the oldest?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] American actress and comedian (born 1961) Julia Louis-Dreyfus Louis-Dreyfus in 2019 Born ( 1961-01-13 ) January 13, 1961 (age 65) New York City , U. S. Occupations Actor comedian producer Years active 1980–present Spouse Brad Hall ( m. 1987) Children 2, including Charlie Hall Father Gérard Louis-Dreyfus Relatives Lauren Bowles (half-sister) Patrick Fischler (brother-in-law) Robert Louis-Dreyfus (second cousin once removed) Kyril Louis-Dreyfus (third cousin) Pierre Louis-Dreyfus (grandfather) Charles Louis-Dreyfus (great-grandfather) Léopold Louis-Dreyfus (great-great-grandfather) Julia Scarlett Elizabeth Louis-Dreyfus ( / ˌ l uː i ˈ d r aɪ f ə s / LOO -ee DRY -fəs ; born January 13, 1961) is an American actress, comedian, and producer. She is known for her r\n\n[NEW 2] ve-action sitcom in terms of total seasons, surpassing The Adventures of Ozzie and Harriet ' s 14 seasons with the release of its 15th season in December 2021. In December 2020, the series was renewed for a total of four additional seasons, bringing it to a total of 18 seasons. The seventeenth and most recent season premiered on July 9, 2025, and ended on August 20, 2025, with an eighteenth season in development. The series has received critical acclaim, being variously described as \"Seinfeld on crack\" and \"white trash comedy\", with many critics lauding the show's dark humor and satire and the cast's performances. It has a large cult following . Synopsis Premise The series follows a group of self-centered, heavy-drinking, narcissistic misfits, referred to as\n\n[NEW 3] ( Jeff Garlin ), and Jeff's wife Susie ( Susie Essman ). Celebrities, including comedians Richard Lewis , Wanda Sykes , and Bob Einstein , appeared on the show regularly. Actors Ted Danson and Mary Steenburgen have had recurring roles as themselves. The show is critically acclaimed and has been nominated for 30 Primetime Emmy Awards, with one win, as well as a Golden Globe win. In the first six seasons, Julia Louis-Dreyfus and Jason Alexander appear in several episodes, and Jerry Seinfeld has a cameo. In season 7, the cast of Seinfeld , including Michael Richards , return in a story arc involving David's attempt to organize a Seinfeld reunion special. On June 2, 2010, the series premiered on the TV Guide Network , its network television debut. TV Guide Netwo\n\n\nKEPT DOCUMENTS (context only):\n[4 documents already kept as relevant]\n[KEPT 1] American comedian and actor (born 1954) Jerry Seinfeld Seinfeld in 2023 Born Jerome Allen Seinfeld ( 1954-04-29 ) April 29, 1954 (age 71) Brooklyn, New York City, U. S. Education Queens College ( BA ) Spouse Jessica Sklar ( m. 1999) Children 3 Comedy career Years active 1976–present Medium Stand-up ...\n[KEPT 2] American television sitcom (1989–1998) Seinfeld Genre Sitcom Slice of life Dark comedy Postmodernism Created by Larry David Jerry Seinfeld Showrunners Larry David (seasons 1–7) Jerry Seinfeld (season 8–9) Directed by Tom Cherones (seasons 1–5) Andy Ackerman (seasons 6–9) Various (seasons 1, 3, 6 & 8...\n[KEPT 3] American actor (born 1959) Jason Alexander Alexander in 2009 Born Jay Scott Greenspan ( 1959-09-23 ) September 23, 1959 (age 66) Newark, New Jersey , U. S. Occupations Actor comedian director podcaster singer presenter Years active 1981–present Spouse Daena Title ( m. 1982) Children 2 Alexander's sp...\n[KEPT 4] American actor and comedian (born 1949) Michael Richards Richards in 1993 Born Michael Anthony Richards ( 1949-07-24 ) July 24, 1949 (age 76) Culver City, California , U. S. Alma mater The Evergreen State College (BA) Occupations Actor comedian Years active 1979–present Spouses Cathleen Lyons ( m. 1...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 3 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 1, 1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1222, + "osl": 792, + "total_tokens": 2014, + "latency_ms": 14349.22, + "tokens_per_second": 55.19 + }, + "context": { + "num_documents_evaluated": 3, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 4 + } + }, + { + "call_id": "c2868ff4-fe98-4fe4-81cc-5afbf0627169", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:12:21.202851Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: Of the four main characters on Seinfeld, which actor is the oldest?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American comedian and actor (born 1954) Jerry Seinfeld Seinfeld in 2023 Born Jerome Allen Seinfeld ( 1954-04-29 ) April 29, 1954 (age 71) Brooklyn, New York City, U. S. Education Queens College ( BA ) Spouse Jessica Sklar ( m. 1999) Children 3 Comedy career Years active 1976–present Medium Stand-up television film Genres Observational comedy clean comedy cringe comedy surreal humor deadpan satire Subjects American culture everyday life gender differences human behavior social awkwardness pop culture current events Website jerryseinfeld . com Signature Jerome Allen Seinfeld ( / ˈ s aɪ n f ɛ l d / SYNE -feld ; born April 29, 1954) is an American stand-up comedian , actor, writer, filmmaker, and television producer specializing in observational comedy . Seinfel\n\n[DOC 2] American television sitcom (1989–1998) Seinfeld Genre Sitcom Slice of life Dark comedy Postmodernism Created by Larry David Jerry Seinfeld Showrunners Larry David (seasons 1–7) Jerry Seinfeld (season 8–9) Directed by Tom Cherones (seasons 1–5) Andy Ackerman (seasons 6–9) Various (seasons 1, 3, 6 & 8) Starring Jerry Seinfeld Julia Louis-Dreyfus Michael Richards Jason Alexander Theme music composer Jonathan Wolff Composer Jonathan Wolff Country of origin United States Original language English No. of seasons 9 No. of episodes 180 ( list of episodes ) Production Executive producers Larry David (1990–1996) George Shapiro Howard West Andrew Scheinman (1991–1993) Jerry Seinfeld (1996–1998) Alec Berg (1997–1998) Jeff Schaffer (1997–1998) Camera setup Multi-camera R\n\n[DOC 3] American actor (born 1959) Jason Alexander Alexander in 2009 Born Jay Scott Greenspan ( 1959-09-23 ) September 23, 1959 (age 66) Newark, New Jersey , U. S. Occupations Actor comedian director podcaster singer presenter Years active 1981–present Spouse Daena Title ( m. 1982) Children 2 Alexander's speaking voice Celebrating the retirement of Cantor Jack of Parsippany-Troy Hills, New Jersey's Adath Shalom. Jay Scott Greenspan (born September 23, 1959), known professionally as Jason Alexander , is an American actor and comedian. Over the course of his career, he has received an Emmy Award and a Tony Award as well as nominations for four Golden Globe Awards . He gained stardom for his role as George Costanza in the NBC sitcom Seinfeld (1989–1998), for which he w\n\n[DOC 4] American actor and comedian (born 1949) Michael Richards Richards in 1993 Born Michael Anthony Richards ( 1949-07-24 ) July 24, 1949 (age 76) Culver City, California , U. S. Alma mater The Evergreen State College (BA) Occupations Actor comedian Years active 1979–present Spouses Cathleen Lyons ( m. 1974; div. 1993) Beth Skipp ( m. 2010) Children 2 Military career Allegiance United States Branch U. S. Army Service years 1970–1972 Michael Anthony Richards (born July 24, 1949) is an American actor and comedian. He achieved global recognition for starring as Cosmo Kramer on the NBC television sitcom Seinfeld from 1989 to 1998. He began his career as a stand-up comedian, first entering the national spotlight when he was featured on Billy Crystal 's first cable TV\n\n[DOC 5] American actress and comedian (born 1961) Julia Louis-Dreyfus Louis-Dreyfus in 2019 Born ( 1961-01-13 ) January 13, 1961 (age 65) New York City , U. S. Occupations Actor comedian producer Years active 1980–present Spouse Brad Hall ( m. 1987) Children 2, including Charlie Hall Father Gérard Louis-Dreyfus Relatives Lauren Bowles (half-sister) Patrick Fischler (brother-in-law) Robert Louis-Dreyfus (second cousin once removed) Kyril Louis-Dreyfus (third cousin) Pierre Louis-Dreyfus (grandfather) Charles Louis-Dreyfus (great-grandfather) Léopold Louis-Dreyfus (great-great-grandfather) Julia Scarlett Elizabeth Louis-Dreyfus ( / ˌ l uː i ˈ d r aɪ f ə s / LOO -ee DRY -fəs ; born January 13, 1961) is an American actress, comedian, and producer. She is known for her r\n\n[DOC 6] ve-action sitcom in terms of total seasons, surpassing The Adventures of Ozzie and Harriet ' s 14 seasons with the release of its 15th season in December 2021. In December 2020, the series was renewed for a total of four additional seasons, bringing it to a total of 18 seasons. The seventeenth and most recent season premiered on July 9, 2025, and ended on August 20, 2025, with an eighteenth season in development. The series has received critical acclaim, being variously described as \"Seinfeld on crack\" and \"white trash comedy\", with many critics lauding the show's dark humor and satire and the cast's performances. It has a large cult following . Synopsis Premise The series follows a group of self-centered, heavy-drinking, narcissistic misfits, referred to as\n\n[DOC 7] ( Jeff Garlin ), and Jeff's wife Susie ( Susie Essman ). Celebrities, including comedians Richard Lewis , Wanda Sykes , and Bob Einstein , appeared on the show regularly. Actors Ted Danson and Mary Steenburgen have had recurring roles as themselves. The show is critically acclaimed and has been nominated for 30 Primetime Emmy Awards, with one win, as well as a Golden Globe win. In the first six seasons, Julia Louis-Dreyfus and Jason Alexander appear in several episodes, and Jerry Seinfeld has a cameo. In season 7, the cast of Seinfeld , including Michael Richards , return in a story arc involving David's attempt to organize a Seinfeld reunion special. On June 2, 2010, the series premiered on the TV Guide Network , its network television debut. TV Guide Netwo\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": true,\n \"reasoning\": \"Documents 1, 3, 4, and 5 provide the birth dates of the four main Seinfeld actors: Jerry Seinfeld (born 1954), Jason Alexander (born 1959), Michael Richards (born 1949), and Julia Louis‑Dreyfus (born 1961). Comparing these dates shows Michael Richards is the oldest.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1640, + "osl": 90, + "total_tokens": 1730, + "latency_ms": 2468.67, + "tokens_per_second": 36.46 + }, + "context": { + "kept_docs_count": 7, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "f27ece26-e085-4767-94c7-3eee271a5237", + "component": "answer_generator", + "hop_count": 3, + "timestamp": "2026-05-16T03:12:24.569940Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: Of the four main characters on Seinfeld, which actor is the oldest?\n\nDOCUMENTS:\n\n[DOC 1] American comedian and actor (born 1954) Jerry Seinfeld Seinfeld in 2023 Born Jerome Allen Seinfeld ( 1954-04-29 ) April 29, 1954 (age 71) Brooklyn, New York City, U. S. Education Queens College ( BA ) Spouse Jessica Sklar ( m. 1999) Children 3 Comedy career Years active 1976–present Medium Stand-up television film Genres Observational comedy clean comedy cringe comedy surreal humor deadpan satire Subjects American culture everyday life gender differences human behavior social awkwardness pop culture current events Website jerryseinfeld . com Signature Jerome Allen Seinfeld ( / ˈ s aɪ n f ɛ l d / SYNE -feld ; born April 29, 1954) is an American stand-up comedian , actor, writer, filmmaker, and television producer specializing in observational comedy . Seinfel\n\n[DOC 2] American television sitcom (1989–1998) Seinfeld Genre Sitcom Slice of life Dark comedy Postmodernism Created by Larry David Jerry Seinfeld Showrunners Larry David (seasons 1–7) Jerry Seinfeld (season 8–9) Directed by Tom Cherones (seasons 1–5) Andy Ackerman (seasons 6–9) Various (seasons 1, 3, 6 & 8) Starring Jerry Seinfeld Julia Louis-Dreyfus Michael Richards Jason Alexander Theme music composer Jonathan Wolff Composer Jonathan Wolff Country of origin United States Original language English No. of seasons 9 No. of episodes 180 ( list of episodes ) Production Executive producers Larry David (1990–1996) George Shapiro Howard West Andrew Scheinman (1991–1993) Jerry Seinfeld (1996–1998) Alec Berg (1997–1998) Jeff Schaffer (1997–1998) Camera setup Multi-camera R\n\n[DOC 3] American actor (born 1959) Jason Alexander Alexander in 2009 Born Jay Scott Greenspan ( 1959-09-23 ) September 23, 1959 (age 66) Newark, New Jersey , U. S. Occupations Actor comedian director podcaster singer presenter Years active 1981–present Spouse Daena Title ( m. 1982) Children 2 Alexander's speaking voice Celebrating the retirement of Cantor Jack of Parsippany-Troy Hills, New Jersey's Adath Shalom. Jay Scott Greenspan (born September 23, 1959), known professionally as Jason Alexander , is an American actor and comedian. Over the course of his career, he has received an Emmy Award and a Tony Award as well as nominations for four Golden Globe Awards . He gained stardom for his role as George Costanza in the NBC sitcom Seinfeld (1989–1998), for which he w\n\n[DOC 4] American actor and comedian (born 1949) Michael Richards Richards in 1993 Born Michael Anthony Richards ( 1949-07-24 ) July 24, 1949 (age 76) Culver City, California , U. S. Alma mater The Evergreen State College (BA) Occupations Actor comedian Years active 1979–present Spouses Cathleen Lyons ( m. 1974; div. 1993) Beth Skipp ( m. 2010) Children 2 Military career Allegiance United States Branch U. S. Army Service years 1970–1972 Michael Anthony Richards (born July 24, 1949) is an American actor and comedian. He achieved global recognition for starring as Cosmo Kramer on the NBC television sitcom Seinfeld from 1989 to 1998. He began his career as a stand-up comedian, first entering the national spotlight when he was featured on Billy Crystal 's first cable TV\n\n[DOC 5] American actress and comedian (born 1961) Julia Louis-Dreyfus Louis-Dreyfus in 2019 Born ( 1961-01-13 ) January 13, 1961 (age 65) New York City , U. S. Occupations Actor comedian producer Years active 1980–present Spouse Brad Hall ( m. 1987) Children 2, including Charlie Hall Father Gérard Louis-Dreyfus Relatives Lauren Bowles (half-sister) Patrick Fischler (brother-in-law) Robert Louis-Dreyfus (second cousin once removed) Kyril Louis-Dreyfus (third cousin) Pierre Louis-Dreyfus (grandfather) Charles Louis-Dreyfus (great-grandfather) Léopold Louis-Dreyfus (great-great-grandfather) Julia Scarlett Elizabeth Louis-Dreyfus ( / ˌ l uː i ˈ d r aɪ f ə s / LOO -ee DRY -fəs ; born January 13, 1961) is an American actress, comedian, and producer. She is known for her r\n\n[DOC 6] ve-action sitcom in terms of total seasons, surpassing The Adventures of Ozzie and Harriet ' s 14 seasons with the release of its 15th season in December 2021. In December 2020, the series was renewed for a total of four additional seasons, bringing it to a total of 18 seasons. The seventeenth and most recent season premiered on July 9, 2025, and ended on August 20, 2025, with an eighteenth season in development. The series has received critical acclaim, being variously described as \"Seinfeld on crack\" and \"white trash comedy\", with many critics lauding the show's dark humor and satire and the cast's performances. It has a large cult following . Synopsis Premise The series follows a group of self-centered, heavy-drinking, narcissistic misfits, referred to as\n\n[DOC 7] ( Jeff Garlin ), and Jeff's wife Susie ( Susie Essman ). Celebrities, including comedians Richard Lewis , Wanda Sykes , and Bob Einstein , appeared on the show regularly. Actors Ted Danson and Mary Steenburgen have had recurring roles as themselves. The show is critically acclaimed and has been nominated for 30 Primetime Emmy Awards, with one win, as well as a Golden Globe win. In the first six seasons, Julia Louis-Dreyfus and Jason Alexander appear in several episodes, and Jerry Seinfeld has a cameo. In season 7, the cast of Seinfeld , including Michael Richards , return in a story arc involving David's attempt to organize a Seinfeld reunion special. On June 2, 2010, the series premiered on the TV Guide Network , its network television debut. TV Guide Netwo\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Michael Richards (born July 24 1949) is the oldest of the four main Seinfeld actors.", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1515, + "osl": 175, + "total_tokens": 1690, + "latency_ms": 3366.39, + "tokens_per_second": 51.98 + }, + "context": { + "num_documents_used": 7, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:12:24.570701Z", + "summary": { + "total_llm_calls": 7, + "total_hops": 3, + "total_input_tokens": 8229, + "total_output_tokens": 2147, + "total_tokens": 10376, + "total_latency_ms": 39718.76, + "average_tokens_per_second": 54.0, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Seinfeld", + "https://en.wikipedia.org/wiki/Jerry_Seinfeld", + "https://en.wikipedia.org/wiki/Jason_Alexander", + "https://en.wikipedia.org/wiki/Julia_Louis-Dreyfus", + "https://en.wikipedia.org/wiki/Michael_Richards" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Michael Richards (born July 24 1949) is the oldest of the four main Seinfeld actors.", + "ground_truth_answer": "Michael Richards" + } + }, + { + "query_id": "36", + "original_query": "How old was Harvard University, when the person whom the longest river in British Columbia is named after, was born? The river in question only flows within the confines of British Columbia and does not enter any other province or territory. ", + "timestamp_start": "2026-05-16T03:12:24.600454Z", + "llm_calls": [ + { + "call_id": "f4f8327a-d8bc-480b-8bb5-5202a084f63f", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:12:36.035246Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: How old was Harvard University, when the person whom the longest river in British Columbia is named after, was born? The river in question only flows within the confines of British Columbia and does not enter any other province or territory. \n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Fraser River longest\",\n \"Fraser River named after\",\n \"Simon Fraser birth\"\n ],\n \"feedback\": \"First query to confirm the longest river wholly within British Columbia. Second to discover the namesake of that river. Third to obtain the birth year of the namesake, needed to calculate Harvard University's age at that time.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 469, + "osl": 592, + "total_tokens": 1061, + "latency_ms": 11434.63, + "tokens_per_second": 51.77 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "a1cc0ec0-f315-4b20-be0e-d9c4827cacdc", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:12:40.651840Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: How old was Harvard University, when the person whom the longest river in British Columbia is named after, was born? The river in question only flows within the confines of British Columbia and does not enter any other province or territory. \n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] r , Willow River , Quesnel River , Thompson River , Coquihalla River , Chilliwack River , Sumas River , Salmon River (lower mainland) • right Morkill River , McGregor River , Salmon River (interior) , Nechako River , West Road (Blackwater) River , Chilcotin River , Bridge River , Harrison River , Stave River , Pitt River , Coquitlam River Protection status Ramsar Wetland Official name Fraser River Delta Designated 24 May 1982 Reference no. 243 The Fraser River ( / ˈ f r eɪ z ər / ) is the longest river within British Columbia , Canada, rising at Fraser Pass near Blackrock Mountain in the Rocky Mountains and flowing for 1,375 kilometres (854 mi), into the Strait of Georgia just south of the City of Vancouver . The river's annual discharge at its mouth is 112\n\n[NEW 2] r him by the explorer David Thompson . Fraser Lake , a lake in north-central British Columbia. Fort Fraser , just east of Fraser Lake. Simon Fraser University , in Burnaby , British Columbia Simon Fraser Elementary School in Vancouver, British Columbia Simon Fraser School in Calgary, Alberta The Simon Fraser Bridge in Prince George over the Fraser River The Simon Fraser Rose , (explorer series) developed by Agriculture and Agri-Food Canada, was named in his honor. Fraser Squadron, Royal Military College of Canada CCGS Simon Fraser , a former Canadian Coast Guard vessel References ^ a b History of British Columbia from its earliest discovery to the present time , p. 97 Archived 2011-05-27 at the Wayback Machine , Alexander Begg, publ. William Briggs, Toronto,\n\n[NEW 3] he Government Office of Tourism. His maternal uncle, George Genereux , was the only Canadian to win a gold medal in the 1952 Summer Olympics , at the Olympic Trap . Fraser and his three older brothers have Irish, Scottish, German, Czech, and French-Canadian ancestry. He holds dual American and Canadian citizenship. Fraser's family moved often during his childhood, living in Eureka, California ; Seattle, Washington ; Ottawa, Ontario ; the Netherlands ; and Switzerland . His earlier years were spent attending a Montessori school in Detroit and the Sacred Heart School in Bellevue, Washington . He then attended Upper Canada College . While on vacation in London , in the 1970s, he attended his first professional theater-show, Oliver! , in the West End , which spa\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 3 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[1,1,0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 795, + "osl": 478, + "total_tokens": 1273, + "latency_ms": 4077.79, + "tokens_per_second": 117.22 + }, + "context": { + "num_documents_evaluated": 3, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "e2b301e7-0068-4a34-8149-3028c3743884", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:12:43.609006Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: How old was Harvard University, when the person whom the longest river in British Columbia is named after, was born? The river in question only flows within the confines of British Columbia and does not enter any other province or territory. \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] r , Willow River , Quesnel River , Thompson River , Coquihalla River , Chilliwack River , Sumas River , Salmon River (lower mainland) • right Morkill River , McGregor River , Salmon River (interior) , Nechako River , West Road (Blackwater) River , Chilcotin River , Bridge River , Harrison River , Stave River , Pitt River , Coquitlam River Protection status Ramsar Wetland Official name Fraser River Delta Designated 24 May 1982 Reference no. 243 The Fraser River ( / ˈ f r eɪ z ər / ) is the longest river within British Columbia , Canada, rising at Fraser Pass near Blackrock Mountain in the Rocky Mountains and flowing for 1,375 kilometres (854 mi), into the Strait of Georgia just south of the City of Vancouver . The river's annual discharge at its mouth is 112\n\n[DOC 2] r him by the explorer David Thompson . Fraser Lake , a lake in north-central British Columbia. Fort Fraser , just east of Fraser Lake. Simon Fraser University , in Burnaby , British Columbia Simon Fraser Elementary School in Vancouver, British Columbia Simon Fraser School in Calgary, Alberta The Simon Fraser Bridge in Prince George over the Fraser River The Simon Fraser Rose , (explorer series) developed by Agriculture and Agri-Food Canada, was named in his honor. Fraser Squadron, Royal Military College of Canada CCGS Simon Fraser , a former Canadian Coast Guard vessel References ^ a b History of British Columbia from its earliest discovery to the present time , p. 97 Archived 2011-05-27 at the Wayback Machine , Alexander Begg, publ. William Briggs, Toronto,\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents identify the Fraser River as the longest river in British Columbia and associate it with Simon Fraser, but they do not provide Simon Fraser's birth year. They also do not give Harvard University's founding year. Without both the birth year of Simon Fraser and Harvard's founding year, we cannot compute Harvard's age at his birth.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 616, + "osl": 81, + "total_tokens": 697, + "latency_ms": 2956.33, + "tokens_per_second": 27.4 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "60298589-3089-4c13-a048-904ff388ec9d", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:12:50.995145Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: How old was Harvard University, when the person whom the longest river in British Columbia is named after, was born? The river in question only flows within the confines of British Columbia and does not enter any other province or territory. \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] r , Willow River , Quesnel River , Thompson River , Coquihalla River , Chilliwack River , Sumas River , Salmon River (lower mainland) • right Morkill River , McGregor River , Salmon River (interior) , Nechako River , West Road (Blackwater) River , Chilcotin River , Bridge River , Harrison River , Stave River , Pitt River , Coquitlam River Protection status Ramsar Wetland Official name Fraser River Delta Designated 24 May 1982 Reference no. 243 The Fraser River ( / ˈ f r eɪ z ər / ) is the longest river within British Columbia , Canada, rising at Fraser Pass near Blackrock Mountain in the Rocky Mountains and flowing for 1,375 kilometres (854 mi), into the Strait of Georgia just south of the City of Vancouver . The river's annual discharge at its mouth is 112\n\n[DOC 2] r him by the explorer David Thompson . Fraser Lake , a lake in north-central British Columbia. Fort Fraser , just east of Fraser Lake. Simon Fraser University , in Burnaby , British Columbia Simon Fraser Elementary School in Vancouver, British Columbia Simon Fraser School in Calgary, Alberta The Simon Fraser Bridge in Prince George over the Fraser River The Simon Fraser Rose , (explorer series) developed by Agriculture and Agri-Food Canada, was named in his honor. Fraser Squadron, Royal Military College of Canada CCGS Simon Fraser , a former Canadian Coast Guard vessel References ^ a b History of British Columbia from its earliest discovery to the present time , p. 97 Archived 2011-05-27 at the Wayback Machine , Alexander Begg, publ. William Briggs, Toronto,\n\n\nSEARCH HISTORY: - Query: 'Fraser River longest' → 1 docs\n- Query: 'Fraser River named after' → 1 docs\n- Query: 'Simon Fraser birth' → 1 docs\n\nFEEDBACK: First query to confirm the longest river wholly within British Columbia. Second to discover the namesake of that river. Third to obtain the birth year of the namesake, needed to calculate Harvard University's age at that time.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Longest river entirely in British Columbia\",\n \"Simon Fraser explorer birth year\",\n \"Harvard University founded year\"\n ],\n \"feedback\": \"We need to confirm the longest river that stays wholly within BC, identify its namesake (Simon Fraser), get his birth year, and verify Harvard's founding year to compute the university's age at that time.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 851, + "osl": 82, + "total_tokens": 933, + "latency_ms": 7385.5, + "tokens_per_second": 11.1 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "d7160f30-bf30-472c-aeed-0a52340e6998", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T03:12:57.645833Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: How old was Harvard University, when the person whom the longest river in British Columbia is named after, was born? The river in question only flows within the confines of British Columbia and does not enter any other province or territory. \n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Rivers on this list shown on a map of Canada The Mackenzie River is the longest stream in Canada if measured from its mouth on the Beaufort Sea to the headwaters of the Finlay River , a major upstream tributary. The main stem , a much shorter segment of the Mackenzie, is marked in dark blue. Among the longest rivers of Canada are 47 streams of at least 600 km (370 mi). In the case of some rivers such as the Columbia , the length listed in the table below is solely that of the main stem . In the case of others such as the Mackenzie , it is the combined lengths of the main stem and one or more upstream tributaries, as noted. Excluded from the list are rivers such as the Dauphin , a short connecting link between lakes Manitoba and Winnipeg , with main stems of\n\n[NEW 2] ) Postgraduates 14,151 (fall 2024) Location Cambridge, Massachusetts , US 42°22′28′′N 71°07′01′′W  /  42.37444°N 71.11694°W  / 42.37444; -71.11694 Campus Midsize city , 209 acres (85 ha) Newspaper The Harvard Crimson Colors Crimson, white, and black Nickname Crimson Sporting affiliations NCAA Division I FCS – Ivy League ECAC Hockey NEISA CWPA IRA EAWRC EARC EISA Mascot John Harvard Website www . harvard . edu Harvard University is a private Ivy League research university in Cambridge, Massachusetts , United States. Founded in 1636, and named in 1639 for its first benefactor, the Puritan clergyman John Harvard , it is the oldest institution of higher learning in the United States. Its influence, wealth, and rankings have made it one of the most prestigious\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] r , Willow River , Quesnel River , Thompson River , Coquihalla River , Chilliwack River , Sumas River , Salmon River (lower mainland) • right Morkill River , McGregor River , Salmon River (interior) , Nechako River , West Road (Blackwater) River , Chilcotin River , Bridge River , Harrison River , St...\n[KEPT 2] r him by the explorer David Thompson . Fraser Lake , a lake in north-central British Columbia. Fort Fraser , just east of Fraser Lake. Simon Fraser University , in Burnaby , British Columbia Simon Fraser Elementary School in Vancouver, British Columbia Simon Fraser School in Calgary, Alberta The Sim...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 2 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[0,1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 824, + "osl": 620, + "total_tokens": 1444, + "latency_ms": 6095.3, + "tokens_per_second": 101.72 + }, + "context": { + "num_documents_evaluated": 2, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "12bc95e1-1558-4d51-b41c-74e38e0c36a2", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:12:58.885546Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: How old was Harvard University, when the person whom the longest river in British Columbia is named after, was born? The river in question only flows within the confines of British Columbia and does not enter any other province or territory. \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] r , Willow River , Quesnel River , Thompson River , Coquihalla River , Chilliwack River , Sumas River , Salmon River (lower mainland) • right Morkill River , McGregor River , Salmon River (interior) , Nechako River , West Road (Blackwater) River , Chilcotin River , Bridge River , Harrison River , Stave River , Pitt River , Coquitlam River Protection status Ramsar Wetland Official name Fraser River Delta Designated 24 May 1982 Reference no. 243 The Fraser River ( / ˈ f r eɪ z ər / ) is the longest river within British Columbia , Canada, rising at Fraser Pass near Blackrock Mountain in the Rocky Mountains and flowing for 1,375 kilometres (854 mi), into the Strait of Georgia just south of the City of Vancouver . The river's annual discharge at its mouth is 112\n\n[DOC 2] r him by the explorer David Thompson . Fraser Lake , a lake in north-central British Columbia. Fort Fraser , just east of Fraser Lake. Simon Fraser University , in Burnaby , British Columbia Simon Fraser Elementary School in Vancouver, British Columbia Simon Fraser School in Calgary, Alberta The Simon Fraser Bridge in Prince George over the Fraser River The Simon Fraser Rose , (explorer series) developed by Agriculture and Agri-Food Canada, was named in his honor. Fraser Squadron, Royal Military College of Canada CCGS Simon Fraser , a former Canadian Coast Guard vessel References ^ a b History of British Columbia from its earliest discovery to the present time , p. 97 Archived 2011-05-27 at the Wayback Machine , Alexander Begg, publ. William Briggs, Toronto,\n\n[DOC 3] ) Postgraduates 14,151 (fall 2024) Location Cambridge, Massachusetts , US 42°22′28′′N 71°07′01′′W  /  42.37444°N 71.11694°W  / 42.37444; -71.11694 Campus Midsize city , 209 acres (85 ha) Newspaper The Harvard Crimson Colors Crimson, white, and black Nickname Crimson Sporting affiliations NCAA Division I FCS – Ivy League ECAC Hockey NEISA CWPA IRA EAWRC EARC EISA Mascot John Harvard Website www . harvard . edu Harvard University is a private Ivy League research university in Cambridge, Massachusetts , United States. Founded in 1636, and named in 1639 for its first benefactor, the Puritan clergyman John Harvard , it is the oldest institution of higher learning in the United States. Its influence, wealth, and rankings have made it one of the most prestigious\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide the longest river in British Columbia (the Fraser River) and indicate it is named after explorer Simon Fraser, as well as Harvard University's founding year (1636). However, they do not contain Simon Fraser's birth year, which is required to calculate Harvard University's age at the time of his birth.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 885, + "osl": 374, + "total_tokens": 1259, + "latency_ms": 1238.79, + "tokens_per_second": 301.91 + }, + "context": { + "kept_docs_count": 3, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "76b8ce45-6d47-4517-b411-134f3afe0891", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:13:09.616959Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: How old was Harvard University, when the person whom the longest river in British Columbia is named after, was born? The river in question only flows within the confines of British Columbia and does not enter any other province or territory. \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] r , Willow River , Quesnel River , Thompson River , Coquihalla River , Chilliwack River , Sumas River , Salmon River (lower mainland) • right Morkill River , McGregor River , Salmon River (interior) , Nechako River , West Road (Blackwater) River , Chilcotin River , Bridge River , Harrison River , Stave River , Pitt River , Coquitlam River Protection status Ramsar Wetland Official name Fraser River Delta Designated 24 May 1982 Reference no. 243 The Fraser River ( / ˈ f r eɪ z ər / ) is the longest river within British Columbia , Canada, rising at Fraser Pass near Blackrock Mountain in the Rocky Mountains and flowing for 1,375 kilometres (854 mi), into the Strait of Georgia just south of the City of Vancouver . The river's annual discharge at its mouth is 112\n\n[DOC 2] r him by the explorer David Thompson . Fraser Lake , a lake in north-central British Columbia. Fort Fraser , just east of Fraser Lake. Simon Fraser University , in Burnaby , British Columbia Simon Fraser Elementary School in Vancouver, British Columbia Simon Fraser School in Calgary, Alberta The Simon Fraser Bridge in Prince George over the Fraser River The Simon Fraser Rose , (explorer series) developed by Agriculture and Agri-Food Canada, was named in his honor. Fraser Squadron, Royal Military College of Canada CCGS Simon Fraser , a former Canadian Coast Guard vessel References ^ a b History of British Columbia from its earliest discovery to the present time , p. 97 Archived 2011-05-27 at the Wayback Machine , Alexander Begg, publ. William Briggs, Toronto,\n\n[DOC 3] ) Postgraduates 14,151 (fall 2024) Location Cambridge, Massachusetts , US 42°22′28′′N 71°07′01′′W  /  42.37444°N 71.11694°W  / 42.37444; -71.11694 Campus Midsize city , 209 acres (85 ha) Newspaper The Harvard Crimson Colors Crimson, white, and black Nickname Crimson Sporting affiliations NCAA Division I FCS – Ivy League ECAC Hockey NEISA CWPA IRA EAWRC EARC EISA Mascot John Harvard Website www . harvard . edu Harvard University is a private Ivy League research university in Cambridge, Massachusetts , United States. Founded in 1636, and named in 1639 for its first benefactor, the Puritan clergyman John Harvard , it is the oldest institution of higher learning in the United States. Its influence, wealth, and rankings have made it one of the most prestigious\n\n\nSEARCH HISTORY: - Query: 'Fraser River longest' → 1 docs\n- Query: 'Fraser River named after' → 1 docs\n- Query: 'Simon Fraser birth' → 1 docs\n- Query: 'Longest river entirely in British Columbia' → 1 docs\n- Query: 'Simon Fraser explorer birth year' → 0 docs\n- Query: 'Harvard University founded year' → 1 docs\n\nFEEDBACK: First query to confirm the longest river wholly within British Columbia. Second to discover the namesake of that river. Third to obtain the birth year of the namesake, needed to calculate Harvard University's age at that time.\nWe need to confirm the longest river that stays wholly within BC, identify its namesake (Simon Fraser), get his birth year, and verify Harvard's founding year to compute the university's age at that time.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Simon Fraser explorer\",\n \"Fraser River namesake\",\n \"longest river entirely British Columbia\"\n ],\n \"feedback\": \"We have identified the longest river wholly within BC (Fraser River) but need to confirm its namesake and obtain the explorer's birth year. The first query should retrieve Simon Fraser's biography with his birth date. The second query reinforces the connection between the river and its namesake. The third query ensures the river is indeed the longest that stays entirely within BC.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1190, + "osl": 673, + "total_tokens": 1863, + "latency_ms": 10730.73, + "tokens_per_second": 62.72 + }, + "context": { + "kept_docs_count": 3, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "aa41855a-c98d-4138-ac06-e69415c20a0b", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:13:11.555101Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: How old was Harvard University, when the person whom the longest river in British Columbia is named after, was born? The river in question only flows within the confines of British Columbia and does not enter any other province or territory. \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] r , Willow River , Quesnel River , Thompson River , Coquihalla River , Chilliwack River , Sumas River , Salmon River (lower mainland) • right Morkill River , McGregor River , Salmon River (interior) , Nechako River , West Road (Blackwater) River , Chilcotin River , Bridge River , Harrison River , Stave River , Pitt River , Coquitlam River Protection status Ramsar Wetland Official name Fraser River Delta Designated 24 May 1982 Reference no. 243 The Fraser River ( / ˈ f r eɪ z ər / ) is the longest river within British Columbia , Canada, rising at Fraser Pass near Blackrock Mountain in the Rocky Mountains and flowing for 1,375 kilometres (854 mi), into the Strait of Georgia just south of the City of Vancouver . The river's annual discharge at its mouth is 112\n\n[DOC 2] r him by the explorer David Thompson . Fraser Lake , a lake in north-central British Columbia. Fort Fraser , just east of Fraser Lake. Simon Fraser University , in Burnaby , British Columbia Simon Fraser Elementary School in Vancouver, British Columbia Simon Fraser School in Calgary, Alberta The Simon Fraser Bridge in Prince George over the Fraser River The Simon Fraser Rose , (explorer series) developed by Agriculture and Agri-Food Canada, was named in his honor. Fraser Squadron, Royal Military College of Canada CCGS Simon Fraser , a former Canadian Coast Guard vessel References ^ a b History of British Columbia from its earliest discovery to the present time , p. 97 Archived 2011-05-27 at the Wayback Machine , Alexander Begg, publ. William Briggs, Toronto,\n\n[DOC 3] ) Postgraduates 14,151 (fall 2024) Location Cambridge, Massachusetts , US 42°22′28′′N 71°07′01′′W  /  42.37444°N 71.11694°W  / 42.37444; -71.11694 Campus Midsize city , 209 acres (85 ha) Newspaper The Harvard Crimson Colors Crimson, white, and black Nickname Crimson Sporting affiliations NCAA Division I FCS – Ivy League ECAC Hockey NEISA CWPA IRA EAWRC EARC EISA Mascot John Harvard Website www . harvard . edu Harvard University is a private Ivy League research university in Cambridge, Massachusetts , United States. Founded in 1636, and named in 1639 for its first benefactor, the Puritan clergyman John Harvard , it is the oldest institution of higher learning in the United States. Its influence, wealth, and rankings have made it one of the most prestigious\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Missing: Simon Fraser's birth year (the person after whom the longest river in British Columbia, the Fraser River, is named). The documents provide Harvard University's founding year (1636) but do not contain the birth date of Simon Fraser, so the age of Harvard at his birth cannot be determined.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 892, + "osl": 318, + "total_tokens": 1210, + "latency_ms": 1357.68, + "tokens_per_second": 234.22 + }, + "context": { + "kept_docs_count": 3, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "216abf32-d3dc-4daa-8483-21dc39d49fdc", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:13:12.742582Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: How old was Harvard University, when the person whom the longest river in British Columbia is named after, was born? The river in question only flows within the confines of British Columbia and does not enter any other province or territory. \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] r , Willow River , Quesnel River , Thompson River , Coquihalla River , Chilliwack River , Sumas River , Salmon River (lower mainland) • right Morkill River , McGregor River , Salmon River (interior) , Nechako River , West Road (Blackwater) River , Chilcotin River , Bridge River , Harrison River , Stave River , Pitt River , Coquitlam River Protection status Ramsar Wetland Official name Fraser River Delta Designated 24 May 1982 Reference no. 243 The Fraser River ( / ˈ f r eɪ z ər / ) is the longest river within British Columbia , Canada, rising at Fraser Pass near Blackrock Mountain in the Rocky Mountains and flowing for 1,375 kilometres (854 mi), into the Strait of Georgia just south of the City of Vancouver . The river's annual discharge at its mouth is 112\n\n[DOC 2] r him by the explorer David Thompson . Fraser Lake , a lake in north-central British Columbia. Fort Fraser , just east of Fraser Lake. Simon Fraser University , in Burnaby , British Columbia Simon Fraser Elementary School in Vancouver, British Columbia Simon Fraser School in Calgary, Alberta The Simon Fraser Bridge in Prince George over the Fraser River The Simon Fraser Rose , (explorer series) developed by Agriculture and Agri-Food Canada, was named in his honor. Fraser Squadron, Royal Military College of Canada CCGS Simon Fraser , a former Canadian Coast Guard vessel References ^ a b History of British Columbia from its earliest discovery to the present time , p. 97 Archived 2011-05-27 at the Wayback Machine , Alexander Begg, publ. William Briggs, Toronto,\n\n[DOC 3] ) Postgraduates 14,151 (fall 2024) Location Cambridge, Massachusetts , US 42°22′28′′N 71°07′01′′W  /  42.37444°N 71.11694°W  / 42.37444; -71.11694 Campus Midsize city , 209 acres (85 ha) Newspaper The Harvard Crimson Colors Crimson, white, and black Nickname Crimson Sporting affiliations NCAA Division I FCS – Ivy League ECAC Hockey NEISA CWPA IRA EAWRC EARC EISA Mascot John Harvard Website www . harvard . edu Harvard University is a private Ivy League research university in Cambridge, Massachusetts , United States. Founded in 1636, and named in 1639 for its first benefactor, the Puritan clergyman John Harvard , it is the oldest institution of higher learning in the United States. Its influence, wealth, and rankings have made it one of the most prestigious\n\n\nSEARCH HISTORY: - Query: 'Fraser River longest' → 1 docs\n- Query: 'Fraser River named after' → 1 docs\n- Query: 'Simon Fraser birth' → 1 docs\n- Query: 'Longest river entirely in British Columbia' → 1 docs\n- Query: 'Simon Fraser explorer birth year' → 0 docs\n- Query: 'Harvard University founded year' → 1 docs\n- Query: 'Simon Fraser explorer' → 0 docs\n- Query: 'Fraser River namesake' → 0 docs\n- Query: 'longest river entirely British Columbia' → 0 docs\n\nFEEDBACK: First query to confirm the longest river wholly within British Columbia. Second to discover the namesake of that river. Third to obtain the birth year of the namesake, needed to calculate Harvard University's age at that time.\nWe need to confirm the longest river that stays wholly within BC, identify its namesake (Simon Fraser), get his birth year, and verify Harvard's founding year to compute the university's age at that time.\nWe have identified the longest river wholly within BC (Fraser River) but need to confirm its namesake and obtain the explorer's birth year. The first query should retrieve Simon Fraser's biography with his birth date. The second query reinforces the connection between the river and its namesake. The third query ensures the river is indeed the longest that stays entirely within BC.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Simon Fraser (explorer) biography\",\n \"Fraser River named after Simon Fraser\",\n \"Fraser River longest British Columbia\"\n ],\n \"feedback\": \"We need Simon Fraser's birth year (biography should include it) and clear confirmation that the Fraser River is named for him. Also reaffirm that the Fraser River is the longest river entirely within BC.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1324, + "osl": 478, + "total_tokens": 1802, + "latency_ms": 1186.7, + "tokens_per_second": 402.8 + }, + "context": { + "kept_docs_count": 3, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "5fb7fb03-b61e-4d0f-a8e6-7f7fb471c515", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:13:20.858524Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: How old was Harvard University, when the person whom the longest river in British Columbia is named after, was born? The river in question only flows within the confines of British Columbia and does not enter any other province or territory. \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] r , Willow River , Quesnel River , Thompson River , Coquihalla River , Chilliwack River , Sumas River , Salmon River (lower mainland) • right Morkill River , McGregor River , Salmon River (interior) , Nechako River , West Road (Blackwater) River , Chilcotin River , Bridge River , Harrison River , Stave River , Pitt River , Coquitlam River Protection status Ramsar Wetland Official name Fraser River Delta Designated 24 May 1982 Reference no. 243 The Fraser River ( / ˈ f r eɪ z ər / ) is the longest river within British Columbia , Canada, rising at Fraser Pass near Blackrock Mountain in the Rocky Mountains and flowing for 1,375 kilometres (854 mi), into the Strait of Georgia just south of the City of Vancouver . The river's annual discharge at its mouth is 112\n\n[DOC 2] r him by the explorer David Thompson . Fraser Lake , a lake in north-central British Columbia. Fort Fraser , just east of Fraser Lake. Simon Fraser University , in Burnaby , British Columbia Simon Fraser Elementary School in Vancouver, British Columbia Simon Fraser School in Calgary, Alberta The Simon Fraser Bridge in Prince George over the Fraser River The Simon Fraser Rose , (explorer series) developed by Agriculture and Agri-Food Canada, was named in his honor. Fraser Squadron, Royal Military College of Canada CCGS Simon Fraser , a former Canadian Coast Guard vessel References ^ a b History of British Columbia from its earliest discovery to the present time , p. 97 Archived 2011-05-27 at the Wayback Machine , Alexander Begg, publ. William Briggs, Toronto,\n\n[DOC 3] ) Postgraduates 14,151 (fall 2024) Location Cambridge, Massachusetts , US 42°22′28′′N 71°07′01′′W  /  42.37444°N 71.11694°W  / 42.37444; -71.11694 Campus Midsize city , 209 acres (85 ha) Newspaper The Harvard Crimson Colors Crimson, white, and black Nickname Crimson Sporting affiliations NCAA Division I FCS – Ivy League ECAC Hockey NEISA CWPA IRA EAWRC EARC EISA Mascot John Harvard Website www . harvard . edu Harvard University is a private Ivy League research university in Cambridge, Massachusetts , United States. Founded in 1636, and named in 1639 for its first benefactor, the Puritan clergyman John Harvard , it is the oldest institution of higher learning in the United States. Its influence, wealth, and rankings have made it one of the most prestigious\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"sufficient\": false, \"reasoning\": \"We have Harvard University's founding year (1636) from Document 3, and we identified the longest river in BC as the Fraser River named after explorer Simon Fraser from Documents 1 and 2. However, the birth year of Simon Fraser is not present in any of the kept documents, so we cannot calculate Harvard's age at his birth.\"}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 868, + "osl": 357, + "total_tokens": 1225, + "latency_ms": 7563.71, + "tokens_per_second": 47.2 + }, + "context": { + "kept_docs_count": 3, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "f5b31da8-f129-438c-adbf-ad4016702ccc", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:13:24.460013Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: How old was Harvard University, when the person whom the longest river in British Columbia is named after, was born? The river in question only flows within the confines of British Columbia and does not enter any other province or territory. \n\nDOCUMENTS:\n\n[DOC 1] r , Willow River , Quesnel River , Thompson River , Coquihalla River , Chilliwack River , Sumas River , Salmon River (lower mainland) • right Morkill River , McGregor River , Salmon River (interior) , Nechako River , West Road (Blackwater) River , Chilcotin River , Bridge River , Harrison River , Stave River , Pitt River , Coquitlam River Protection status Ramsar Wetland Official name Fraser River Delta Designated 24 May 1982 Reference no. 243 The Fraser River ( / ˈ f r eɪ z ər / ) is the longest river within British Columbia , Canada, rising at Fraser Pass near Blackrock Mountain in the Rocky Mountains and flowing for 1,375 kilometres (854 mi), into the Strait of Georgia just south of the City of Vancouver . The river's annual discharge at its mouth is 112\n\n[DOC 2] r him by the explorer David Thompson . Fraser Lake , a lake in north-central British Columbia. Fort Fraser , just east of Fraser Lake. Simon Fraser University , in Burnaby , British Columbia Simon Fraser Elementary School in Vancouver, British Columbia Simon Fraser School in Calgary, Alberta The Simon Fraser Bridge in Prince George over the Fraser River The Simon Fraser Rose , (explorer series) developed by Agriculture and Agri-Food Canada, was named in his honor. Fraser Squadron, Royal Military College of Canada CCGS Simon Fraser , a former Canadian Coast Guard vessel References ^ a b History of British Columbia from its earliest discovery to the present time , p. 97 Archived 2011-05-27 at the Wayback Machine , Alexander Begg, publ. William Briggs, Toronto,\n\n[DOC 3] ) Postgraduates 14,151 (fall 2024) Location Cambridge, Massachusetts , US 42°22′28′′N 71°07′01′′W  /  42.37444°N 71.11694°W  / 42.37444; -71.11694 Campus Midsize city , 209 acres (85 ha) Newspaper The Harvard Crimson Colors Crimson, white, and black Nickname Crimson Sporting affiliations NCAA Division I FCS – Ivy League ECAC Hockey NEISA CWPA IRA EAWRC EARC EISA Mascot John Harvard Website www . harvard . edu Harvard University is a private Ivy League research university in Cambridge, Massachusetts , United States. Founded in 1636, and named in 1639 for its first benefactor, the Puritan clergyman John Harvard , it is the oldest institution of higher learning in the United States. Its influence, wealth, and rankings have made it one of the most prestigious\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 722, + "osl": 182, + "total_tokens": 904, + "latency_ms": 3600.73, + "tokens_per_second": 50.55 + }, + "context": { + "num_documents_used": 3, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:13:24.460741Z", + "summary": { + "total_llm_calls": 11, + "total_hops": 5, + "total_input_tokens": 9436, + "total_output_tokens": 4235, + "total_tokens": 13671, + "total_latency_ms": 57627.89, + "average_tokens_per_second": 128.06, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Harvard_University", + "https://en.wikipedia.org/wiki/List_of_longest_rivers_of_Canada", + "https://en.wikipedia.org/wiki/Fraser_River", + "https://en.wikipedia.org/wiki/Simon_Fraser_(explorer)" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "140 years old." + } + }, + { + "query_id": "37", + "original_query": "On the same day that the The Mercedes-Benz W222 arrived at dealerships, a star of the sit-com Modern Family was wed. Who did the star marry?", + "timestamp_start": "2026-05-16T03:13:24.492140Z", + "llm_calls": [ + { + "call_id": "9b23a5f8-ddb1-41cf-8322-717eb2253e44", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:13:32.438284Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: On the same day that the The Mercedes-Benz W222 arrived at dealerships, a star of the sit-com Modern Family was wed. Who did the star marry?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Mercedes-Benz W222 dealership arrival\",\n \"Modern Family star wedding 2014\",\n \"Jesse Tyler Ferguson wedding\"\n ],\n \"feedback\": \"Need the exact date the W222 arrived at dealerships, then identify a Modern Family cast member who married on that exact date.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 469, + "osl": 612, + "total_tokens": 1081, + "latency_ms": 7946.01, + "tokens_per_second": 77.02 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "66d36da8-19c5-46d6-a7b9-1c319fd7e4fa", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:14:24.747177Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: On the same day that the The Mercedes-Benz W222 arrived at dealerships, a star of the sit-com Modern Family was wed. Who did the star marry?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Robert Lešnik . The W222 has a design theme similar to the C-Class (W205) and E-Class (W213) . In Europe , sales of the S400 Hybrid, S350 BlueTEC, S350 BlueTEC Hybrid, and S500 began in September 2013; and sales of the S550 in the United States also began on that same month. The four-wheel drive ( 4Matic ) model went on sale in November, and additional models, including V12 models and those from AMG , were released in 2014. The W222 S-Class debuted on 15 May 2013, in Hamburg, Germany , and entered production in Sindelfingen , Germany , in June 2013. Production of the W222 ended in September 2020 with the introduction of its successor, the S-Class (W223) . Suspension Mercedes' hydropneumatic , active suspension , known as Active Body Control , was updated wi\n\n[NEW 2] 2014-03-11 ) 3ATM19 2.48 69 20 \"Mars Landing\" Lynn Shelton Josh Malmuth & Nina Pedrad March 25, 2014 ( 2014-03-25 ) 3ATM20 2.49 70 21 \"Big News\" Steven Tsuchida Berkley Johnson & Kim Rosenstock April 15, 2014 ( 2014-04-15 ) 3ATM21 2.19 71 22 \"Dance\" Trent O'Donnell Rebecca Addelman & Ryan Koh April 29, 2014 ( 2014-04-29 ) 3ATM22 2.20 72 23 \"Cruise\" Elizabeth Meriwether Luvh Rakhe & Rob Rosell May 6, 2014 ( 2014-05-06 ) 3ATM23 2.40 Season 4 (2014–15) No. overall No. in season Title Directed by Written by Original release date Prod. code U. S. viewers (millions) 73 1 \"The Last Wedding\" Trent O'Donnell J. J. Philbin September 16, 2014 ( 2014-09-16 ) 4ATM01 3.04 74 2 \"Dice\" Lynn Shelton Matt Fusfeld & Alex Cuthbertson September 23, 2014 ( 2014-09-23 ) 4ATM02 2.\n\n[NEW 3] couple's non-profit made a grant of US$10,000 to the ACLU of New Mexico for same sex efforts. In November 2013, a pop-up retail store for 'Tie The Knot' was located at the Los Angeles Beverly Center featuring the fourth collection from the couple, including professional athlete designs with Scott Fujita and Chris Kluwe , and Brittney Griner . Personal life Ferguson with Justin Mikita in 2014 Ferguson uses his full name, as there was already an actor called Jesse Ferguson in the actors' union when he joined. In September 2012, he announced his engagement to lawyer Justin Mikita, his boyfriend of nearly two years. They married in Manhattan on July 20, 2013, with the playwright and screenwriter Tony Kushner officiating at their wedding. The couple have two chil\n\n[NEW 4] d the final of New York comedy show Thrills and Spills on December 31, 2015. The final was held in Montgomery, Alabama . [ citation needed ] Personal life In 1987, Castellaneta married writer and actress Deb Lacusta , whom he had met at an improv class in Chicago . They divide their time between Los Angeles and Santa Barbara, California . Castellaneta is a vegetarian and does not drink alcohol . He enjoys exercising regularly. Filmography Discography Album Released Label Notes Two Lips February 2000 Oglio Records All-music comedy album I Am Not Homer April 23, 2002 Oglio Records Comedy album released with Deb Lacusta Also featured in: The Simpsons Sing the Blues (1990) Songs in the Key of Springfield (1997) The Yellow Album (1998) Go Simpsonic with The Simps\n\n[NEW 5] American television sitcom (2009–2020) Modern Family Genre Sitcom Mockumentary Created by Christopher Lloyd Steven Levitan Showrunners Steven Levitan Christopher Lloyd Starring Ed O'Neill Sofía Vergara Julie Bowen Ty Burrell Jesse Tyler Ferguson Eric Stonestreet Sarah Hyland Ariel Winter Nolan Gould Rico Rodriguez Aubrey Anderson-Emmons Jeremy Maguire Reid Ewing Theme music composer Gabriel Mann Composer Gabriel Mann Country of origin United States Original language English No. of seasons 11 No. of episodes 250 ( list of episodes ) Production Executive producers Steven Levitan Christopher Lloyd Jeffrey Morton Paul Corrigan Dan O'Shannon Brad Walsh Danny Zuker Bill Wrubel Jeffrey Richman Abraham Higginbotham Stephen Lloyd Elaine Ko Chuck Tatham Vali Chandrase\n\n[NEW 6] , 2014 October 13, 2014 October 22, 2014 24 Includes: Deleted Scenes Audio Commentary on \"The Locket\" Audio Commentary on \"The Rehearsal Dinner\" Audio Commentary on \"How Your Mother Met Me\" Last Forever Alternate Ending How It All Ends Cristin Milioti Audition Behind the Scenes of \"Gary Blauman\" Gag Reel The Whole Story Seasons 1–9 September 23, 2014 October 13, 2014 October 22, 2014 208 Includes: All Out of Spoilers: Looking Back at Your Mother – A Retrospective Table Read of the Series Finale with the Entire Cast The Secret of the Pineapple Incident Revealed Meeting the Mother How I Met Your Mother at Comic-Con 2013–2014 The Mosby Kids: 8 Years on the Couch How We Filmed the Kids Plus All the Special Features from Every Previous Season Seasons 1 through 9\n\n[NEW 7] n horseback with Frank, intended to go with the McLaurys to Fort Worth. Will McLaury came to Tombstone after the gun fight and joined the prosecution team in an attempt to convict the Earps and Holliday for his brothers' murder. Paul Johnson told a different story, that the McLaurys were about to leave for Iowa to attend the wedding of their sister, Sarah Caroline. Tom and Frank were especially close to Sarah, one of their 14 siblings and half-siblings. Caroline married James Reed in Richland, Iowa at the end of November that year. Virgil decides to disarm Cowboys Citizens reported to Virgil on the Cowboys' movements and their threats told him that Ike and Tom had left their livery stable and entered town while armed, in violation of the city ordinance. Virg\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 7 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 0, 1, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1673, + "osl": 3369, + "total_tokens": 5042, + "latency_ms": 51657.47, + "tokens_per_second": 65.22 + }, + "context": { + "num_documents_evaluated": 7, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "be5a3f33-752c-47ff-beb4-457ea008a6f9", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:14:34.216632Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: On the same day that the The Mercedes-Benz W222 arrived at dealerships, a star of the sit-com Modern Family was wed. Who did the star marry?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Robert Lešnik . The W222 has a design theme similar to the C-Class (W205) and E-Class (W213) . In Europe , sales of the S400 Hybrid, S350 BlueTEC, S350 BlueTEC Hybrid, and S500 began in September 2013; and sales of the S550 in the United States also began on that same month. The four-wheel drive ( 4Matic ) model went on sale in November, and additional models, including V12 models and those from AMG , were released in 2014. The W222 S-Class debuted on 15 May 2013, in Hamburg, Germany , and entered production in Sindelfingen , Germany , in June 2013. Production of the W222 ended in September 2020 with the introduction of its successor, the S-Class (W223) . Suspension Mercedes' hydropneumatic , active suspension , known as Active Body Control , was updated wi\n\n[DOC 2] couple's non-profit made a grant of US$10,000 to the ACLU of New Mexico for same sex efforts. In November 2013, a pop-up retail store for 'Tie The Knot' was located at the Los Angeles Beverly Center featuring the fourth collection from the couple, including professional athlete designs with Scott Fujita and Chris Kluwe , and Brittney Griner . Personal life Ferguson with Justin Mikita in 2014 Ferguson uses his full name, as there was already an actor called Jesse Ferguson in the actors' union when he joined. In September 2012, he announced his engagement to lawyer Justin Mikita, his boyfriend of nearly two years. They married in Manhattan on July 20, 2013, with the playwright and screenwriter Tony Kushner officiating at their wedding. The couple have two chil\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents provide information about the Mercedes-Benz W222 debut and some unrelated marriage (July 20, 2013), but they do not mention any Modern Family star or the spouse they married. Therefore, the necessary facts to answer the question are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 667, + "osl": 361, + "total_tokens": 1028, + "latency_ms": 9468.36, + "tokens_per_second": 38.13 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "81614bee-fb54-42a3-8a7f-d144bb24ace1", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:14:41.027147Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: On the same day that the The Mercedes-Benz W222 arrived at dealerships, a star of the sit-com Modern Family was wed. Who did the star marry?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Robert Lešnik . The W222 has a design theme similar to the C-Class (W205) and E-Class (W213) . In Europe , sales of the S400 Hybrid, S350 BlueTEC, S350 BlueTEC Hybrid, and S500 began in September 2013; and sales of the S550 in the United States also began on that same month. The four-wheel drive ( 4Matic ) model went on sale in November, and additional models, including V12 models and those from AMG , were released in 2014. The W222 S-Class debuted on 15 May 2013, in Hamburg, Germany , and entered production in Sindelfingen , Germany , in June 2013. Production of the W222 ended in September 2020 with the introduction of its successor, the S-Class (W223) . Suspension Mercedes' hydropneumatic , active suspension , known as Active Body Control , was updated wi\n\n[DOC 2] couple's non-profit made a grant of US$10,000 to the ACLU of New Mexico for same sex efforts. In November 2013, a pop-up retail store for 'Tie The Knot' was located at the Los Angeles Beverly Center featuring the fourth collection from the couple, including professional athlete designs with Scott Fujita and Chris Kluwe , and Brittney Griner . Personal life Ferguson with Justin Mikita in 2014 Ferguson uses his full name, as there was already an actor called Jesse Ferguson in the actors' union when he joined. In September 2012, he announced his engagement to lawyer Justin Mikita, his boyfriend of nearly two years. They married in Manhattan on July 20, 2013, with the playwright and screenwriter Tony Kushner officiating at their wedding. The couple have two chil\n\n\nSEARCH HISTORY: - Query: 'Mercedes-Benz W222 dealership arrival' → 1 docs\n- Query: 'Modern Family star wedding 2014' → 5 docs\n- Query: 'Jesse Tyler Ferguson wedding' → 1 docs\n\nFEEDBACK: Need the exact date the W222 arrived at dealerships, then identify a Modern Family cast member who married on that exact date.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Mercedes-Benz W222 dealership arrival date\",\n \"Mercedes-Benz S-Class W222 US launch July 2013\",\n \"Jesse Tyler Ferguson wedding spouse\"\n ],\n \"feedback\": \"Need exact dealership arrival date of the W222 to match the Modern Family star's wedding date, and confirmation of the star's spouse.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 904, + "osl": 480, + "total_tokens": 1384, + "latency_ms": 6809.71, + "tokens_per_second": 70.49 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "4326b696-2b19-48f0-a019-7e45d5c855a6", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T03:14:46.398218Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: On the same day that the The Mercedes-Benz W222 arrived at dealerships, a star of the sit-com Modern Family was wed. Who did the star marry?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] n drama. Personal life Chazelle and Hamilton in 2018 Chazelle married producer Jasmine McGlade in 2010; they divorced in 2014. In October 2017, Chazelle and actress Olivia Hamilton , a Princeton University graduate and former McKinsey & Company consultant, announced their engagement, and they married September 22, 2018. They have a son who was born in November 2019. Their second child was born in December 2022. Chazelle is fluent in French. Filmography Film Year Title Director Writer Producer 2009 Guy and Madeline on a Park Bench Yes Yes Yes 2011 Maria My Love No No Co-producer 2013 The Last Exorcism Part II No Yes No Grand Piano No Yes No 2014 Whiplash Yes Yes No 2016 10 Cloverfield Lane No Yes No La La Land Yes Yes No 2018 First Man Yes No Yes 2022 Babylon\n\n[NEW 2] rst season , then in two episodes of season 2 and episode 8 of season 3 . In season 4 , he was a guest panelist in episodes 3 and 4, on his first appearance performing Robin Thicke 's \" Blurred Lines \" as the \"Robin\" (wearing a cut-out face of Robin Thicke). He went on to be a guest panelist in the third episode of season 5 and episode 7 of season 6 . He was absent in season 7 . He later served as a guest panelist alongside the late Leslie Jordan on episode 7 of season 8 . In April 2025, he is set with Paula Patton for musical indie Reimagined . Personal life McHale married Sarah Williams in July 1996. They have two sons. The family resides in Los Angeles, California , currently in Studio City and previously in Los Feliz . In a 2018 interview on Dax Shepard\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] Robert Lešnik . The W222 has a design theme similar to the C-Class (W205) and E-Class (W213) . In Europe , sales of the S400 Hybrid, S350 BlueTEC, S350 BlueTEC Hybrid, and S500 began in September 2013; and sales of the S550 in the United States also began on that same month. The four-wheel drive ( 4...\n[KEPT 2] couple's non-profit made a grant of US$10,000 to the ACLU of New Mexico for same sex efforts. In November 2013, a pop-up retail store for 'Tie The Knot' was located at the Los Angeles Beverly Center featuring the fourth collection from the couple, including professional athlete designs with Scott Fu...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 2 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 840, + "osl": 518, + "total_tokens": 1358, + "latency_ms": 4783.67, + "tokens_per_second": 108.29 + }, + "context": { + "num_documents_evaluated": 2, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "944818a7-1c6a-42d5-9fc2-164e373e9d51", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:14:50.940396Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: On the same day that the The Mercedes-Benz W222 arrived at dealerships, a star of the sit-com Modern Family was wed. Who did the star marry?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Robert Lešnik . The W222 has a design theme similar to the C-Class (W205) and E-Class (W213) . In Europe , sales of the S400 Hybrid, S350 BlueTEC, S350 BlueTEC Hybrid, and S500 began in September 2013; and sales of the S550 in the United States also began on that same month. The four-wheel drive ( 4Matic ) model went on sale in November, and additional models, including V12 models and those from AMG , were released in 2014. The W222 S-Class debuted on 15 May 2013, in Hamburg, Germany , and entered production in Sindelfingen , Germany , in June 2013. Production of the W222 ended in September 2020 with the introduction of its successor, the S-Class (W223) . Suspension Mercedes' hydropneumatic , active suspension , known as Active Body Control , was updated wi\n\n[DOC 2] couple's non-profit made a grant of US$10,000 to the ACLU of New Mexico for same sex efforts. In November 2013, a pop-up retail store for 'Tie The Knot' was located at the Los Angeles Beverly Center featuring the fourth collection from the couple, including professional athlete designs with Scott Fujita and Chris Kluwe , and Brittney Griner . Personal life Ferguson with Justin Mikita in 2014 Ferguson uses his full name, as there was already an actor called Jesse Ferguson in the actors' union when he joined. In September 2012, he announced his engagement to lawyer Justin Mikita, his boyfriend of nearly two years. They married in Manhattan on July 20, 2013, with the playwright and screenwriter Tony Kushner officiating at their wedding. The couple have two chil\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide details about the debut and sales timeline of the Mercedes-Benz W222, but do not specify the exact day it arrived at dealerships. Additionally, none of the documents mention a Modern Family star's wedding or the identity of the spouse. Therefore, the required facts to answer the question are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 682, + "osl": 298, + "total_tokens": 980, + "latency_ms": 4541.32, + "tokens_per_second": 65.62 + }, + "context": { + "kept_docs_count": 2, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "013c4dc7-b974-42a3-ba9e-e1494103e7f8", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:14:59.767988Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: On the same day that the The Mercedes-Benz W222 arrived at dealerships, a star of the sit-com Modern Family was wed. Who did the star marry?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Robert Lešnik . The W222 has a design theme similar to the C-Class (W205) and E-Class (W213) . In Europe , sales of the S400 Hybrid, S350 BlueTEC, S350 BlueTEC Hybrid, and S500 began in September 2013; and sales of the S550 in the United States also began on that same month. The four-wheel drive ( 4Matic ) model went on sale in November, and additional models, including V12 models and those from AMG , were released in 2014. The W222 S-Class debuted on 15 May 2013, in Hamburg, Germany , and entered production in Sindelfingen , Germany , in June 2013. Production of the W222 ended in September 2020 with the introduction of its successor, the S-Class (W223) . Suspension Mercedes' hydropneumatic , active suspension , known as Active Body Control , was updated wi\n\n[DOC 2] couple's non-profit made a grant of US$10,000 to the ACLU of New Mexico for same sex efforts. In November 2013, a pop-up retail store for 'Tie The Knot' was located at the Los Angeles Beverly Center featuring the fourth collection from the couple, including professional athlete designs with Scott Fujita and Chris Kluwe , and Brittney Griner . Personal life Ferguson with Justin Mikita in 2014 Ferguson uses his full name, as there was already an actor called Jesse Ferguson in the actors' union when he joined. In September 2012, he announced his engagement to lawyer Justin Mikita, his boyfriend of nearly two years. They married in Manhattan on July 20, 2013, with the playwright and screenwriter Tony Kushner officiating at their wedding. The couple have two chil\n\n\nSEARCH HISTORY: - Query: 'Mercedes-Benz W222 dealership arrival' → 1 docs\n- Query: 'Modern Family star wedding 2014' → 5 docs\n- Query: 'Jesse Tyler Ferguson wedding' → 1 docs\n- Query: 'Mercedes-Benz W222 dealership arrival date' → 0 docs\n- Query: 'Mercedes-Benz S-Class W222 US launch July 2013' → 0 docs\n- Query: 'Jesse Tyler Ferguson wedding spouse' → 2 docs\n\nFEEDBACK: Need the exact date the W222 arrived at dealerships, then identify a Modern Family cast member who married on that exact date.\nNeed exact dealership arrival date of the W222 to match the Modern Family star's wedding date, and confirmation of the star's spouse.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Mercedes-Benz W222 launch\",\n \"W222 US deliveries\",\n \"S-Class W222 US arrival\"\n ],\n \"feedback\": \"Need the exact calendar date the W222 first arrived at dealerships (U.S.). This will be matched to the wedding date of a Modern Family star. Prior attempts at \\\"Mercedes-Benz W222 dealership arrival\\\" returned no results, so broader launch/delivery queries are required to locate the specific date.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 932, + "osl": 100, + "total_tokens": 1032, + "latency_ms": 8827.0, + "tokens_per_second": 11.33 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "240af305-37ac-4549-8eca-55af91773665", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T03:15:03.905808Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: On the same day that the The Mercedes-Benz W222 arrived at dealerships, a star of the sit-com Modern Family was wed. Who did the star marry?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] 1927 WAB Wabtec Industrials Construction Machinery & Heavy Transportation Equipment Pittsburgh , Pennsylvania 2019-02-27 0000943452 1999 (1869) WMT Walmart Consumer Staples Consumer Staples Merchandise Retail Bentonville, Arkansas 1982-08-31 0000104169 1962 DIS Walt Disney Company (The) Communication Services Movies & Entertainment Burbank, California 1976-06-30 0001744489 1923 WBD Warner Bros. Discovery Communication Services Broadcasting New York City , New York 2022-04-11 0001437107 2022 ( Warner Bros. 1923) WM Waste Management Industrials Environmental & Facilities Services Houston , Texas 1998-08-31 0000823768 1968 WAT Waters Corporation Health Care Life Sciences Tools & Services Milford, Massachusetts 2002-01-02 0001000697 1958 WEC WEC Energy Group Ut\n\n[NEW 2] – – – – – −200ER – – – 48 50 63 42 55 41 29 22 13 23 19 3 4 3 – 3 4 – −200LR – – – – – – – – – – – – 2 10 11 16 9 6 1 1 3 −300 – – – – 14 17 4 3 6 9 2 4 1 – – – – – – – – −300ER – – – – – – – – – – 10 20 39 53 47 52 40 52 60 79 83 777F – – – – – – – – – – – – – – – 16 22 15 19 14 13 777X – – – – – – – – – – – – – – – – – – – – – All – 13 32 59 74 83 55 61 47 39 36 40 65 83 61 88 74 73 83 98 99 Orders through February 28, 2026 and deliveries Boeing 777 orders and deliveries (cumulative, by year): Orders Deliveries through February 28, 2026 Accidents and incidents A laboratory replication of ice crystals clogging the fuel-oil heat exchanger on a Rolls-Royce Trent 800 engine, from the Air Accidents Investigation Branch (AAIB) report on the British Airways Fligh\n\n[NEW 3] s (Lt. R. B. Cottingham, USNR) Cruiser Division 14 (Rear admiral Wilder D. Baker ) 3 light cruisers Vincennes (Capt. A. D. Brown) Houston (Capt. W. W. Behrens) Miami (Capt. J. G. Crawford) Screen 1 anti-aircraft light cruiser San Diego (Capt. L. J. Hudson) 14 destroyers 6 Fletcher -class (5 × 5 in. main battery): Charles Ausburne , Stanly , Dyson , Converse , Spence , Thatcher 1 Mahan -class (5 × 5 in. main battery): Case 3 Gleaves -class (4 × 5 in. main battery): Lansdowne , Lardner , McCalla 4 Benham -class (4 × 5 in. main battery): Lang , Sterett , Wilson , Ellet Task Group 58.7 Vice Adm. Willis A. Lee Fast battleships Iowa at anchor in Algeria North Carolina underway Destroyer Bagley Before World War II After World War II Commander, Battleships, Pacific\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] Robert Lešnik . The W222 has a design theme similar to the C-Class (W205) and E-Class (W213) . In Europe , sales of the S400 Hybrid, S350 BlueTEC, S350 BlueTEC Hybrid, and S500 began in September 2013; and sales of the S550 in the United States also began on that same month. The four-wheel drive ( 4...\n[KEPT 2] couple's non-profit made a grant of US$10,000 to the ACLU of New Mexico for same sex efforts. In November 2013, a pop-up retail store for 'Tie The Knot' was located at the Los Angeles Beverly Center featuring the fourth collection from the couple, including professional athlete designs with Scott Fu...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 3 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[0,0,0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1210, + "osl": 262, + "total_tokens": 1472, + "latency_ms": 3496.35, + "tokens_per_second": 74.94 + }, + "context": { + "num_documents_evaluated": 3, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "c8f6ffe8-8404-4f8d-ad64-734931ce82f0", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:15:10.855880Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: On the same day that the The Mercedes-Benz W222 arrived at dealerships, a star of the sit-com Modern Family was wed. Who did the star marry?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Robert Lešnik . The W222 has a design theme similar to the C-Class (W205) and E-Class (W213) . In Europe , sales of the S400 Hybrid, S350 BlueTEC, S350 BlueTEC Hybrid, and S500 began in September 2013; and sales of the S550 in the United States also began on that same month. The four-wheel drive ( 4Matic ) model went on sale in November, and additional models, including V12 models and those from AMG , were released in 2014. The W222 S-Class debuted on 15 May 2013, in Hamburg, Germany , and entered production in Sindelfingen , Germany , in June 2013. Production of the W222 ended in September 2020 with the introduction of its successor, the S-Class (W223) . Suspension Mercedes' hydropneumatic , active suspension , known as Active Body Control , was updated wi\n\n[DOC 2] couple's non-profit made a grant of US$10,000 to the ACLU of New Mexico for same sex efforts. In November 2013, a pop-up retail store for 'Tie The Knot' was located at the Los Angeles Beverly Center featuring the fourth collection from the couple, including professional athlete designs with Scott Fujita and Chris Kluwe , and Brittney Griner . Personal life Ferguson with Justin Mikita in 2014 Ferguson uses his full name, as there was already an actor called Jesse Ferguson in the actors' union when he joined. In September 2012, he announced his engagement to lawyer Justin Mikita, his boyfriend of nearly two years. They married in Manhattan on July 20, 2013, with the playwright and screenwriter Tony Kushner officiating at their wedding. The couple have two chil\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents provide the debut date of the Mercedes‑Benz W222 (15 May 2013) but do not specify the exact day it arrived at dealerships. Document 2 mentions a marriage on July 20 2013 (Ferguson and Justin Mikita), which does not correspond to the W222 date and does not identify a Modern Family star. Thus, the required facts to determine which Modern Family star was wed on the same day as the W222 dealership arrival, and the name of their spouse, are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 624, + "osl": 135, + "total_tokens": 759, + "latency_ms": 6949.23, + "tokens_per_second": 19.43 + }, + "context": { + "kept_docs_count": 2, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "fac3b42b-61b6-461d-9977-649e549ceba8", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:15:16.535343Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: On the same day that the The Mercedes-Benz W222 arrived at dealerships, a star of the sit-com Modern Family was wed. Who did the star marry?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Robert Lešnik . The W222 has a design theme similar to the C-Class (W205) and E-Class (W213) . In Europe , sales of the S400 Hybrid, S350 BlueTEC, S350 BlueTEC Hybrid, and S500 began in September 2013; and sales of the S550 in the United States also began on that same month. The four-wheel drive ( 4Matic ) model went on sale in November, and additional models, including V12 models and those from AMG , were released in 2014. The W222 S-Class debuted on 15 May 2013, in Hamburg, Germany , and entered production in Sindelfingen , Germany , in June 2013. Production of the W222 ended in September 2020 with the introduction of its successor, the S-Class (W223) . Suspension Mercedes' hydropneumatic , active suspension , known as Active Body Control , was updated wi\n\n[DOC 2] couple's non-profit made a grant of US$10,000 to the ACLU of New Mexico for same sex efforts. In November 2013, a pop-up retail store for 'Tie The Knot' was located at the Los Angeles Beverly Center featuring the fourth collection from the couple, including professional athlete designs with Scott Fujita and Chris Kluwe , and Brittney Griner . Personal life Ferguson with Justin Mikita in 2014 Ferguson uses his full name, as there was already an actor called Jesse Ferguson in the actors' union when he joined. In September 2012, he announced his engagement to lawyer Justin Mikita, his boyfriend of nearly two years. They married in Manhattan on July 20, 2013, with the playwright and screenwriter Tony Kushner officiating at their wedding. The couple have two chil\n\n\nSEARCH HISTORY: - Query: 'Mercedes-Benz W222 dealership arrival' → 1 docs\n- Query: 'Modern Family star wedding 2014' → 5 docs\n- Query: 'Jesse Tyler Ferguson wedding' → 1 docs\n- Query: 'Mercedes-Benz W222 dealership arrival date' → 0 docs\n- Query: 'Mercedes-Benz S-Class W222 US launch July 2013' → 0 docs\n- Query: 'Jesse Tyler Ferguson wedding spouse' → 2 docs\n- Query: 'Mercedes-Benz W222 launch' → 0 docs\n- Query: 'W222 US deliveries' → 2 docs\n- Query: 'S-Class W222 US arrival' → 1 docs\n\nFEEDBACK: Need the exact date the W222 arrived at dealerships, then identify a Modern Family cast member who married on that exact date.\nNeed exact dealership arrival date of the W222 to match the Modern Family star's wedding date, and confirmation of the star's spouse.\nNeed the exact calendar date the W222 first arrived at dealerships (U.S.). This will be matched to the wedding date of a Modern Family star. Prior attempts at \"Mercedes-Benz W222 dealership arrival\" returned no results, so broader launch/delivery queries are required to locate the specific date.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Mercedes-Benz W222 US dealership date\",\n \"Mercedes-Benz S-Class W222 US launch July 2013\",\n \"Jesse Tyler Ferguson wedding date\"\n ],\n \"feedback\": \"Need the exact calendar date the W222 first arrived at U.S. dealerships and confirm which Modern Family star married on that same date (including spouse name).\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1039, + "osl": 83, + "total_tokens": 1122, + "latency_ms": 5678.87, + "tokens_per_second": 14.62 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "b0281904-7b39-4b4d-a9c7-bd7b2fb1bc05", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T03:15:19.678975Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: On the same day that the The Mercedes-Benz W222 arrived at dealerships, a star of the sit-com Modern Family was wed. Who did the star marry?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] vember 12, 1790 – September 10, 1842 (aged 51) April 4, 1841 – September 10, 1842 † 50 years, 143 days John Tyler m. ( 1813-03-29 ) March 29, 1813 Priscilla Tyler June 14, 1816 – December 29, 1889 (aged 73) September 10, 1842 – June 26, 1844 26 years, 88 days John Tyler Father-in-law Julia Tyler May 4, 1820 – July 10, 1889 (aged 69) June 26, 1844 – March 4, 1845 24 years, 53 days John Tyler m. June 26, 1844 Married while president 11 Sarah Polk September 4, 1803 – August 14, 1891 (aged 87) March 4, 1845 – March 4, 1849 41 years, 181 days James K. Polk m. January 1, 1824 12 Margaret \"Peggy\" Taylor September 21, 1788 – August 14, 1852 (aged 63) March 4, 1849 – July 9, 1850 60 years, 164 days Zachary Taylor m. June 21, 1810 13 Abigail Fillmore March 13, 1798 –\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] Robert Lešnik . The W222 has a design theme similar to the C-Class (W205) and E-Class (W213) . In Europe , sales of the S400 Hybrid, S350 BlueTEC, S350 BlueTEC Hybrid, and S500 began in September 2013; and sales of the S550 in the United States also began on that same month. The four-wheel drive ( 4...\n[KEPT 2] couple's non-profit made a grant of US$10,000 to the ACLU of New Mexico for same sex efforts. In November 2013, a pop-up retail store for 'Tie The Knot' was located at the Los Angeles Beverly Center featuring the fourth collection from the couple, including professional athlete designs with Scott Fu...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 735, + "osl": 193, + "total_tokens": 928, + "latency_ms": 2556.08, + "tokens_per_second": 75.51 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "1dee7c1d-0021-4bc6-b6f1-4e5638f2f4b5", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:15:29.217497Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: On the same day that the The Mercedes-Benz W222 arrived at dealerships, a star of the sit-com Modern Family was wed. Who did the star marry?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Robert Lešnik . The W222 has a design theme similar to the C-Class (W205) and E-Class (W213) . In Europe , sales of the S400 Hybrid, S350 BlueTEC, S350 BlueTEC Hybrid, and S500 began in September 2013; and sales of the S550 in the United States also began on that same month. The four-wheel drive ( 4Matic ) model went on sale in November, and additional models, including V12 models and those from AMG , were released in 2014. The W222 S-Class debuted on 15 May 2013, in Hamburg, Germany , and entered production in Sindelfingen , Germany , in June 2013. Production of the W222 ended in September 2020 with the introduction of its successor, the S-Class (W223) . Suspension Mercedes' hydropneumatic , active suspension , known as Active Body Control , was updated wi\n\n[DOC 2] couple's non-profit made a grant of US$10,000 to the ACLU of New Mexico for same sex efforts. In November 2013, a pop-up retail store for 'Tie The Knot' was located at the Los Angeles Beverly Center featuring the fourth collection from the couple, including professional athlete designs with Scott Fujita and Chris Kluwe , and Brittney Griner . Personal life Ferguson with Justin Mikita in 2014 Ferguson uses his full name, as there was already an actor called Jesse Ferguson in the actors' union when he joined. In September 2012, he announced his engagement to lawyer Justin Mikita, his boyfriend of nearly two years. They married in Manhattan on July 20, 2013, with the playwright and screenwriter Tony Kushner officiating at their wedding. The couple have two chil\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"sufficient\": false, \"reasoning\": \"The documents do not provide the date the Mercedes-Benz W222 arrived at dealerships, nor do they mention a Modern Family star's wedding or the spouse's name. Therefore the required facts to answer the question are missing.\"}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 667, + "osl": 380, + "total_tokens": 1047, + "latency_ms": 9537.74, + "tokens_per_second": 39.84 + }, + "context": { + "kept_docs_count": 2, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "b2413d10-f551-4fea-a1a7-d38e2ca2aa45", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:15:37.352553Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: On the same day that the The Mercedes-Benz W222 arrived at dealerships, a star of the sit-com Modern Family was wed. Who did the star marry?\n\nDOCUMENTS:\n\n[DOC 1] Robert Lešnik . The W222 has a design theme similar to the C-Class (W205) and E-Class (W213) . In Europe , sales of the S400 Hybrid, S350 BlueTEC, S350 BlueTEC Hybrid, and S500 began in September 2013; and sales of the S550 in the United States also began on that same month. The four-wheel drive ( 4Matic ) model went on sale in November, and additional models, including V12 models and those from AMG , were released in 2014. The W222 S-Class debuted on 15 May 2013, in Hamburg, Germany , and entered production in Sindelfingen , Germany , in June 2013. Production of the W222 ended in September 2020 with the introduction of its successor, the S-Class (W223) . Suspension Mercedes' hydropneumatic , active suspension , known as Active Body Control , was updated wi\n\n[DOC 2] couple's non-profit made a grant of US$10,000 to the ACLU of New Mexico for same sex efforts. In November 2013, a pop-up retail store for 'Tie The Knot' was located at the Los Angeles Beverly Center featuring the fourth collection from the couple, including professional athlete designs with Scott Fujita and Chris Kluwe , and Brittney Griner . Personal life Ferguson with Justin Mikita in 2014 Ferguson uses his full name, as there was already an actor called Jesse Ferguson in the actors' union when he joined. In September 2012, he announced his engagement to lawyer Justin Mikita, his boyfriend of nearly two years. They married in Manhattan on July 20, 2013, with the playwright and screenwriter Tony Kushner officiating at their wedding. The couple have two chil\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 521, + "osl": 276, + "total_tokens": 797, + "latency_ms": 8134.39, + "tokens_per_second": 33.93 + }, + "context": { + "num_documents_used": 2, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:15:37.353435Z", + "summary": { + "total_llm_calls": 13, + "total_hops": 5, + "total_input_tokens": 10963, + "total_output_tokens": 7067, + "total_tokens": 18030, + "total_latency_ms": 130386.2, + "average_tokens_per_second": 53.41, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Mercedes-Benz_S-Class_(W222)", + "https://en.wikipedia.org/wiki/Modern_Family", + "https://en.wikipedia.org/wiki/Jesse_Tyler_Ferguson" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "Justin Mikita" + } + }, + { + "query_id": "38", + "original_query": "Which species from the genus mulona are both found in the same country?", + "timestamp_start": "2026-05-16T03:15:37.384488Z", + "llm_calls": [ + { + "call_id": "9218f412-b23b-4e64-86e4-a25a67f1cab6", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:15:40.484713Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: Which species from the genus mulona are both found in the same country?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Mulona genus\",\n \"Mulona species\",\n \"Mulona distribution\"\n ],\n \"feedback\": \"Need to locate the Wikipedia (or other reliable) page for the genus Mulona to obtain a full species list and their geographic ranges. Initial attempts should focus on retrieving the genus overview and a compiled species list, then distribution details for each species.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 390, + "osl": 81, + "total_tokens": 471, + "latency_ms": 3100.06, + "tokens_per_second": 26.13 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "588c8f41-06a3-4e4b-be7c-31c291888fef", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:15:49.526390Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: Which species from the genus mulona are both found in the same country?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Genus of moths Mulona Mulona grisea Scientific classification Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Lepidoptera Superfamily: Noctuoidea Family: Erebidae Subfamily: Arctiinae Tribe: Lithosiini Genus: Mulona Walker , 1866 Mulona is a genus of moths in the subfamily Arctiinae . The genus was erected by Francis Walker in 1866. Species Mulona barnesi Field, 1952 Mulona grisea Hampson, 1900 Mulona lapidaria Walker, 1866 Mulona manni Field, 1952 Mulona phelina (Druce, 1885) Mulona piperita Reich, 1933 Mulona schausi Field, 1952 References ^ ^ This Lithosiini -related article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[NEW 2] Species of moth Mulona schausi Scientific classification Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Lepidoptera Superfamily: Noctuoidea Family: Erebidae Subfamily: Arctiinae Genus: Mulona Species: M. schausi Binomial name Mulona schausi Field, 1952 Mulona schausi is a moth of the subfamily Arctiinae first described by William Dewitt Field in 1952. It is found on Cuba . References ^ ^ This Lithosiini -related article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[NEW 3] Species of moth Mulona piperita Scientific classification Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Lepidoptera Superfamily: Noctuoidea Family: Erebidae Subfamily: Arctiinae Genus: Mulona Species: M. piperita Binomial name Mulona piperita Reich, 1933 Mulona piperita is a moth of the subfamily Arctiinae first described by Reich in 1933. It is found in Brazil . References ^ ^ This Lithosiini -related article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[NEW 4] Species of moth Mulona barnesi Scientific classification Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Lepidoptera Superfamily: Noctuoidea Family: Erebidae Subfamily: Arctiinae Genus: Mulona Species: M. barnesi Binomial name Mulona barnesi Field, 1952 Mulona barnesi is a moth of the subfamily Arctiinae first described by William Dewitt Field in 1952. It is found on Cuba . References ^ ^ This Lithosiini -related article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[NEW 5] Species of insect Mulona lapidaria Scientific classification Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Lepidoptera Superfamily: Noctuoidea Family: Erebidae Subfamily: Arctiinae Genus: Mulona Species: M. lapidaria Binomial name Mulona lapidaria Walker , 1866 Mulona lapidaria is a moth of the subfamily Arctiinae . It was described by Francis Walker in 1866. It is found on Haiti . References ^ This Lithosiini -related article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[NEW 6] Species of moth Mulona grisea Scientific classification Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Lepidoptera Superfamily: Noctuoidea Family: Erebidae Subfamily: Arctiinae Genus: Mulona Species: M. grisea Binomial name Mulona grisea Hampson , 1900 Mulona grisea is a moth of the subfamily Arctiinae first described by George Hampson in 1900. It is found on Jamaica . References ^ Moths of Jamaica ^ This Lithosiini -related article is a stub . You can help Wikipedia by adding missing information . v t e\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 6 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 1, 1, 1, 1, 1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1156, + "osl": 545, + "total_tokens": 1701, + "latency_ms": 8408.27, + "tokens_per_second": 64.82 + }, + "context": { + "num_documents_evaluated": 6, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "ff006383-670a-4485-a91b-3439cddd4391", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:15:52.289266Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: Which species from the genus mulona are both found in the same country?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Genus of moths Mulona Mulona grisea Scientific classification Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Lepidoptera Superfamily: Noctuoidea Family: Erebidae Subfamily: Arctiinae Tribe: Lithosiini Genus: Mulona Walker , 1866 Mulona is a genus of moths in the subfamily Arctiinae . The genus was erected by Francis Walker in 1866. Species Mulona barnesi Field, 1952 Mulona grisea Hampson, 1900 Mulona lapidaria Walker, 1866 Mulona manni Field, 1952 Mulona phelina (Druce, 1885) Mulona piperita Reich, 1933 Mulona schausi Field, 1952 References ^ ^ This Lithosiini -related article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 2] Species of moth Mulona schausi Scientific classification Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Lepidoptera Superfamily: Noctuoidea Family: Erebidae Subfamily: Arctiinae Genus: Mulona Species: M. schausi Binomial name Mulona schausi Field, 1952 Mulona schausi is a moth of the subfamily Arctiinae first described by William Dewitt Field in 1952. It is found on Cuba . References ^ ^ This Lithosiini -related article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 3] Species of moth Mulona piperita Scientific classification Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Lepidoptera Superfamily: Noctuoidea Family: Erebidae Subfamily: Arctiinae Genus: Mulona Species: M. piperita Binomial name Mulona piperita Reich, 1933 Mulona piperita is a moth of the subfamily Arctiinae first described by Reich in 1933. It is found in Brazil . References ^ ^ This Lithosiini -related article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 4] Species of moth Mulona barnesi Scientific classification Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Lepidoptera Superfamily: Noctuoidea Family: Erebidae Subfamily: Arctiinae Genus: Mulona Species: M. barnesi Binomial name Mulona barnesi Field, 1952 Mulona barnesi is a moth of the subfamily Arctiinae first described by William Dewitt Field in 1952. It is found on Cuba . References ^ ^ This Lithosiini -related article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 5] Species of insect Mulona lapidaria Scientific classification Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Lepidoptera Superfamily: Noctuoidea Family: Erebidae Subfamily: Arctiinae Genus: Mulona Species: M. lapidaria Binomial name Mulona lapidaria Walker , 1866 Mulona lapidaria is a moth of the subfamily Arctiinae . It was described by Francis Walker in 1866. It is found on Haiti . References ^ This Lithosiini -related article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 6] Species of moth Mulona grisea Scientific classification Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Lepidoptera Superfamily: Noctuoidea Family: Erebidae Subfamily: Arctiinae Genus: Mulona Species: M. grisea Binomial name Mulona grisea Hampson , 1900 Mulona grisea is a moth of the subfamily Arctiinae first described by George Hampson in 1900. It is found on Jamaica . References ^ Moths of Jamaica ^ This Lithosiini -related article is a stub . You can help Wikipedia by adding missing information . v t e\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": true,\n \"reasoning\": \"Documents provide locations for each Mulona species: Mulona schausi (Cuba) and Mulona barnesi (Cuba) share the same country. No other species share a country based on the given documents. Thus we can answer which species are both found in the same country.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1205, + "osl": 276, + "total_tokens": 1481, + "latency_ms": 2762.08, + "tokens_per_second": 99.92 + }, + "context": { + "kept_docs_count": 6, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "9474e1e6-dbce-4b15-8d51-3410e2c0dff8", + "component": "answer_generator", + "hop_count": 2, + "timestamp": "2026-05-16T03:15:55.994359Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: Which species from the genus mulona are both found in the same country?\n\nDOCUMENTS:\n\n[DOC 1] Genus of moths Mulona Mulona grisea Scientific classification Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Lepidoptera Superfamily: Noctuoidea Family: Erebidae Subfamily: Arctiinae Tribe: Lithosiini Genus: Mulona Walker , 1866 Mulona is a genus of moths in the subfamily Arctiinae . The genus was erected by Francis Walker in 1866. Species Mulona barnesi Field, 1952 Mulona grisea Hampson, 1900 Mulona lapidaria Walker, 1866 Mulona manni Field, 1952 Mulona phelina (Druce, 1885) Mulona piperita Reich, 1933 Mulona schausi Field, 1952 References ^ ^ This Lithosiini -related article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 2] Species of moth Mulona schausi Scientific classification Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Lepidoptera Superfamily: Noctuoidea Family: Erebidae Subfamily: Arctiinae Genus: Mulona Species: M. schausi Binomial name Mulona schausi Field, 1952 Mulona schausi is a moth of the subfamily Arctiinae first described by William Dewitt Field in 1952. It is found on Cuba . References ^ ^ This Lithosiini -related article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 3] Species of moth Mulona piperita Scientific classification Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Lepidoptera Superfamily: Noctuoidea Family: Erebidae Subfamily: Arctiinae Genus: Mulona Species: M. piperita Binomial name Mulona piperita Reich, 1933 Mulona piperita is a moth of the subfamily Arctiinae first described by Reich in 1933. It is found in Brazil . References ^ ^ This Lithosiini -related article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 4] Species of moth Mulona barnesi Scientific classification Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Lepidoptera Superfamily: Noctuoidea Family: Erebidae Subfamily: Arctiinae Genus: Mulona Species: M. barnesi Binomial name Mulona barnesi Field, 1952 Mulona barnesi is a moth of the subfamily Arctiinae first described by William Dewitt Field in 1952. It is found on Cuba . References ^ ^ This Lithosiini -related article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 5] Species of insect Mulona lapidaria Scientific classification Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Lepidoptera Superfamily: Noctuoidea Family: Erebidae Subfamily: Arctiinae Genus: Mulona Species: M. lapidaria Binomial name Mulona lapidaria Walker , 1866 Mulona lapidaria is a moth of the subfamily Arctiinae . It was described by Francis Walker in 1866. It is found on Haiti . References ^ This Lithosiini -related article is a stub . You can help Wikipedia by adding missing information . v t e\n\n[DOC 6] Species of moth Mulona grisea Scientific classification Kingdom: Animalia Phylum: Arthropoda Class: Insecta Order: Lepidoptera Superfamily: Noctuoidea Family: Erebidae Subfamily: Arctiinae Genus: Mulona Species: M. grisea Binomial name Mulona grisea Hampson , 1900 Mulona grisea is a moth of the subfamily Arctiinae first described by George Hampson in 1900. It is found on Jamaica . References ^ Moths of Jamaica ^ This Lithosiini -related article is a stub . You can help Wikipedia by adding missing information . v t e\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Mulona schausi and Mulona barnesi – both are found in Cuba.", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1048, + "osl": 163, + "total_tokens": 1211, + "latency_ms": 3704.42, + "tokens_per_second": 44.0 + }, + "context": { + "num_documents_used": 6, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:15:55.995032Z", + "summary": { + "total_llm_calls": 4, + "total_hops": 2, + "total_input_tokens": 3799, + "total_output_tokens": 1065, + "total_tokens": 4864, + "total_latency_ms": 17974.83, + "average_tokens_per_second": 58.72, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Mulona", + "https://en.wikipedia.org/wiki/Mulona_barnesi", + "https://en.wikipedia.org/wiki/Mulona_grisea", + "https://en.wikipedia.org/wiki/Mulona_lapidaria", + "https://en.wikipedia.org/wiki/Mulona_manni", + "https://en.wikipedia.org/wiki/Mulona_piperita", + "https://en.wikipedia.org/wiki/Mulona_schausi" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Mulona schausi and Mulona barnesi – both are found in Cuba.", + "ground_truth_answer": "Mulona barnesi and mulona schausi" + } + }, + { + "query_id": "39", + "original_query": "As of July 1, 2024, if I wanted to give my daughter the middle name of the American woman who is the most decorated female in the history of American gymnastics as her first name and the full first name of the American woman who holds the world record in the 800-meter freestyle as her middle name, what would I name my daughter? ", + "timestamp_start": "2026-05-16T03:15:56.026880Z", + "llm_calls": [ + { + "call_id": "130ea89e-30b8-465e-8772-cadbbf0ca840", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:16:05.471594Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of July 1, 2024, if I wanted to give my daughter the middle name of the American woman who is the most decorated female in the history of American gymnastics as her first name and the full first name of the American woman who holds the world record in the 800-meter freestyle as her middle name, what would I name my daughter? \n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Simone Biles middle name\",\n \"Katie Ledecky 800m freestyle world record\",\n \"women 800 meter freestyle world record\"\n ],\n \"feedback\": \"Need Simone Biles' middle name (Arianne) and confirmation that Katie Ledecky currently holds the women's 800‑meter freestyle world record.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 450, + "osl": 84, + "total_tokens": 534, + "latency_ms": 9444.58, + "tokens_per_second": 8.89 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "25ce36fd-83cf-4374-9665-c57f30418da7", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:16:13.158848Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of July 1, 2024, if I wanted to give my daughter the middle name of the American woman who is the most decorated female in the history of American gymnastics as her first name and the full first name of the American woman who holds the world record in the 800-meter freestyle as her middle name, what would I name my daughter? \n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] couple formally adopted Simone and her younger sister Adria. Ron's sister, Shanon's aunt Harriet, adopted the two oldest children. Simone holds Belizean citizenship through her adoptive mother and considers Belize to be her second home. Biles and her family are Catholic . Biles attended Benfer Elementary School in Harris County , Texas. In 2012, Biles switched from public school to home schooling , allowing her to increase her training from about 20 to 32 hours a week. She earned her high-school diploma in mid-2015. Biles verbally committed to UCLA on August 4, 2014, and signed a National Letter of Intent in November 2014, planning to defer enrollment until after the 2016 Summer Olympics in Rio de Janeiro. Instead, on July 29, 2015, she announced that she w\n\n[NEW 2] ss Athlete of the Year 2017 2022 Succeeded by Serena Williams Simone Biles Portals : Biography Sports Olympic Games .\n\n[NEW 3] n the 400, 800, and 1500 m freestyle events Emma McKeon holds Olympic records in one individual and one relay event. ♦ denotes a performance that is also a current world record . Statistics are correct as of the end of the 2024 Olympics and include only those events which are currently recognised by the IOC as Olympic events. Event Time Name Nation Games Date Ref 50 m freestyle 23.66 Sarah Sjöström Sweden 2024 Paris 3 August 2024 100 m freestyle 51.96 Emma McKeon Australia 2020 Tokyo 30 July 2021 200 m freestyle 1:53.27 Mollie O'Callaghan Australia 2024 Paris 29 July 2024 400 m freestyle 3:56.46 Katie Ledecky United States 2016 Rio de Janeiro 7 August 2016 800 m freestyle 8:04.79 Katie Ledecky United States 2016 Rio de Janeiro 12 August 2016 1500 m freestyle\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 3 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 1, 1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 737, + "osl": 412, + "total_tokens": 1149, + "latency_ms": 7160.67, + "tokens_per_second": 57.54 + }, + "context": { + "num_documents_evaluated": 3, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "4c48034a-cf2d-482a-8313-5fcf8836221f", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:16:28.526995Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of July 1, 2024, if I wanted to give my daughter the middle name of the American woman who is the most decorated female in the history of American gymnastics as her first name and the full first name of the American woman who holds the world record in the 800-meter freestyle as her middle name, what would I name my daughter? \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] couple formally adopted Simone and her younger sister Adria. Ron's sister, Shanon's aunt Harriet, adopted the two oldest children. Simone holds Belizean citizenship through her adoptive mother and considers Belize to be her second home. Biles and her family are Catholic . Biles attended Benfer Elementary School in Harris County , Texas. In 2012, Biles switched from public school to home schooling , allowing her to increase her training from about 20 to 32 hours a week. She earned her high-school diploma in mid-2015. Biles verbally committed to UCLA on August 4, 2014, and signed a National Letter of Intent in November 2014, planning to defer enrollment until after the 2016 Summer Olympics in Rio de Janeiro. Instead, on July 29, 2015, she announced that she w\n\n[DOC 2] ss Athlete of the Year 2017 2022 Succeeded by Serena Williams Simone Biles Portals : Biography Sports Olympic Games .\n\n[DOC 3] n the 400, 800, and 1500 m freestyle events Emma McKeon holds Olympic records in one individual and one relay event. ♦ denotes a performance that is also a current world record . Statistics are correct as of the end of the 2024 Olympics and include only those events which are currently recognised by the IOC as Olympic events. Event Time Name Nation Games Date Ref 50 m freestyle 23.66 Sarah Sjöström Sweden 2024 Paris 3 August 2024 100 m freestyle 51.96 Emma McKeon Australia 2020 Tokyo 30 July 2021 200 m freestyle 1:53.27 Mollie O'Callaghan Australia 2024 Paris 29 July 2024 400 m freestyle 3:56.46 Katie Ledecky United States 2016 Rio de Janeiro 7 August 2016 800 m freestyle 8:04.79 Katie Ledecky United States 2016 Rio de Janeiro 12 August 2016 1500 m freestyle\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide the identity of the American gymnast (Simone Biles) and the American swimmer (Katie Ledecky) and confirm Ledecky's world record in the 800‑meter freestyle. However, none of the kept documents contain Simone Biles' middle name, which is required to form the daughter's first name. Therefore, the necessary information is incomplete.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 771, + "osl": 552, + "total_tokens": 1323, + "latency_ms": 15367.32, + "tokens_per_second": 35.92 + }, + "context": { + "kept_docs_count": 3, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "413bf25b-f3d1-4838-bdba-4dc36041ba55", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:16:32.872188Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of July 1, 2024, if I wanted to give my daughter the middle name of the American woman who is the most decorated female in the history of American gymnastics as her first name and the full first name of the American woman who holds the world record in the 800-meter freestyle as her middle name, what would I name my daughter? \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] couple formally adopted Simone and her younger sister Adria. Ron's sister, Shanon's aunt Harriet, adopted the two oldest children. Simone holds Belizean citizenship through her adoptive mother and considers Belize to be her second home. Biles and her family are Catholic . Biles attended Benfer Elementary School in Harris County , Texas. In 2012, Biles switched from public school to home schooling , allowing her to increase her training from about 20 to 32 hours a week. She earned her high-school diploma in mid-2015. Biles verbally committed to UCLA on August 4, 2014, and signed a National Letter of Intent in November 2014, planning to defer enrollment until after the 2016 Summer Olympics in Rio de Janeiro. Instead, on July 29, 2015, she announced that she w\n\n[DOC 2] ss Athlete of the Year 2017 2022 Succeeded by Serena Williams Simone Biles Portals : Biography Sports Olympic Games .\n\n[DOC 3] n the 400, 800, and 1500 m freestyle events Emma McKeon holds Olympic records in one individual and one relay event. ♦ denotes a performance that is also a current world record . Statistics are correct as of the end of the 2024 Olympics and include only those events which are currently recognised by the IOC as Olympic events. Event Time Name Nation Games Date Ref 50 m freestyle 23.66 Sarah Sjöström Sweden 2024 Paris 3 August 2024 100 m freestyle 51.96 Emma McKeon Australia 2020 Tokyo 30 July 2021 200 m freestyle 1:53.27 Mollie O'Callaghan Australia 2024 Paris 29 July 2024 400 m freestyle 3:56.46 Katie Ledecky United States 2016 Rio de Janeiro 7 August 2016 800 m freestyle 8:04.79 Katie Ledecky United States 2016 Rio de Janeiro 12 August 2016 1500 m freestyle\n\n\nSEARCH HISTORY: - Query: 'Simone Biles middle name' → 2 docs\n- Query: 'Katie Ledecky 800m freestyle world record' → 0 docs\n- Query: 'women 800 meter freestyle world record' → 1 docs\n\nFEEDBACK: Need Simone Biles' middle name (Arianne) and confirmation that Katie Ledecky currently holds the women's 800‑meter freestyle world record.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Simone Biles full name\",\n \"Katie Ledecky 800 freestyle world record\",\n \"women's 800 m freestyle world record\"\n ],\n \"feedback\": \"Need Simone Biles' middle name (Arianne) and confirmation that Katie Ledecky holds the women's 800‑meter freestyle world record as of July 1 2024.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 970, + "osl": 95, + "total_tokens": 1065, + "latency_ms": 4344.61, + "tokens_per_second": 21.87 + }, + "context": { + "kept_docs_count": 3, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "a82599fc-2ffb-4274-be18-5dd128f1b0ca", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:16:52.675559Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of July 1, 2024, if I wanted to give my daughter the middle name of the American woman who is the most decorated female in the history of American gymnastics as her first name and the full first name of the American woman who holds the world record in the 800-meter freestyle as her middle name, what would I name my daughter? \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] couple formally adopted Simone and her younger sister Adria. Ron's sister, Shanon's aunt Harriet, adopted the two oldest children. Simone holds Belizean citizenship through her adoptive mother and considers Belize to be her second home. Biles and her family are Catholic . Biles attended Benfer Elementary School in Harris County , Texas. In 2012, Biles switched from public school to home schooling , allowing her to increase her training from about 20 to 32 hours a week. She earned her high-school diploma in mid-2015. Biles verbally committed to UCLA on August 4, 2014, and signed a National Letter of Intent in November 2014, planning to defer enrollment until after the 2016 Summer Olympics in Rio de Janeiro. Instead, on July 29, 2015, she announced that she w\n\n[DOC 2] ss Athlete of the Year 2017 2022 Succeeded by Serena Williams Simone Biles Portals : Biography Sports Olympic Games .\n\n[DOC 3] n the 400, 800, and 1500 m freestyle events Emma McKeon holds Olympic records in one individual and one relay event. ♦ denotes a performance that is also a current world record . Statistics are correct as of the end of the 2024 Olympics and include only those events which are currently recognised by the IOC as Olympic events. Event Time Name Nation Games Date Ref 50 m freestyle 23.66 Sarah Sjöström Sweden 2024 Paris 3 August 2024 100 m freestyle 51.96 Emma McKeon Australia 2020 Tokyo 30 July 2021 200 m freestyle 1:53.27 Mollie O'Callaghan Australia 2024 Paris 29 July 2024 400 m freestyle 3:56.46 Katie Ledecky United States 2016 Rio de Janeiro 7 August 2016 800 m freestyle 8:04.79 Katie Ledecky United States 2016 Rio de Janeiro 12 August 2016 1500 m freestyle\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents identify Simone Biles as the most decorated American gymnast and Katie Ledecky as the 800m freestyle record holder, but they do not provide Simone Biles' middle name (Arianne) nor explicitly state that Katie Ledecky holds the world record. Therefore, the required facts to construct the daughter's name are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 760, + "osl": 692, + "total_tokens": 1452, + "latency_ms": 19271.12, + "tokens_per_second": 35.91 + }, + "context": { + "kept_docs_count": 3, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "3dc891f7-6f82-43ec-acdd-7c24e504be34", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:17:11.071283Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of July 1, 2024, if I wanted to give my daughter the middle name of the American woman who is the most decorated female in the history of American gymnastics as her first name and the full first name of the American woman who holds the world record in the 800-meter freestyle as her middle name, what would I name my daughter? \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] couple formally adopted Simone and her younger sister Adria. Ron's sister, Shanon's aunt Harriet, adopted the two oldest children. Simone holds Belizean citizenship through her adoptive mother and considers Belize to be her second home. Biles and her family are Catholic . Biles attended Benfer Elementary School in Harris County , Texas. In 2012, Biles switched from public school to home schooling , allowing her to increase her training from about 20 to 32 hours a week. She earned her high-school diploma in mid-2015. Biles verbally committed to UCLA on August 4, 2014, and signed a National Letter of Intent in November 2014, planning to defer enrollment until after the 2016 Summer Olympics in Rio de Janeiro. Instead, on July 29, 2015, she announced that she w\n\n[DOC 2] ss Athlete of the Year 2017 2022 Succeeded by Serena Williams Simone Biles Portals : Biography Sports Olympic Games .\n\n[DOC 3] n the 400, 800, and 1500 m freestyle events Emma McKeon holds Olympic records in one individual and one relay event. ♦ denotes a performance that is also a current world record . Statistics are correct as of the end of the 2024 Olympics and include only those events which are currently recognised by the IOC as Olympic events. Event Time Name Nation Games Date Ref 50 m freestyle 23.66 Sarah Sjöström Sweden 2024 Paris 3 August 2024 100 m freestyle 51.96 Emma McKeon Australia 2020 Tokyo 30 July 2021 200 m freestyle 1:53.27 Mollie O'Callaghan Australia 2024 Paris 29 July 2024 400 m freestyle 3:56.46 Katie Ledecky United States 2016 Rio de Janeiro 7 August 2016 800 m freestyle 8:04.79 Katie Ledecky United States 2016 Rio de Janeiro 12 August 2016 1500 m freestyle\n\n\nSEARCH HISTORY: - Query: 'Simone Biles middle name' → 2 docs\n- Query: 'Katie Ledecky 800m freestyle world record' → 0 docs\n- Query: 'women 800 meter freestyle world record' → 1 docs\n- Query: 'Simone Biles full name' → 0 docs\n- Query: 'Katie Ledecky 800 freestyle world record' → 0 docs\n- Query: 'women's 800 m freestyle world record' → 0 docs\n\nFEEDBACK: Need Simone Biles' middle name (Arianne) and confirmation that Katie Ledecky currently holds the women's 800‑meter freestyle world record.\nNeed Simone Biles' middle name (Arianne) and confirmation that Katie Ledecky holds the women's 800‑meter freestyle world record as of July 1 2024.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Simone Biles biography\",\n \"Simone Arianne Biles\",\n \"Katie Ledecky 800 m freestyle world record\"\n ],\n \"feedback\": \"Needed Simone Biles' middle name (Arianne) and confirmation that Katie Ledecky currently holds the women's 800‑meter freestyle world record as of July 1 2024.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1100, + "osl": 490, + "total_tokens": 1590, + "latency_ms": 18394.77, + "tokens_per_second": 26.64 + }, + "context": { + "kept_docs_count": 3, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "67c7baec-eebd-447c-bcba-d86909a24b9c", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:17:20.089098Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of July 1, 2024, if I wanted to give my daughter the middle name of the American woman who is the most decorated female in the history of American gymnastics as her first name and the full first name of the American woman who holds the world record in the 800-meter freestyle as her middle name, what would I name my daughter? \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] couple formally adopted Simone and her younger sister Adria. Ron's sister, Shanon's aunt Harriet, adopted the two oldest children. Simone holds Belizean citizenship through her adoptive mother and considers Belize to be her second home. Biles and her family are Catholic . Biles attended Benfer Elementary School in Harris County , Texas. In 2012, Biles switched from public school to home schooling , allowing her to increase her training from about 20 to 32 hours a week. She earned her high-school diploma in mid-2015. Biles verbally committed to UCLA on August 4, 2014, and signed a National Letter of Intent in November 2014, planning to defer enrollment until after the 2016 Summer Olympics in Rio de Janeiro. Instead, on July 29, 2015, she announced that she w\n\n[DOC 2] ss Athlete of the Year 2017 2022 Succeeded by Serena Williams Simone Biles Portals : Biography Sports Olympic Games .\n\n[DOC 3] n the 400, 800, and 1500 m freestyle events Emma McKeon holds Olympic records in one individual and one relay event. ♦ denotes a performance that is also a current world record . Statistics are correct as of the end of the 2024 Olympics and include only those events which are currently recognised by the IOC as Olympic events. Event Time Name Nation Games Date Ref 50 m freestyle 23.66 Sarah Sjöström Sweden 2024 Paris 3 August 2024 100 m freestyle 51.96 Emma McKeon Australia 2020 Tokyo 30 July 2021 200 m freestyle 1:53.27 Mollie O'Callaghan Australia 2024 Paris 29 July 2024 400 m freestyle 3:56.46 Katie Ledecky United States 2016 Rio de Janeiro 7 August 2016 800 m freestyle 8:04.79 Katie Ledecky United States 2016 Rio de Janeiro 12 August 2016 1500 m freestyle\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide the name of the most decorated American gymnast (Simone Biles) and the American swimmer who holds the world record in the 800‑meter freestyle (Katie Ledecky). However, they do not contain Simone Biles's middle name, which is required to form the daughter's first name. Without that middle name, the answer cannot be fully constructed.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 775, + "osl": 584, + "total_tokens": 1359, + "latency_ms": 8497.7, + "tokens_per_second": 68.72 + }, + "context": { + "kept_docs_count": 3, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "b4cd9291-1991-453b-a6bf-650f01fa6109", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:17:23.498051Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of July 1, 2024, if I wanted to give my daughter the middle name of the American woman who is the most decorated female in the history of American gymnastics as her first name and the full first name of the American woman who holds the world record in the 800-meter freestyle as her middle name, what would I name my daughter? \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] couple formally adopted Simone and her younger sister Adria. Ron's sister, Shanon's aunt Harriet, adopted the two oldest children. Simone holds Belizean citizenship through her adoptive mother and considers Belize to be her second home. Biles and her family are Catholic . Biles attended Benfer Elementary School in Harris County , Texas. In 2012, Biles switched from public school to home schooling , allowing her to increase her training from about 20 to 32 hours a week. She earned her high-school diploma in mid-2015. Biles verbally committed to UCLA on August 4, 2014, and signed a National Letter of Intent in November 2014, planning to defer enrollment until after the 2016 Summer Olympics in Rio de Janeiro. Instead, on July 29, 2015, she announced that she w\n\n[DOC 2] ss Athlete of the Year 2017 2022 Succeeded by Serena Williams Simone Biles Portals : Biography Sports Olympic Games .\n\n[DOC 3] n the 400, 800, and 1500 m freestyle events Emma McKeon holds Olympic records in one individual and one relay event. ♦ denotes a performance that is also a current world record . Statistics are correct as of the end of the 2024 Olympics and include only those events which are currently recognised by the IOC as Olympic events. Event Time Name Nation Games Date Ref 50 m freestyle 23.66 Sarah Sjöström Sweden 2024 Paris 3 August 2024 100 m freestyle 51.96 Emma McKeon Australia 2020 Tokyo 30 July 2021 200 m freestyle 1:53.27 Mollie O'Callaghan Australia 2024 Paris 29 July 2024 400 m freestyle 3:56.46 Katie Ledecky United States 2016 Rio de Janeiro 7 August 2016 800 m freestyle 8:04.79 Katie Ledecky United States 2016 Rio de Janeiro 12 August 2016 1500 m freestyle\n\n\nSEARCH HISTORY: - Query: 'Simone Biles middle name' → 2 docs\n- Query: 'Katie Ledecky 800m freestyle world record' → 0 docs\n- Query: 'women 800 meter freestyle world record' → 1 docs\n- Query: 'Simone Biles full name' → 0 docs\n- Query: 'Katie Ledecky 800 freestyle world record' → 0 docs\n- Query: 'women's 800 m freestyle world record' → 0 docs\n- Query: 'Simone Biles biography' → 0 docs\n- Query: 'Simone Arianne Biles' → 0 docs\n- Query: 'Katie Ledecky 800 m freestyle world record' → 0 docs\n\nFEEDBACK: Need Simone Biles' middle name (Arianne) and confirmation that Katie Ledecky currently holds the women's 800‑meter freestyle world record.\nNeed Simone Biles' middle name (Arianne) and confirmation that Katie Ledecky holds the women's 800‑meter freestyle world record as of July 1 2024.\nNeeded Simone Biles' middle name (Arianne) and confirmation that Katie Ledecky currently holds the women's 800‑meter freestyle world record as of July 1 2024.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Simone Biles Wikipedia\",\n \"Simone Biles Arianne\",\n \"800m freestyle world record Ledecky\"\n ],\n \"feedback\": \"We need a source confirming Simone Biles' middle name (Arianne) and a source confirming that Katie Ledecky holds the women's 800‑meter freestyle world record as of July 1 2024.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1201, + "osl": 831, + "total_tokens": 2032, + "latency_ms": 3408.02, + "tokens_per_second": 243.84 + }, + "context": { + "kept_docs_count": 3, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "26c39196-7d13-4bd1-bc5a-98192337802d", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T03:17:32.092977Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of July 1, 2024, if I wanted to give my daughter the middle name of the American woman who is the most decorated female in the history of American gymnastics as her first name and the full first name of the American woman who holds the world record in the 800-meter freestyle as her middle name, what would I name my daughter? \n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] ander Popov Russia Swimming 1996 Summer M 2 2 0 4 Simone Manuel United States Swimming 2016 Summer F 2 2 0 4 Zhang Yufei China Swimming 2020 Summer F 2 2 0 4 Katie Ledecky United States Swimming 2020 Summer F 2 2 0 4 Ariarne Titmus Australia Swimming 2024 Summer F 2 2 0 4 Kate Douglass United States Swimming 2024 Summer F 2 2 0 4 Gretchen Walsh United States Swimming 2024 Summer F 2 2 0 4 208 Alexei Nemov Russia Gymnastics 1996 Summer M 2 1 3 6 Alexei Nemov Russia Gymnastics 2000 Summer M 2 1 3 6 210 Mitsuo Tsukahara Japan Gymnastics 1976 Summer M 2 1 2 5 Koji Gushiken Japan Gymnastics 1984 Summer M 2 1 2 5 Svetlana Boginskaya Soviet Union Gymnastics 1988 Summer F 2 1 2 5 Yelena Shushunova Soviet Union Gymnastics 1988 Summer F 2 1 2 5 Marit Bjørgen Norway Cr\n\n\nKEPT DOCUMENTS (context only):\n[3 documents already kept as relevant]\n[KEPT 1] couple formally adopted Simone and her younger sister Adria. Ron's sister, Shanon's aunt Harriet, adopted the two oldest children. Simone holds Belizean citizenship through her adoptive mother and considers Belize to be her second home. Biles and her family are Catholic . Biles attended Benfer Eleme...\n[KEPT 2] ss Athlete of the Year 2017 2022 Succeeded by Serena Williams Simone Biles Portals : Biography Sports Olympic Games ....\n[KEPT 3] n the 400, 800, and 1500 m freestyle events Emma McKeon holds Olympic records in one individual and one relay event. ♦ denotes a performance that is also a current world record . Statistics are correct as of the end of the 2024 Olympics and include only those events which are currently recognised by...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 769, + "osl": 1055, + "total_tokens": 1824, + "latency_ms": 8057.01, + "tokens_per_second": 130.94 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 3 + } + }, + { + "call_id": "3dab3dcb-1dca-4002-83d9-972ecd03c06b", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:17:36.983491Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of July 1, 2024, if I wanted to give my daughter the middle name of the American woman who is the most decorated female in the history of American gymnastics as her first name and the full first name of the American woman who holds the world record in the 800-meter freestyle as her middle name, what would I name my daughter? \n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] couple formally adopted Simone and her younger sister Adria. Ron's sister, Shanon's aunt Harriet, adopted the two oldest children. Simone holds Belizean citizenship through her adoptive mother and considers Belize to be her second home. Biles and her family are Catholic . Biles attended Benfer Elementary School in Harris County , Texas. In 2012, Biles switched from public school to home schooling , allowing her to increase her training from about 20 to 32 hours a week. She earned her high-school diploma in mid-2015. Biles verbally committed to UCLA on August 4, 2014, and signed a National Letter of Intent in November 2014, planning to defer enrollment until after the 2016 Summer Olympics in Rio de Janeiro. Instead, on July 29, 2015, she announced that she w\n\n[DOC 2] ss Athlete of the Year 2017 2022 Succeeded by Serena Williams Simone Biles Portals : Biography Sports Olympic Games .\n\n[DOC 3] n the 400, 800, and 1500 m freestyle events Emma McKeon holds Olympic records in one individual and one relay event. ♦ denotes a performance that is also a current world record . Statistics are correct as of the end of the 2024 Olympics and include only those events which are currently recognised by the IOC as Olympic events. Event Time Name Nation Games Date Ref 50 m freestyle 23.66 Sarah Sjöström Sweden 2024 Paris 3 August 2024 100 m freestyle 51.96 Emma McKeon Australia 2020 Tokyo 30 July 2021 200 m freestyle 1:53.27 Mollie O'Callaghan Australia 2024 Paris 29 July 2024 400 m freestyle 3:56.46 Katie Ledecky United States 2016 Rio de Janeiro 7 August 2016 800 m freestyle 8:04.79 Katie Ledecky United States 2016 Rio de Janeiro 12 August 2016 1500 m freestyle\n\n[DOC 4] ander Popov Russia Swimming 1996 Summer M 2 2 0 4 Simone Manuel United States Swimming 2016 Summer F 2 2 0 4 Zhang Yufei China Swimming 2020 Summer F 2 2 0 4 Katie Ledecky United States Swimming 2020 Summer F 2 2 0 4 Ariarne Titmus Australia Swimming 2024 Summer F 2 2 0 4 Kate Douglass United States Swimming 2024 Summer F 2 2 0 4 Gretchen Walsh United States Swimming 2024 Summer F 2 2 0 4 208 Alexei Nemov Russia Gymnastics 1996 Summer M 2 1 3 6 Alexei Nemov Russia Gymnastics 2000 Summer M 2 1 3 6 210 Mitsuo Tsukahara Japan Gymnastics 1976 Summer M 2 1 2 5 Koji Gushiken Japan Gymnastics 1984 Summer M 2 1 2 5 Svetlana Boginskaya Soviet Union Gymnastics 1988 Summer F 2 1 2 5 Yelena Shushunova Soviet Union Gymnastics 1988 Summer F 2 1 2 5 Marit Bjørgen Norway Cr\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents identify the most decorated American female gymnast as Simone Biles and the American record holder in the 800‑meter freestyle as Katie Ledecky. However, none of the kept documents provide Simone Biles's middle name, which is required to determine the daughter's first name. Without that fact, the answer cannot be fully derived from the provided sources.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1055, + "osl": 748, + "total_tokens": 1803, + "latency_ms": 4889.74, + "tokens_per_second": 152.97 + }, + "context": { + "kept_docs_count": 4, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "162d39e3-2050-4f31-a52f-196635bff154", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:17:41.296771Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: As of July 1, 2024, if I wanted to give my daughter the middle name of the American woman who is the most decorated female in the history of American gymnastics as her first name and the full first name of the American woman who holds the world record in the 800-meter freestyle as her middle name, what would I name my daughter? \n\nDOCUMENTS:\n\n[DOC 1] couple formally adopted Simone and her younger sister Adria. Ron's sister, Shanon's aunt Harriet, adopted the two oldest children. Simone holds Belizean citizenship through her adoptive mother and considers Belize to be her second home. Biles and her family are Catholic . Biles attended Benfer Elementary School in Harris County , Texas. In 2012, Biles switched from public school to home schooling , allowing her to increase her training from about 20 to 32 hours a week. She earned her high-school diploma in mid-2015. Biles verbally committed to UCLA on August 4, 2014, and signed a National Letter of Intent in November 2014, planning to defer enrollment until after the 2016 Summer Olympics in Rio de Janeiro. Instead, on July 29, 2015, she announced that she w\n\n[DOC 2] ss Athlete of the Year 2017 2022 Succeeded by Serena Williams Simone Biles Portals : Biography Sports Olympic Games .\n\n[DOC 3] n the 400, 800, and 1500 m freestyle events Emma McKeon holds Olympic records in one individual and one relay event. ♦ denotes a performance that is also a current world record . Statistics are correct as of the end of the 2024 Olympics and include only those events which are currently recognised by the IOC as Olympic events. Event Time Name Nation Games Date Ref 50 m freestyle 23.66 Sarah Sjöström Sweden 2024 Paris 3 August 2024 100 m freestyle 51.96 Emma McKeon Australia 2020 Tokyo 30 July 2021 200 m freestyle 1:53.27 Mollie O'Callaghan Australia 2024 Paris 29 July 2024 400 m freestyle 3:56.46 Katie Ledecky United States 2016 Rio de Janeiro 7 August 2016 800 m freestyle 8:04.79 Katie Ledecky United States 2016 Rio de Janeiro 12 August 2016 1500 m freestyle\n\n[DOC 4] ander Popov Russia Swimming 1996 Summer M 2 2 0 4 Simone Manuel United States Swimming 2016 Summer F 2 2 0 4 Zhang Yufei China Swimming 2020 Summer F 2 2 0 4 Katie Ledecky United States Swimming 2020 Summer F 2 2 0 4 Ariarne Titmus Australia Swimming 2024 Summer F 2 2 0 4 Kate Douglass United States Swimming 2024 Summer F 2 2 0 4 Gretchen Walsh United States Swimming 2024 Summer F 2 2 0 4 208 Alexei Nemov Russia Gymnastics 1996 Summer M 2 1 3 6 Alexei Nemov Russia Gymnastics 2000 Summer M 2 1 3 6 210 Mitsuo Tsukahara Japan Gymnastics 1976 Summer M 2 1 2 5 Koji Gushiken Japan Gymnastics 1984 Summer M 2 1 2 5 Svetlana Boginskaya Soviet Union Gymnastics 1988 Summer F 2 1 2 5 Yelena Shushunova Soviet Union Gymnastics 1988 Summer F 2 1 2 5 Marit Bjørgen Norway Cr\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 874, + "osl": 1, + "total_tokens": 875, + "latency_ms": 4312.59, + "tokens_per_second": 0.23 + }, + "context": { + "num_documents_used": 4, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:17:41.297597Z", + "summary": { + "total_llm_calls": 11, + "total_hops": 5, + "total_input_tokens": 9462, + "total_output_tokens": 5544, + "total_tokens": 15006, + "total_latency_ms": 103148.13, + "average_tokens_per_second": 71.22, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Simone_Biles", + "https://en.wikipedia.org/wiki/Katie_Ledecky" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "Arianne Kathleen" + } + }, + { + "query_id": "40", + "original_query": "I am thinking of a Ancient Roman City. The city was destroyed by volcanic eruption. The eruption occurred in the year 79 AD. The volcano was a stratovolcano. Where was the session held where it was decided that the city would be named a UNESCO world heritage site?", + "timestamp_start": "2026-05-16T03:17:41.333087Z", + "llm_calls": [ + { + "call_id": "56c47f35-09fc-40ef-bed5-97643fa603cc", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:17:45.371853Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: I am thinking of a Ancient Roman City. The city was destroyed by volcanic eruption. The eruption occurred in the year 79 AD. The volcano was a stratovolcano. Where was the session held where it was decided that the city would be named a UNESCO world heritage site?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Pompeii UNESCO session\",\n \"Pompeii World Heritage 1997\",\n \"UNESCO 21st session location\"\n ],\n \"feedback\": \"Need to locate the UNESCO World Heritage Committee session that decided to inscribe the ancient Roman city destroyed in 79 AD (Pompeii) as a World Heritage Site.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 436, + "osl": 82, + "total_tokens": 518, + "latency_ms": 4038.63, + "tokens_per_second": 20.3 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "c7330944-171a-4ea1-8469-cc9e873362f6", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:17:48.495788Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: I am thinking of a Ancient Roman City. The city was destroyed by volcanic eruption. The eruption occurred in the year 79 AD. The volcano was a stratovolcano. Where was the session held where it was decided that the city would be named a UNESCO world heritage site?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] ry. Pompeii Live (28 June 2006), a Channel 5 production featuring a live archaeological dig at Pompeii and Herculaneum. Pompeii: The Mystery of the People Frozen in Time (2013), a BBC One drama documentary presented by Margaret Mountford . The Riddle of Pompeii (23 May 2014), Discovery Channel. Pompeii: The Dead Speak (8 August 2016), Smithsonian Channel . Pompeii's People (3 September 2017), a CBC Gem documentary presented by David Suzuki . Gallery The Theatre Entrance to the Basilica in the Forum The Basilica The Triangular Forum The Forum The Temple of Apollo The House of the Faun Fresco from the Villa dei Misteri Street in Pompeii See also Italy portal Cities portal Foreign influences on Pompeii List of World Heritage Sites in Italy Mastroberardino , a p\n\n[NEW 2] within the territory of state members of the World Heritage Committee at their invitation. Rotation between regions and cultures is a consideration for selection and the location for the next session is chosen by the committee at the end of each session. Session Year Date Host city 1 1977 27 June – 1 July Paris 2 1978 5–8 September Washington, D. C. 3 1979 22–26 October Cairo & Luxor 4 1980 1–5 September Paris 5 1981 26–30 October Sydney 6 1982 13–17 December Paris 7 1983 5–9 December Florence 8 1984 29 October – 2 November Buenos Aires 9 1985 2–6 December Paris 10 1986 24–28 November Paris 11 1987 7–11 December Paris 12 1988 5–9 December Brasília 13 1989 11–15 December Paris 14 1990 7–12 December Banff 15 1991 9–13 December Carthage 16 1992 7–14 December Sa\n\n[NEW 3] uary 2002 Athens Greece 2004 Europe S028 XXVIII 13 August 2004 29 August 2004 Turin Italy 2006 W020 XX 10 February 2006 26 February 2006 Beijing China 2008 Asia S029 XXIX 8 August 2008 24 August 2008 Vancouver Canada 2010 North America W021 XXI 12 February 2010 28 February 2010 London United Kingdom 2012 Europe S030 XXX 27 July 2012 12 August 2012 Sochi Russia 2014 W022 XXII 7 February 2014 23 February 2014 Rio de Janeiro Brazil 2016 South America S031 XXXI 5 August 2016 21 August 2016 Pyeongchang South Korea 2018 Asia W023 XXIII 9 February 2018 25 February 2018 § Tokyo Japan 2020 S032 XXXII 23 July 2021 8 August 2021 Beijing China 2022 W024 XXIV 4 February 2022 20 February 2022 Paris France 2024 Europe S033 XXXIII 26 July 2024 11 August 2024 Milan Cortina d\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 3 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 1, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 987, + "osl": 470, + "total_tokens": 1457, + "latency_ms": 2525.14, + "tokens_per_second": 186.13 + }, + "context": { + "num_documents_evaluated": 3, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "bf8b54eb-4153-4d88-ba16-03dcbd72a9bd", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:17:56.948189Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: I am thinking of a Ancient Roman City. The city was destroyed by volcanic eruption. The eruption occurred in the year 79 AD. The volcano was a stratovolcano. Where was the session held where it was decided that the city would be named a UNESCO world heritage site?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] within the territory of state members of the World Heritage Committee at their invitation. Rotation between regions and cultures is a consideration for selection and the location for the next session is chosen by the committee at the end of each session. Session Year Date Host city 1 1977 27 June – 1 July Paris 2 1978 5–8 September Washington, D. C. 3 1979 22–26 October Cairo & Luxor 4 1980 1–5 September Paris 5 1981 26–30 October Sydney 6 1982 13–17 December Paris 7 1983 5–9 December Florence 8 1984 29 October – 2 November Buenos Aires 9 1985 2–6 December Paris 10 1986 24–28 November Paris 11 1987 7–11 December Paris 12 1988 5–9 December Brasília 13 1989 11–15 December Paris 14 1990 7–12 December Banff 15 1991 9–13 December Carthage 16 1992 7–14 December Sa\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept document provides a list of World Heritage Committee sessions up to 1992, but the inscription of the ancient Roman city (Pompeii) as a UNESCO World Heritage Site occurred after 1992 (1997). The document does not contain information about the session year, date, or host city for the decision that designated the city as a World Heritage Site, so the required fact is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 566, + "osl": 319, + "total_tokens": 885, + "latency_ms": 8451.63, + "tokens_per_second": 37.74 + }, + "context": { + "kept_docs_count": 1, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "80033c8d-2d9b-4587-9f65-a24352527d59", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:18:02.453191Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: I am thinking of a Ancient Roman City. The city was destroyed by volcanic eruption. The eruption occurred in the year 79 AD. The volcano was a stratovolcano. Where was the session held where it was decided that the city would be named a UNESCO world heritage site?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] within the territory of state members of the World Heritage Committee at their invitation. Rotation between regions and cultures is a consideration for selection and the location for the next session is chosen by the committee at the end of each session. Session Year Date Host city 1 1977 27 June – 1 July Paris 2 1978 5–8 September Washington, D. C. 3 1979 22–26 October Cairo & Luxor 4 1980 1–5 September Paris 5 1981 26–30 October Sydney 6 1982 13–17 December Paris 7 1983 5–9 December Florence 8 1984 29 October – 2 November Buenos Aires 9 1985 2–6 December Paris 10 1986 24–28 November Paris 11 1987 7–11 December Paris 12 1988 5–9 December Brasília 13 1989 11–15 December Paris 14 1990 7–12 December Banff 15 1991 9–13 December Carthage 16 1992 7–14 December Sa\n\n\nSEARCH HISTORY: - Query: 'Pompeii UNESCO session' → 1 docs\n- Query: 'Pompeii World Heritage 1997' → 0 docs\n- Query: 'UNESCO 21st session location' → 2 docs\n\nFEEDBACK: Need to locate the UNESCO World Heritage Committee session that decided to inscribe the ancient Roman city destroyed in 79 AD (Pompeii) as a World Heritage Site.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"1997 UNESCO World Heritage Committee session\",\n \"Pompeii UNESCO inscription 1997 session\",\n \"UNESCO World Heritage Committee 1997 host city\"\n ],\n \"feedback\": \"Need to identify the session (city) that decided to inscribe Pompeii as a UNESCO World Heritage Site in 1997.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 759, + "osl": 76, + "total_tokens": 835, + "latency_ms": 5504.36, + "tokens_per_second": 13.81 + }, + "context": { + "kept_docs_count": 1, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "ce4b8f8e-ad54-4fbd-9c8a-791ff7981c8d", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T03:18:04.671818Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: I am thinking of a Ancient Roman City. The city was destroyed by volcanic eruption. The eruption occurred in the year 79 AD. The volcano was a stratovolcano. Where was the session held where it was decided that the city would be named a UNESCO world heritage site?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] the question as \"what good would 'posted edicts' do in a world of low literacy?'. ^ Political slogans and obscenities are widely preserved as graffiti in Pompeii: Antonio Varone, Erotica Pompeiana: Love Inscriptions on the Walls of Pompeii (\"L'Erma\" di Bretschneider, 2002). Soldiers sometimes inscribed sling bullets with aggressive messages: Phang, \"Military Documents, Languages, and Literacy,\" p. 300. ^ The caesareum at Najaran was possibly known later as the \"Kaaba of Najran\" ^ \"This mentality,\" notes John T. Koch, \"lay at the core of the genius of cultural assimilation which made the Roman Empire possible\"; entry on \"Interpretatio romana,\" in Celtic Culture: A Historical Encyclopedia (ABC-Clio, 2006), p. 974. References Citations ^ ; ^ ^ Bennett (1997) .\n\n\nKEPT DOCUMENTS (context only):\n[1 documents already kept as relevant]\n[KEPT 1] within the territory of state members of the World Heritage Committee at their invitation. Rotation between regions and cultures is a consideration for selection and the location for the next session is chosen by the committee at the end of each session. Session Year Date Host city 1 1977 27 June – ...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 559, + "osl": 224, + "total_tokens": 783, + "latency_ms": 1601.5, + "tokens_per_second": 139.87 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 1 + } + }, + { + "call_id": "cfb92aba-9b8e-402d-afa2-4eac25110ab7", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:18:10.495799Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: I am thinking of a Ancient Roman City. The city was destroyed by volcanic eruption. The eruption occurred in the year 79 AD. The volcano was a stratovolcano. Where was the session held where it was decided that the city would be named a UNESCO world heritage site?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] within the territory of state members of the World Heritage Committee at their invitation. Rotation between regions and cultures is a consideration for selection and the location for the next session is chosen by the committee at the end of each session. Session Year Date Host city 1 1977 27 June – 1 July Paris 2 1978 5–8 September Washington, D. C. 3 1979 22–26 October Cairo & Luxor 4 1980 1–5 September Paris 5 1981 26–30 October Sydney 6 1982 13–17 December Paris 7 1983 5–9 December Florence 8 1984 29 October – 2 November Buenos Aires 9 1985 2–6 December Paris 10 1986 24–28 November Paris 11 1987 7–11 December Paris 12 1988 5–9 December Brasília 13 1989 11–15 December Paris 14 1990 7–12 December Banff 15 1991 9–13 December Carthage 16 1992 7–14 December Sa\n\n[DOC 2] the question as \"what good would 'posted edicts' do in a world of low literacy?'. ^ Political slogans and obscenities are widely preserved as graffiti in Pompeii: Antonio Varone, Erotica Pompeiana: Love Inscriptions on the Walls of Pompeii (\"L'Erma\" di Bretschneider, 2002). Soldiers sometimes inscribed sling bullets with aggressive messages: Phang, \"Military Documents, Languages, and Literacy,\" p. 300. ^ The caesareum at Najaran was possibly known later as the \"Kaaba of Najran\" ^ \"This mentality,\" notes John T. Koch, \"lay at the core of the genius of cultural assimilation which made the Roman Empire possible\"; entry on \"Interpretatio romana,\" in Celtic Culture: A Historical Encyclopedia (ABC-Clio, 2006), p. 974. References Citations ^ ; ^ ^ Bennett (1997) .\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents provide a list of World Heritage Committee session host cities up to 1992, but do not include the session in which the Ancient Roman city (Pompeii) destroyed by the 79 AD eruption of Vesuvius was inscribed as a UNESCO World Heritage site. The specific session year and location for that inscription are missing, so the question cannot be answered with the available information.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 720, + "osl": 98, + "total_tokens": 818, + "latency_ms": 5823.21, + "tokens_per_second": 16.83 + }, + "context": { + "kept_docs_count": 2, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "b3d6101f-0cd7-4d21-a519-80286f14a4ca", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:18:17.747217Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: I am thinking of a Ancient Roman City. The city was destroyed by volcanic eruption. The eruption occurred in the year 79 AD. The volcano was a stratovolcano. Where was the session held where it was decided that the city would be named a UNESCO world heritage site?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] within the territory of state members of the World Heritage Committee at their invitation. Rotation between regions and cultures is a consideration for selection and the location for the next session is chosen by the committee at the end of each session. Session Year Date Host city 1 1977 27 June – 1 July Paris 2 1978 5–8 September Washington, D. C. 3 1979 22–26 October Cairo & Luxor 4 1980 1–5 September Paris 5 1981 26–30 October Sydney 6 1982 13–17 December Paris 7 1983 5–9 December Florence 8 1984 29 October – 2 November Buenos Aires 9 1985 2–6 December Paris 10 1986 24–28 November Paris 11 1987 7–11 December Paris 12 1988 5–9 December Brasília 13 1989 11–15 December Paris 14 1990 7–12 December Banff 15 1991 9–13 December Carthage 16 1992 7–14 December Sa\n\n[DOC 2] the question as \"what good would 'posted edicts' do in a world of low literacy?'. ^ Political slogans and obscenities are widely preserved as graffiti in Pompeii: Antonio Varone, Erotica Pompeiana: Love Inscriptions on the Walls of Pompeii (\"L'Erma\" di Bretschneider, 2002). Soldiers sometimes inscribed sling bullets with aggressive messages: Phang, \"Military Documents, Languages, and Literacy,\" p. 300. ^ The caesareum at Najaran was possibly known later as the \"Kaaba of Najran\" ^ \"This mentality,\" notes John T. Koch, \"lay at the core of the genius of cultural assimilation which made the Roman Empire possible\"; entry on \"Interpretatio romana,\" in Celtic Culture: A Historical Encyclopedia (ABC-Clio, 2006), p. 974. References Citations ^ ; ^ ^ Bennett (1997) .\n\n\nSEARCH HISTORY: - Query: 'Pompeii UNESCO session' → 1 docs\n- Query: 'Pompeii World Heritage 1997' → 0 docs\n- Query: 'UNESCO 21st session location' → 2 docs\n- Query: '1997 UNESCO World Heritage Committee session' → 0 docs\n- Query: 'Pompeii UNESCO inscription 1997 session' → 1 docs\n- Query: 'UNESCO World Heritage Committee 1997 host city' → 0 docs\n\nFEEDBACK: Need to locate the UNESCO World Heritage Committee session that decided to inscribe the ancient Roman city destroyed in 79 AD (Pompeii) as a World Heritage Site.\nNeed to identify the session (city) that decided to inscribe Pompeii as a UNESCO World Heritage Site in 1997.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"UNESCO World Heritage Committee 1997 session city\",\n \"World Heritage Committee 1997 host city\",\n \"UNESCO World Heritage Committee sessions list\"\n ],\n \"feedback\": \"Need to find the city where the 1997 UNESCO World Heritage Committee session took place, as that session inscribed Pompeii as a World Heritage Site.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1076, + "osl": 328, + "total_tokens": 1404, + "latency_ms": 7250.74, + "tokens_per_second": 45.24 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "4b644c5b-76c4-422e-abeb-d0f57170316a", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T03:18:21.882366Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: I am thinking of a Ancient Roman City. The city was destroyed by volcanic eruption. The eruption occurred in the year 79 AD. The volcano was a stratovolcano. Where was the session held where it was decided that the city would be named a UNESCO world heritage site?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] WSM Western Samoa 5 TOG Togo 5 TGA Tonga 5 BAN Bangladesh 4 CHA Chad 4 COM Comoros 4 MTN Mauritania 4 OMA Oman 4 RWA Rwanda 4 SOL Solomon Islands 4 SOM Somalia 4 SUD Sudan 4 UAE United Arab Emirates 4 VAN Vanuatu 4 YEM Yemen 4 ARU Aruba 3 CPV Cape Verde 3 COK Cook Islands 3 GBS Guinea-Bissau 3 IRQ Iraq 3 MLI Mali 3 MON Monaco 3 MOZ Mozambique 3 MYA Myanmar 3 NRU Nauru 3 NIG Niger 3 AFG Afghanistan 2 BHU Bhutan 2 LIE Liechtenstein 2 MAW Malawi 2 STP São Tomé and Príncipe 2 BRU Brunei 1 LIB Lebanon 1 PLE Palestine 1 Centennial Olympic Park bombing The marker at the entrance to Centennial Park in downtown Atlanta The 1996 Olympics were marred by the Centennial Olympic Park bombing , which occurred on July 27. Security guard Richard Jewell discovered the pipe bo\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] within the territory of state members of the World Heritage Committee at their invitation. Rotation between regions and cultures is a consideration for selection and the location for the next session is chosen by the committee at the end of each session. Session Year Date Host city 1 1977 27 June – ...\n[KEPT 2] the question as \"what good would 'posted edicts' do in a world of low literacy?'. ^ Political slogans and obscenities are widely preserved as graffiti in Pompeii: Antonio Varone, Erotica Pompeiana: Love Inscriptions on the Walls of Pompeii (\"L'Erma\" di Bretschneider, 2002). Soldiers sometimes inscri...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 685, + "osl": 168, + "total_tokens": 853, + "latency_ms": 3527.75, + "tokens_per_second": 47.62 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "68160117-4aa1-45d9-a47c-5406f31b8742", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:18:26.507395Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: I am thinking of a Ancient Roman City. The city was destroyed by volcanic eruption. The eruption occurred in the year 79 AD. The volcano was a stratovolcano. Where was the session held where it was decided that the city would be named a UNESCO world heritage site?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] within the territory of state members of the World Heritage Committee at their invitation. Rotation between regions and cultures is a consideration for selection and the location for the next session is chosen by the committee at the end of each session. Session Year Date Host city 1 1977 27 June – 1 July Paris 2 1978 5–8 September Washington, D. C. 3 1979 22–26 October Cairo & Luxor 4 1980 1–5 September Paris 5 1981 26–30 October Sydney 6 1982 13–17 December Paris 7 1983 5–9 December Florence 8 1984 29 October – 2 November Buenos Aires 9 1985 2–6 December Paris 10 1986 24–28 November Paris 11 1987 7–11 December Paris 12 1988 5–9 December Brasília 13 1989 11–15 December Paris 14 1990 7–12 December Banff 15 1991 9–13 December Carthage 16 1992 7–14 December Sa\n\n[DOC 2] the question as \"what good would 'posted edicts' do in a world of low literacy?'. ^ Political slogans and obscenities are widely preserved as graffiti in Pompeii: Antonio Varone, Erotica Pompeiana: Love Inscriptions on the Walls of Pompeii (\"L'Erma\" di Bretschneider, 2002). Soldiers sometimes inscribed sling bullets with aggressive messages: Phang, \"Military Documents, Languages, and Literacy,\" p. 300. ^ The caesareum at Najaran was possibly known later as the \"Kaaba of Najran\" ^ \"This mentality,\" notes John T. Koch, \"lay at the core of the genius of cultural assimilation which made the Roman Empire possible\"; entry on \"Interpretatio romana,\" in Celtic Culture: A Historical Encyclopedia (ABC-Clio, 2006), p. 974. References Citations ^ ; ^ ^ Bennett (1997) .\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The question requires the host city of the UNESCO World Heritage Committee session that inscribed the ancient Roman city (Pompeii) as a World Heritage site. We can infer the city (Pompeii) from the description, but the kept documents do not provide the specific session year (likely 1997) nor the host city for that session. DOC 1 only lists sessions up to 1992, and DOC 2 is unrelated. Therefore, the necessary information to answer the question is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 720, + "osl": 118, + "total_tokens": 838, + "latency_ms": 4624.19, + "tokens_per_second": 25.52 + }, + "context": { + "kept_docs_count": 2, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "1f5a528a-91f6-4fbc-95c6-7cf86cf0f1d2", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:18:30.278295Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: I am thinking of a Ancient Roman City. The city was destroyed by volcanic eruption. The eruption occurred in the year 79 AD. The volcano was a stratovolcano. Where was the session held where it was decided that the city would be named a UNESCO world heritage site?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] within the territory of state members of the World Heritage Committee at their invitation. Rotation between regions and cultures is a consideration for selection and the location for the next session is chosen by the committee at the end of each session. Session Year Date Host city 1 1977 27 June – 1 July Paris 2 1978 5–8 September Washington, D. C. 3 1979 22–26 October Cairo & Luxor 4 1980 1–5 September Paris 5 1981 26–30 October Sydney 6 1982 13–17 December Paris 7 1983 5–9 December Florence 8 1984 29 October – 2 November Buenos Aires 9 1985 2–6 December Paris 10 1986 24–28 November Paris 11 1987 7–11 December Paris 12 1988 5–9 December Brasília 13 1989 11–15 December Paris 14 1990 7–12 December Banff 15 1991 9–13 December Carthage 16 1992 7–14 December Sa\n\n[DOC 2] the question as \"what good would 'posted edicts' do in a world of low literacy?'. ^ Political slogans and obscenities are widely preserved as graffiti in Pompeii: Antonio Varone, Erotica Pompeiana: Love Inscriptions on the Walls of Pompeii (\"L'Erma\" di Bretschneider, 2002). Soldiers sometimes inscribed sling bullets with aggressive messages: Phang, \"Military Documents, Languages, and Literacy,\" p. 300. ^ The caesareum at Najaran was possibly known later as the \"Kaaba of Najran\" ^ \"This mentality,\" notes John T. Koch, \"lay at the core of the genius of cultural assimilation which made the Roman Empire possible\"; entry on \"Interpretatio romana,\" in Celtic Culture: A Historical Encyclopedia (ABC-Clio, 2006), p. 974. References Citations ^ ; ^ ^ Bennett (1997) .\n\n\nSEARCH HISTORY: - Query: 'Pompeii UNESCO session' → 1 docs\n- Query: 'Pompeii World Heritage 1997' → 0 docs\n- Query: 'UNESCO 21st session location' → 2 docs\n- Query: '1997 UNESCO World Heritage Committee session' → 0 docs\n- Query: 'Pompeii UNESCO inscription 1997 session' → 1 docs\n- Query: 'UNESCO World Heritage Committee 1997 host city' → 0 docs\n- Query: 'UNESCO World Heritage Committee 1997 session city' → 0 docs\n- Query: 'World Heritage Committee 1997 host city' → 1 docs\n- Query: 'UNESCO World Heritage Committee sessions list' → 0 docs\n\nFEEDBACK: Need to locate the UNESCO World Heritage Committee session that decided to inscribe the ancient Roman city destroyed in 79 AD (Pompeii) as a World Heritage Site.\nNeed to identify the session (city) that decided to inscribe Pompeii as a UNESCO World Heritage Site in 1997.\nNeed to find the city where the 1997 UNESCO World Heritage Committee session took place, as that session inscribed Pompeii as a World Heritage Site.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"21st UNESCO World Heritage Committee session\",\n \"UNESCO World Heritage Committee 21st session city\",\n \"Pompeii UNESCO inscription 1997 session city\"\n ],\n \"feedback\": \"The goal is to discover which city hosted the UNESCO World Heritage Committee session that inscribed Pompeii (destroyed in 79 AD) as a World Heritage Site in 1997. The queries focus on the session number (21st) and the year, avoiding previously failed phrasing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1175, + "osl": 523, + "total_tokens": 1698, + "latency_ms": 3770.24, + "tokens_per_second": 138.72 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "b357f6fc-209d-4fc9-a900-7faeb434c57d", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:18:41.392314Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: I am thinking of a Ancient Roman City. The city was destroyed by volcanic eruption. The eruption occurred in the year 79 AD. The volcano was a stratovolcano. Where was the session held where it was decided that the city would be named a UNESCO world heritage site?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] within the territory of state members of the World Heritage Committee at their invitation. Rotation between regions and cultures is a consideration for selection and the location for the next session is chosen by the committee at the end of each session. Session Year Date Host city 1 1977 27 June – 1 July Paris 2 1978 5–8 September Washington, D. C. 3 1979 22–26 October Cairo & Luxor 4 1980 1–5 September Paris 5 1981 26–30 October Sydney 6 1982 13–17 December Paris 7 1983 5–9 December Florence 8 1984 29 October – 2 November Buenos Aires 9 1985 2–6 December Paris 10 1986 24–28 November Paris 11 1987 7–11 December Paris 12 1988 5–9 December Brasília 13 1989 11–15 December Paris 14 1990 7–12 December Banff 15 1991 9–13 December Carthage 16 1992 7–14 December Sa\n\n[DOC 2] the question as \"what good would 'posted edicts' do in a world of low literacy?'. ^ Political slogans and obscenities are widely preserved as graffiti in Pompeii: Antonio Varone, Erotica Pompeiana: Love Inscriptions on the Walls of Pompeii (\"L'Erma\" di Bretschneider, 2002). Soldiers sometimes inscribed sling bullets with aggressive messages: Phang, \"Military Documents, Languages, and Literacy,\" p. 300. ^ The caesareum at Najaran was possibly known later as the \"Kaaba of Najran\" ^ \"This mentality,\" notes John T. Koch, \"lay at the core of the genius of cultural assimilation which made the Roman Empire possible\"; entry on \"Interpretatio romana,\" in Celtic Culture: A Historical Encyclopedia (ABC-Clio, 2006), p. 974. References Citations ^ ; ^ ^ Bennett (1997) .\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide a list of World Heritage Committee sessions up to 1992, but do not include the session in which the ancient Roman city destroyed in 79 AD (Pompeii) was inscribed as a UNESCO World Heritage site. The required information about the year and host city of that specific session is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 760, + "osl": 483, + "total_tokens": 1243, + "latency_ms": 10508.25, + "tokens_per_second": 45.96 + }, + "context": { + "kept_docs_count": 2, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "922ad225-2843-4415-a4e5-b5a26eb1f5e2", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:18:43.003992Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: I am thinking of a Ancient Roman City. The city was destroyed by volcanic eruption. The eruption occurred in the year 79 AD. The volcano was a stratovolcano. Where was the session held where it was decided that the city would be named a UNESCO world heritage site?\n\nDOCUMENTS:\n\n[DOC 1] within the territory of state members of the World Heritage Committee at their invitation. Rotation between regions and cultures is a consideration for selection and the location for the next session is chosen by the committee at the end of each session. Session Year Date Host city 1 1977 27 June – 1 July Paris 2 1978 5–8 September Washington, D. C. 3 1979 22–26 October Cairo & Luxor 4 1980 1–5 September Paris 5 1981 26–30 October Sydney 6 1982 13–17 December Paris 7 1983 5–9 December Florence 8 1984 29 October – 2 November Buenos Aires 9 1985 2–6 December Paris 10 1986 24–28 November Paris 11 1987 7–11 December Paris 12 1988 5–9 December Brasília 13 1989 11–15 December Paris 14 1990 7–12 December Banff 15 1991 9–13 December Carthage 16 1992 7–14 December Sa\n\n[DOC 2] the question as \"what good would 'posted edicts' do in a world of low literacy?'. ^ Political slogans and obscenities are widely preserved as graffiti in Pompeii: Antonio Varone, Erotica Pompeiana: Love Inscriptions on the Walls of Pompeii (\"L'Erma\" di Bretschneider, 2002). Soldiers sometimes inscribed sling bullets with aggressive messages: Phang, \"Military Documents, Languages, and Literacy,\" p. 300. ^ The caesareum at Najaran was possibly known later as the \"Kaaba of Najran\" ^ \"This mentality,\" notes John T. Koch, \"lay at the core of the genius of cultural assimilation which made the Roman Empire possible\"; entry on \"Interpretatio romana,\" in Celtic Culture: A Historical Encyclopedia (ABC-Clio, 2006), p. 974. References Citations ^ ; ^ ^ Bennett (1997) .\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 627, + "osl": 198, + "total_tokens": 825, + "latency_ms": 1610.96, + "tokens_per_second": 122.91 + }, + "context": { + "num_documents_used": 2, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:18:43.004654Z", + "summary": { + "total_llm_calls": 12, + "total_hops": 5, + "total_input_tokens": 9070, + "total_output_tokens": 3087, + "total_tokens": 12157, + "total_latency_ms": 59236.6, + "average_tokens_per_second": 70.05, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Stratovolcano", + "https://en.wikipedia.org/wiki/Eruption_of_Mount_Vesuvius_in_79_AD", + "https://en.wikipedia.org/wiki/Pompeii", + "https://en.wikipedia.org/wiki/World_Heritage_Committee" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "Naples" + } + }, + { + "query_id": "41", + "original_query": "What Formula One car was driven in 1994 by the nephew of a racing driver from Italy who drove a Ferrari 312T and shares a last name with a common cocktail drink?", + "timestamp_start": "2026-05-16T03:18:43.037796Z", + "llm_calls": [ + { + "call_id": "1d507116-a97d-4226-a861-6d1a8fd1a2c6", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:19:11.464911Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What Formula One car was driven in 1994 by the nephew of a racing driver from Italy who drove a Ferrari 312T and shares a last name with a common cocktail drink?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Ferrari 312T Italian drivers\",\n \"Italian driver nephew Formula One 1994\",\n \"1994 Formula One car driven by nephew\"\n ],\n \"feedback\": \"First query will list Italian drivers who raced the Ferrari 312T. Second query aims to find the nephew of such a driver who competed in F1 in 1994. Third query seeks the specific car he drove in that season.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 459, + "osl": 1307, + "total_tokens": 1766, + "latency_ms": 28426.88, + "tokens_per_second": 45.98 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "342422cd-6fad-492d-82f8-46f9f146f556", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:19:17.784788Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: What Formula One car was driven in 1994 by the nephew of a racing driver from Italy who drove a Ferrari 312T and shares a last name with a common cocktail drink?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Ret 4 Ret Ret 16 Ret 11 10 Ret 3 13 Ivan Capelli Ret Ret 5 10 † Ret Ret Ret Ret 9 Ret 6 Ret Ret Ret 3 14 Thierry Boutsen Ret 10 Ret Ret Ret 12 10 Ret 10 7 Ret Ret Ret 8 Ret 5 2 15 Johnny Herbert 6 7 Ret Ret Ret Ret Ret 6 Ret Ret Ret 13 † Ret Ret Ret 13 2 16 Pierluigi Martini Ret Ret Ret 6 6 Ret 8 10 15 11 Ret Ret 8 Ret 10 Ret 2 17 Stefano Modena DNQ Ret Ret DNQ Ret Ret Ret Ret Ret DNQ Ret 15 DNQ 13 7 6 1 18 Christian Fittipaldi Ret Ret Ret 11 Ret 8 13 DNQ DNQ DNQ 12 6 9 1 19 Bertrand Gachot Ret 11 Ret Ret Ret 6 DSQ Ret Ret 14 Ret 18 † Ret Ret Ret Ret 1 — Aguri Suzuki 8 DNQ Ret 7 10 11 DNQ Ret 12 Ret Ret 9 Ret 10 8 8 0 — JJ Lehto Ret 8 8 Ret 11 † 9 9 9 13 10 DNQ 7 11 † Ret 9 Ret 0 — Gianni Morbidelli Ret Ret 7 Ret Ret Ret 11 8 17 † 12 DNQ 16 Ret 14 14 10 0 —\n\n[NEW 2] Italian racing driver Giancarlo Martini Nationality Italian Born ( 1947-08-16 ) 16 August 1947 Lavezzola , Italy Died 26 March 2013 (2013-03-26) (aged 65) Forlì , Italy Relatives Pierluigi Martini Oliver Martini European Formula Two Championship Years active 1974–1979 Starts 42 Wins 0 Poles 0 Fastest laps 0 Best finish 7th in 1976 Previous series 1978 1972–1973 British Formula One Formula Italia Championship titles 1973 Formula Italia British Formula One Championship career Active years 1978 Entries 2 Championships 0 Wins 1 Podiums 1 Career points 32 Pole positions 1 Fastest laps 1 Martini driving the Scuderia Everest Ferrari 312T in the 1976 BRDC International Trophy race at Silverstone . Giancarlo Martini (16 August 1947 – 26 March 2013) was a racing drive\n\n[NEW 3] 6 Alta F2 2.0 L4 D Alan Brown 1 John Barber 1 Adolfo Schwelm Cruz 1 Stirling Moss 5, 7, 9 Equipe Gordini Gordini Simca-Gordini Type 16 Type 15 Gordini 20 2.0 L6 Gordini 1500 1.5 L4 E Robert Manzon 1 Harry Schell 1, 3–7, 9 Maurice Trintignant 1, 3–9 Jean Behra 1, 4–8 Carlos Menditéguy 1 Pablo Birger 1 Roberto Mieres 3, 5, 9 Fred Wacker 3–4, 8 Ecurie Rosier Ferrari 500 Ferrari 500 2.0 L4 D Louis Rosier 3–7, 9 E 8 Enrico Platé Maserati A6GCM Maserati A6 2.0 L6 P Toulo de Graffenried 3 Connaught Engineering Connaught - Lea-Francis Type A Lea-Francis 2.0 L4 D Roy Salvadori 3, 5–7, 9 Kenneth McAlpine 3, 6–7, 9 Stirling Moss 3 Birabongse Bhanudej 5–7 Jack Fairman 9 Ecurie Belge Connaught - Lea-Francis Type A Lea-Francis 2.0 L4 E Johnny Claes 3, 5, 7, 9 André Pilett\n\n[NEW 4] t GOO 1952 Scuderia Ferrari Ferrari 500 Ferrari 500 2.0 L4 SYR 1 PAU 1 IBS MAR 1 AST INT ELÄ NAP EIF PAR ALB FRO ULS MNZ Ret LAC ESS MAR 3* SAB Ret CAE DMT COM 1† NAT BAU 1 MOD 3‡ CAD SKA MAD AVU JOE NEW Ferrari 375 Ferrari 375 4.5 V12 VAL 5 RIC LAV 1953 Scuderia Ferrari Ferrari 500 Ferrari 500 2.0 L4 SYR Ret PAU 1 LAV AST BOR 1 INT ELÄ NAP 5 ULS WIN FRO COR EIF Ferrari 375 Ferrari 375 4.5 V12 ALB DNQ PRI ESS MID ROU CRY AVU USF LAC BRI CHE SAB NEW CAD RED SKA LON MOD MAD JOE CUR 1955 Scuderia Lancia Lancia D50 Lancia DS50 2.5 V8 VAL 1 PAU 5 GLO BOR INT NAP 1 ALB CUR COR LON DRT RED DTT OUL AVO SYR Source: * Indicates shared drive with Luigi Villoresi † Indicates shared drive with André Simon ‡ Indicates shared drive with Sergio Sighinolfi Complete 24 Hours\n\n[NEW 5] Italian racing driver (born 1961) Pierluigi Martini Martini in 2016 Born ( 1961-04-23 ) 23 April 1961 (age 64) Lugo, Emilia-Romagna , Italy Relatives Oliver Martini (brother) Giancarlo Martini (uncle) Formula One World Championship career Nationality Italian Active years 1984 – 1985 , 1988 – 1995 Teams Toleman , Minardi , Scuderia Italia Entries 124 (118 starts) Championships 0 Wins 0 Podiums 0 Career points 18 Pole positions 0 Fastest laps 0 First entry 1984 Italian Grand Prix Last entry 1995 German Grand Prix 24 Hours of Le Mans career Years 1984 , 1996 – 1999 Teams Lancia , Joest , Italia , BMW Best finish 1st ( 1999 ) Class wins 1 ( 1999 ) Pierluigi Martini ( Italian pronunciation: [ˌpjɛrluˈiːdʒi marˈtiːni] ; born 23 April 1961) is an Italian former raci\n\n[NEW 6] Formula One racing car Racing car model Minardi M194 Category Formula One Constructor Minardi Designers Aldo Costa (Technical Director) Mauro Gennaro (Chief Designer) Rene Hilhorst (Chief Aerodynamicist) Predecessor M193 Successor M195 Technical specifications Chassis Carbon fibre monocoque Engine Ford HBC7 / Ford HBC8 , 3,494 cc (213.2 cu in), 75° V8 , NA , mid-engine , longitudinally mounted Transmission Minardi / XTrac T 6-speed semi-automatic Fuel Agip Tyres Goodyear Competition history Notable entrants Minardi Scuderia Italia Notable drivers 23. Pierluigi Martini 24. Michele Alboreto Debut 1994 Canadian Grand Prix Races Wins Podiums Poles F/Laps 11 0 0 0 0 Constructors' Championships 0 Drivers' Championships 0 The Minardi M194 was a Formula One car desi\n\n[NEW 7] well, with British-built Cooper , BRM , Lotus , Brabham and McLaren cars as well as French-built Matra cars entered by the British privateer team Matra International of Ken Tyrrell . DFV engine, 12-cylinder engines, and the arrival of sponsorship, safety, and aerodynamics (1968–1976) See 1968 season , 1969 season , 1970 season , 1971 season , 1972 season , 1973 season , 1974 season , 1975 season and 1976 season . The Lotus 49 , the second F1 car to appear in a sponsor's livery , at a demonstration run in 2005 In 1968, Lotus lost its exclusive right to use the DFV. McLaren built a DFV-powered car and a new force appeared on the scene when Ken Tyrrell entered his team using Cosworth-powered French Matra chassis driven by ex-BRM Jackie Stewart as lead driver. C\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 7 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1,1,0,0,1,1,0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2093, + "osl": 801, + "total_tokens": 2894, + "latency_ms": 5665.46, + "tokens_per_second": 141.38 + }, + "context": { + "num_documents_evaluated": 7, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "a9ae701a-59d7-4738-9cc8-dd57cc52b130", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:19:21.647319Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What Formula One car was driven in 1994 by the nephew of a racing driver from Italy who drove a Ferrari 312T and shares a last name with a common cocktail drink?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Ret 4 Ret Ret 16 Ret 11 10 Ret 3 13 Ivan Capelli Ret Ret 5 10 † Ret Ret Ret Ret 9 Ret 6 Ret Ret Ret 3 14 Thierry Boutsen Ret 10 Ret Ret Ret 12 10 Ret 10 7 Ret Ret Ret 8 Ret 5 2 15 Johnny Herbert 6 7 Ret Ret Ret Ret Ret 6 Ret Ret Ret 13 † Ret Ret Ret 13 2 16 Pierluigi Martini Ret Ret Ret 6 6 Ret 8 10 15 11 Ret Ret 8 Ret 10 Ret 2 17 Stefano Modena DNQ Ret Ret DNQ Ret Ret Ret Ret Ret DNQ Ret 15 DNQ 13 7 6 1 18 Christian Fittipaldi Ret Ret Ret 11 Ret 8 13 DNQ DNQ DNQ 12 6 9 1 19 Bertrand Gachot Ret 11 Ret Ret Ret 6 DSQ Ret Ret 14 Ret 18 † Ret Ret Ret Ret 1 — Aguri Suzuki 8 DNQ Ret 7 10 11 DNQ Ret 12 Ret Ret 9 Ret 10 8 8 0 — JJ Lehto Ret 8 8 Ret 11 † 9 9 9 13 10 DNQ 7 11 † Ret 9 Ret 0 — Gianni Morbidelli Ret Ret 7 Ret Ret Ret 11 8 17 † 12 DNQ 16 Ret 14 14 10 0 —\n\n[DOC 2] Italian racing driver Giancarlo Martini Nationality Italian Born ( 1947-08-16 ) 16 August 1947 Lavezzola , Italy Died 26 March 2013 (2013-03-26) (aged 65) Forlì , Italy Relatives Pierluigi Martini Oliver Martini European Formula Two Championship Years active 1974–1979 Starts 42 Wins 0 Poles 0 Fastest laps 0 Best finish 7th in 1976 Previous series 1978 1972–1973 British Formula One Formula Italia Championship titles 1973 Formula Italia British Formula One Championship career Active years 1978 Entries 2 Championships 0 Wins 1 Podiums 1 Career points 32 Pole positions 1 Fastest laps 1 Martini driving the Scuderia Everest Ferrari 312T in the 1976 BRDC International Trophy race at Silverstone . Giancarlo Martini (16 August 1947 – 26 March 2013) was a racing drive\n\n[DOC 3] Italian racing driver (born 1961) Pierluigi Martini Martini in 2016 Born ( 1961-04-23 ) 23 April 1961 (age 64) Lugo, Emilia-Romagna , Italy Relatives Oliver Martini (brother) Giancarlo Martini (uncle) Formula One World Championship career Nationality Italian Active years 1984 – 1985 , 1988 – 1995 Teams Toleman , Minardi , Scuderia Italia Entries 124 (118 starts) Championships 0 Wins 0 Podiums 0 Career points 18 Pole positions 0 Fastest laps 0 First entry 1984 Italian Grand Prix Last entry 1995 German Grand Prix 24 Hours of Le Mans career Years 1984 , 1996 – 1999 Teams Lancia , Joest , Italia , BMW Best finish 1st ( 1999 ) Class wins 1 ( 1999 ) Pierluigi Martini ( Italian pronunciation: [ˌpjɛrluˈiːdʒi marˈtiːni] ; born 23 April 1961) is an Italian former raci\n\n[DOC 4] Formula One racing car Racing car model Minardi M194 Category Formula One Constructor Minardi Designers Aldo Costa (Technical Director) Mauro Gennaro (Chief Designer) Rene Hilhorst (Chief Aerodynamicist) Predecessor M193 Successor M195 Technical specifications Chassis Carbon fibre monocoque Engine Ford HBC7 / Ford HBC8 , 3,494 cc (213.2 cu in), 75° V8 , NA , mid-engine , longitudinally mounted Transmission Minardi / XTrac T 6-speed semi-automatic Fuel Agip Tyres Goodyear Competition history Notable entrants Minardi Scuderia Italia Notable drivers 23. Pierluigi Martini 24. Michele Alboreto Debut 1994 Canadian Grand Prix Races Wins Podiums Poles F/Laps 11 0 0 0 0 Constructors' Championships 0 Drivers' Championships 0 The Minardi M194 was a Formula One car desi\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": true,\n \"reasoning\": \"Document 2 identifies Giancarlo Martini as an Italian driver who drove a Ferrari 312T and whose surname matches the cocktail 'Martini'. Document 3 shows Pierluigi Martini is Giancarlo's nephew and was active in Formula One through 1994. Document 4 describes the Minardi M194 as a 1994 Formula One car and lists Pierluigi Martini as a notable driver of that car. Combining these facts answers the question: the car is the Minardi M194.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1273, + "osl": 120, + "total_tokens": 1393, + "latency_ms": 3861.76, + "tokens_per_second": 31.07 + }, + "context": { + "kept_docs_count": 4, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "2d4416ae-960b-4bc4-8552-47c4f9f43046", + "component": "answer_generator", + "hop_count": 2, + "timestamp": "2026-05-16T03:19:25.091499Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: What Formula One car was driven in 1994 by the nephew of a racing driver from Italy who drove a Ferrari 312T and shares a last name with a common cocktail drink?\n\nDOCUMENTS:\n\n[DOC 1] Ret 4 Ret Ret 16 Ret 11 10 Ret 3 13 Ivan Capelli Ret Ret 5 10 † Ret Ret Ret Ret 9 Ret 6 Ret Ret Ret 3 14 Thierry Boutsen Ret 10 Ret Ret Ret 12 10 Ret 10 7 Ret Ret Ret 8 Ret 5 2 15 Johnny Herbert 6 7 Ret Ret Ret Ret Ret 6 Ret Ret Ret 13 † Ret Ret Ret 13 2 16 Pierluigi Martini Ret Ret Ret 6 6 Ret 8 10 15 11 Ret Ret 8 Ret 10 Ret 2 17 Stefano Modena DNQ Ret Ret DNQ Ret Ret Ret Ret Ret DNQ Ret 15 DNQ 13 7 6 1 18 Christian Fittipaldi Ret Ret Ret 11 Ret 8 13 DNQ DNQ DNQ 12 6 9 1 19 Bertrand Gachot Ret 11 Ret Ret Ret 6 DSQ Ret Ret 14 Ret 18 † Ret Ret Ret Ret 1 — Aguri Suzuki 8 DNQ Ret 7 10 11 DNQ Ret 12 Ret Ret 9 Ret 10 8 8 0 — JJ Lehto Ret 8 8 Ret 11 † 9 9 9 13 10 DNQ 7 11 † Ret 9 Ret 0 — Gianni Morbidelli Ret Ret 7 Ret Ret Ret 11 8 17 † 12 DNQ 16 Ret 14 14 10 0 —\n\n[DOC 2] Italian racing driver Giancarlo Martini Nationality Italian Born ( 1947-08-16 ) 16 August 1947 Lavezzola , Italy Died 26 March 2013 (2013-03-26) (aged 65) Forlì , Italy Relatives Pierluigi Martini Oliver Martini European Formula Two Championship Years active 1974–1979 Starts 42 Wins 0 Poles 0 Fastest laps 0 Best finish 7th in 1976 Previous series 1978 1972–1973 British Formula One Formula Italia Championship titles 1973 Formula Italia British Formula One Championship career Active years 1978 Entries 2 Championships 0 Wins 1 Podiums 1 Career points 32 Pole positions 1 Fastest laps 1 Martini driving the Scuderia Everest Ferrari 312T in the 1976 BRDC International Trophy race at Silverstone . Giancarlo Martini (16 August 1947 – 26 March 2013) was a racing drive\n\n[DOC 3] Italian racing driver (born 1961) Pierluigi Martini Martini in 2016 Born ( 1961-04-23 ) 23 April 1961 (age 64) Lugo, Emilia-Romagna , Italy Relatives Oliver Martini (brother) Giancarlo Martini (uncle) Formula One World Championship career Nationality Italian Active years 1984 – 1985 , 1988 – 1995 Teams Toleman , Minardi , Scuderia Italia Entries 124 (118 starts) Championships 0 Wins 0 Podiums 0 Career points 18 Pole positions 0 Fastest laps 0 First entry 1984 Italian Grand Prix Last entry 1995 German Grand Prix 24 Hours of Le Mans career Years 1984 , 1996 – 1999 Teams Lancia , Joest , Italia , BMW Best finish 1st ( 1999 ) Class wins 1 ( 1999 ) Pierluigi Martini ( Italian pronunciation: [ˌpjɛrluˈiːdʒi marˈtiːni] ; born 23 April 1961) is an Italian former raci\n\n[DOC 4] Formula One racing car Racing car model Minardi M194 Category Formula One Constructor Minardi Designers Aldo Costa (Technical Director) Mauro Gennaro (Chief Designer) Rene Hilhorst (Chief Aerodynamicist) Predecessor M193 Successor M195 Technical specifications Chassis Carbon fibre monocoque Engine Ford HBC7 / Ford HBC8 , 3,494 cc (213.2 cu in), 75° V8 , NA , mid-engine , longitudinally mounted Transmission Minardi / XTrac T 6-speed semi-automatic Fuel Agip Tyres Goodyear Competition history Notable entrants Minardi Scuderia Italia Notable drivers 23. Pierluigi Martini 24. Michele Alboreto Debut 1994 Canadian Grand Prix Races Wins Podiums Poles F/Laps 11 0 0 0 0 Constructors' Championships 0 Drivers' Championships 0 The Minardi M194 was a Formula One car desi\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "The nephew, Pierluigi Martini, drove the **Minardi M194** Formula One car in 1994.", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1128, + "osl": 31, + "total_tokens": 1159, + "latency_ms": 3443.56, + "tokens_per_second": 9.0 + }, + "context": { + "num_documents_used": 4, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:19:25.092373Z", + "summary": { + "total_llm_calls": 4, + "total_hops": 2, + "total_input_tokens": 4953, + "total_output_tokens": 2259, + "total_tokens": 7212, + "total_latency_ms": 41397.66, + "average_tokens_per_second": 56.86, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Minardi_M194", + "https://en.wikipedia.org/wiki/Pierluigi_Martini", + "https://en.wikipedia.org/wiki/Giancarlo_Martini", + "https://en.wikipedia.org/wiki/List_of_cocktails" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "The nephew, Pierluigi Martini, drove the **Minardi M194** Formula One car in 1994.", + "ground_truth_answer": "Minardi M194" + } + }, + { + "query_id": "42", + "original_query": "As of August 1, 2024, who is the president of the team that inspired the original name of the Washington Commanders?", + "timestamp_start": "2026-05-16T03:19:25.128284Z", + "llm_calls": [ + { + "call_id": "5c9ad1e7-d50a-406b-ab55-bb92ce772df0", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:19:31.085638Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 1, 2024, who is the president of the team that inspired the original name of the Washington Commanders?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Atlanta Braves president\",\n \"Washington Commanders original name Boston Braves\",\n \"Boston Braves original name football team\"\n ],\n \"feedback\": \"Need to confirm that the Washington Commanders were originally named the Boston Braves (inspired by the Boston Braves baseball team) and identify the current president of that baseball franchise (Atlanta Braves).\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 463, + "osl": 799, + "total_tokens": 1262, + "latency_ms": 5957.19, + "tokens_per_second": 134.12 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "a5919b3d-ac6f-419f-bd23-fe5d7f6f918e", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:19:50.171609Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of August 1, 2024, who is the president of the team that inspired the original name of the Washington Commanders?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] to international signing rule violations, general manager John Coppolella resigned and faced a baseball ban. Alex Anthopoulos took over as the new general manager. The team's chairman, Terry McGuirk , apologized for the scandal and expressed confidence in Anthopoulos' integrity. A new on field mascot named Blooper was introduced at a fan event before the 2017 season. Under Anthopoulos, the Braves made the playoffs in six of his first seven seasons. In 2020 the Braves reached the National League Championship Series , but ultimately lost to the Dodgers after leading 3–1. President Joe Biden hosts the 2021 World Series Champion Atlanta Braves on September 26, 2022, in the East Room of the White House . In the 2021 season, the Braves won the National League East\n\n[NEW 2] Major League Baseball season Major League Baseball team season 2021 Atlanta Braves World Series champions National League champions National League East champions League National League Division East Ballpark Truist Park City Atlanta Record 88–73 (.547) Divisional place 1st Owners Liberty Media / John Malone General manager Alex Anthopoulos Manager Brian Snitker Television Bally Sports Southeast Bally Sports South ( Chip Caray , Jeff Francoeur , Paul Byrd , Tom Glavine , Dale Murphy , Joe Simpson , Kelly Crull ) Radio 680 The Fan Rock 100.5 Atlanta Braves Radio Network (Ben Ingram, Jim Powell , Joe Simpson ) 1600 La Mejor (Daniel Cantú, Emanuel Zamarrón) ← 2020 Seasons 2022 → The 2021 Atlanta Braves season was the 151st season of the Atlanta Braves franchise\n\n[NEW 3] National Football League franchise based in the Washington, D. C., area Washington Commanders Current season Logo Wordmark Uniforms General information Founded July 9, 1932 ; 93 years ago ( 1932-07-09 ) Inaugural season 1932 Stadium Northwest Stadium ( Landover, Maryland ) Headquartered Ashburn, Virginia Colors Burgundy, gold, white, black Fight song \" Hail to the Commanders \" Mascot Major Tuddy Website commanders . com Personnel Owner Josh Harris General manager Adam Peters Head coach Dan Quinn President Mark Clouse Nicknames The Burgundy and Gold The 'Skins (as the Redskins) The Over-the-Hill Gang (1970s) The Fun Bunch (1980s receiving group) The Hogs (1980s offensive line) Team history Boston Braves ( 1932 ) Boston Redskins ( 1933 – 1936 ) Washington Reds\n\n[NEW 4] Former American baseball team Boston Braves Logo Cap insignia Information League National League (1876–1952) Ballpark Braves Field ( 1915 – 1952 ) Established 1871 Relocated 1953 (to Milwaukee ; became the Milwaukee Braves ) World Series championships 1 1914 Pre-modern World Series championships 1 1892 National League pennants 10 1877 1878 1883 1891 1892 1893 1897 1898 1914 1948 National Association pennants 4 1872 1873 1874 1875 Former name Boston Bees ( 1936 – 1940 ) Boston Braves ( 1912 – 1935 ); ( 1941 – 1952 ) Boston Rustlers ( 1911 ) Boston Doves ( 1907 – 1910 ) Boston Beaneaters ( 1883 – 1906 ) Boston Red Caps ( 1876 – 1882 ) Boston Red Stockings ( 1871 – 1875 ) Former league National Association (1871–1876) Former ballparks Fenway Park ( 1914 – 1915\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 4 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 1, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1042, + "osl": 1044, + "total_tokens": 2086, + "latency_ms": 18529.51, + "tokens_per_second": 56.34 + }, + "context": { + "num_documents_evaluated": 4, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "53d7ab8a-4335-44af-9e29-fc37dad1bb39", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:19:59.480644Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 1, 2024, who is the president of the team that inspired the original name of the Washington Commanders?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] National Football League franchise based in the Washington, D. C., area Washington Commanders Current season Logo Wordmark Uniforms General information Founded July 9, 1932 ; 93 years ago ( 1932-07-09 ) Inaugural season 1932 Stadium Northwest Stadium ( Landover, Maryland ) Headquartered Ashburn, Virginia Colors Burgundy, gold, white, black Fight song \" Hail to the Commanders \" Mascot Major Tuddy Website commanders . com Personnel Owner Josh Harris General manager Adam Peters Head coach Dan Quinn President Mark Clouse Nicknames The Burgundy and Gold The 'Skins (as the Redskins) The Over-the-Hill Gang (1970s) The Fun Bunch (1980s receiving group) The Hogs (1980s offensive line) Team history Boston Braves ( 1932 ) Boston Redskins ( 1933 – 1936 ) Washington Reds\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The provided document only lists the president of the Washington Commanders (Mark Clouse) and mentions the team history (Boston Braves, Boston Redskins). It does not identify the team that inspired the original name, nor does it provide the president of that other team (e.g., Boston Braves/Atlanta Braves). Therefore, the necessary information to answer the question is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 487, + "osl": 1135, + "total_tokens": 1622, + "latency_ms": 9308.04, + "tokens_per_second": 121.94 + }, + "context": { + "kept_docs_count": 1, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "26b3e45e-90bc-41c8-b69b-c7c1c6239b84", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:20:06.206456Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 1, 2024, who is the president of the team that inspired the original name of the Washington Commanders?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] National Football League franchise based in the Washington, D. C., area Washington Commanders Current season Logo Wordmark Uniforms General information Founded July 9, 1932 ; 93 years ago ( 1932-07-09 ) Inaugural season 1932 Stadium Northwest Stadium ( Landover, Maryland ) Headquartered Ashburn, Virginia Colors Burgundy, gold, white, black Fight song \" Hail to the Commanders \" Mascot Major Tuddy Website commanders . com Personnel Owner Josh Harris General manager Adam Peters Head coach Dan Quinn President Mark Clouse Nicknames The Burgundy and Gold The 'Skins (as the Redskins) The Over-the-Hill Gang (1970s) The Fun Bunch (1980s receiving group) The Hogs (1980s offensive line) Team history Boston Braves ( 1932 ) Boston Redskins ( 1933 – 1936 ) Washington Reds\n\n\nSEARCH HISTORY: - Query: 'Atlanta Braves president' → 2 docs\n- Query: 'Washington Commanders original name Boston Braves' → 1 docs\n- Query: 'Boston Braves original name football team' → 1 docs\n\nFEEDBACK: Need to confirm that the Washington Commanders were originally named the Boston Braves (inspired by the Boston Braves baseball team) and identify the current president of that baseball franchise (Atlanta Braves).\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Atlanta Braves president\",\n \"Atlanta Braves president 2024\",\n \"Washington Commanders original name Boston Braves origin\"\n ],\n \"feedback\": \"We need to confirm the current president of the Atlanta Braves (the baseball team that inspired the original \\\"Boston Braves\\\" name of the Washington Commanders) and verify that the Commanders' original name was indeed taken from the Boston Braves baseball franchise.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 662, + "osl": 89, + "total_tokens": 751, + "latency_ms": 6725.2, + "tokens_per_second": 13.23 + }, + "context": { + "kept_docs_count": 1, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "c722dfc5-a6e6-4333-9959-6aba38f94025", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:20:09.510773Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 1, 2024, who is the president of the team that inspired the original name of the Washington Commanders?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] National Football League franchise based in the Washington, D. C., area Washington Commanders Current season Logo Wordmark Uniforms General information Founded July 9, 1932 ; 93 years ago ( 1932-07-09 ) Inaugural season 1932 Stadium Northwest Stadium ( Landover, Maryland ) Headquartered Ashburn, Virginia Colors Burgundy, gold, white, black Fight song \" Hail to the Commanders \" Mascot Major Tuddy Website commanders . com Personnel Owner Josh Harris General manager Adam Peters Head coach Dan Quinn President Mark Clouse Nicknames The Burgundy and Gold The 'Skins (as the Redskins) The Over-the-Hill Gang (1970s) The Fun Bunch (1980s receiving group) The Hogs (1980s offensive line) Team history Boston Braves ( 1932 ) Boston Redskins ( 1933 – 1936 ) Washington Reds\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept document provides the President of the Washington Commanders (Mark Clouse) but does not identify which other team inspired the original name (Washington Redskins) nor give that team's president as of August 1, 2024. Additional information about the inspiring team and its president is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 489, + "osl": 452, + "total_tokens": 941, + "latency_ms": 2757.26, + "tokens_per_second": 163.93 + }, + "context": { + "kept_docs_count": 1, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "7a1ca458-7a9a-4a75-90a9-5d95341b4543", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:20:14.118537Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 1, 2024, who is the president of the team that inspired the original name of the Washington Commanders?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] National Football League franchise based in the Washington, D. C., area Washington Commanders Current season Logo Wordmark Uniforms General information Founded July 9, 1932 ; 93 years ago ( 1932-07-09 ) Inaugural season 1932 Stadium Northwest Stadium ( Landover, Maryland ) Headquartered Ashburn, Virginia Colors Burgundy, gold, white, black Fight song \" Hail to the Commanders \" Mascot Major Tuddy Website commanders . com Personnel Owner Josh Harris General manager Adam Peters Head coach Dan Quinn President Mark Clouse Nicknames The Burgundy and Gold The 'Skins (as the Redskins) The Over-the-Hill Gang (1970s) The Fun Bunch (1980s receiving group) The Hogs (1980s offensive line) Team history Boston Braves ( 1932 ) Boston Redskins ( 1933 – 1936 ) Washington Reds\n\n\nSEARCH HISTORY: - Query: 'Atlanta Braves president' → 2 docs\n- Query: 'Washington Commanders original name Boston Braves' → 1 docs\n- Query: 'Boston Braves original name football team' → 1 docs\n- Query: 'Atlanta Braves president' → 0 docs\n- Query: 'Atlanta Braves president 2024' → 0 docs\n- Query: 'Washington Commanders original name Boston Braves origin' → 0 docs\n\nFEEDBACK: Need to confirm that the Washington Commanders were originally named the Boston Braves (inspired by the Boston Braves baseball team) and identify the current president of that baseball franchise (Atlanta Braves).\nWe need to confirm the current president of the Atlanta Braves (the baseball team that inspired the original \"Boston Braves\" name of the Washington Commanders) and verify that the Commanders' original name was indeed taken from the Boston Braves baseball franchise.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Washington Commanders name origin Boston Braves\",\n \"Atlanta Braves president Terry McGuirk\",\n \"Atlanta Braves leadership\"\n ],\n \"feedback\": \"Need confirmation that the Commanders' original name was taken from the Boston Braves baseball team and the current president of that franchise (Atlanta Braves).\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 758, + "osl": 68, + "total_tokens": 826, + "latency_ms": 4607.03, + "tokens_per_second": 14.76 + }, + "context": { + "kept_docs_count": 1, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "3a8cb05d-7713-43ff-8e2c-57feb8065d9c", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T03:20:22.585778Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of August 1, 2024, who is the president of the team that inspired the original name of the Washington Commanders?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] WAA 79.69% 2011 Pat Gillick EXEC Toronto Blue Jays 1978–2008 VC (EE) 81.3% 2012 Barry Larkin SS Cincinnati Reds 1986–2004 BBWAA 86.39% 2012 Ron Santo 3B Chicago Cubs 1960–1974 VC (GE) 93.75% 2013 Hank O'Day UMP — 1895–1927 VC (PI) 93.8% 2013 Jacob Ruppert EXEC New York Yankees 1915–1938 VC (PI) 93.8% 2013 Deacon White 3B Buffalo Bisons 1871–1890 VC (PI) 87.5% 2014 Bobby Cox MGR Atlanta Braves 1978–2010 VC (EE) 100% 2014 Tom Glavine P Atlanta Braves 1987–2008 BBWAA 91.94% 2014 Tony La Russa MGR St. Louis Cardinals 1979–2011 2021–2022 VC (EE) 100% 2014 Greg Maddux P Atlanta Braves 1986–2008 BBWAA 97.2% 2014 Frank Thomas 1B Chicago White Sox 1990–2008 BBWAA 83.71% 2014 Joe Torre MGR New York Yankees 1977–2010 VC (EE) 100% 2015 Craig Biggio 2B Houston Astros 198\n\n\nKEPT DOCUMENTS (context only):\n[1 documents already kept as relevant]\n[KEPT 1] National Football League franchise based in the Washington, D. C., area Washington Commanders Current season Logo Wordmark Uniforms General information Founded July 9, 1932 ; 93 years ago ( 1932-07-09 ) Inaugural season 1932 Stadium Northwest Stadium ( Landover, Maryland ) Headquartered Ashburn, Vir...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 671, + "osl": 529, + "total_tokens": 1200, + "latency_ms": 7937.8, + "tokens_per_second": 66.64 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 1 + } + }, + { + "call_id": "ee24d165-bc10-4a0b-9934-c258510438c1", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:20:37.599188Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 1, 2024, who is the president of the team that inspired the original name of the Washington Commanders?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] National Football League franchise based in the Washington, D. C., area Washington Commanders Current season Logo Wordmark Uniforms General information Founded July 9, 1932 ; 93 years ago ( 1932-07-09 ) Inaugural season 1932 Stadium Northwest Stadium ( Landover, Maryland ) Headquartered Ashburn, Virginia Colors Burgundy, gold, white, black Fight song \" Hail to the Commanders \" Mascot Major Tuddy Website commanders . com Personnel Owner Josh Harris General manager Adam Peters Head coach Dan Quinn President Mark Clouse Nicknames The Burgundy and Gold The 'Skins (as the Redskins) The Over-the-Hill Gang (1970s) The Fun Bunch (1980s receiving group) The Hogs (1980s offensive line) Team history Boston Braves ( 1932 ) Boston Redskins ( 1933 – 1936 ) Washington Reds\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept document provides the President of the Washington Commanders (Mark Clouse) but does not identify the team that inspired the original name of the Commanders nor the President of that team. Therefore, the required fact about the president of the team that inspired the original name is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 476, + "osl": 742, + "total_tokens": 1218, + "latency_ms": 15012.71, + "tokens_per_second": 49.42 + }, + "context": { + "kept_docs_count": 1, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "3d0ef26d-f9a4-45f0-afb4-8991d810886e", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:20:43.310330Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 1, 2024, who is the president of the team that inspired the original name of the Washington Commanders?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] National Football League franchise based in the Washington, D. C., area Washington Commanders Current season Logo Wordmark Uniforms General information Founded July 9, 1932 ; 93 years ago ( 1932-07-09 ) Inaugural season 1932 Stadium Northwest Stadium ( Landover, Maryland ) Headquartered Ashburn, Virginia Colors Burgundy, gold, white, black Fight song \" Hail to the Commanders \" Mascot Major Tuddy Website commanders . com Personnel Owner Josh Harris General manager Adam Peters Head coach Dan Quinn President Mark Clouse Nicknames The Burgundy and Gold The 'Skins (as the Redskins) The Over-the-Hill Gang (1970s) The Fun Bunch (1980s receiving group) The Hogs (1980s offensive line) Team history Boston Braves ( 1932 ) Boston Redskins ( 1933 – 1936 ) Washington Reds\n\n\nSEARCH HISTORY: - Query: 'Atlanta Braves president' → 2 docs\n- Query: 'Washington Commanders original name Boston Braves' → 1 docs\n- Query: 'Boston Braves original name football team' → 1 docs\n- Query: 'Atlanta Braves president' → 0 docs\n- Query: 'Atlanta Braves president 2024' → 0 docs\n- Query: 'Washington Commanders original name Boston Braves origin' → 0 docs\n- Query: 'Washington Commanders name origin Boston Braves' → 0 docs\n- Query: 'Atlanta Braves president Terry McGuirk' → 1 docs\n- Query: 'Atlanta Braves leadership' → 0 docs\n\nFEEDBACK: Need to confirm that the Washington Commanders were originally named the Boston Braves (inspired by the Boston Braves baseball team) and identify the current president of that baseball franchise (Atlanta Braves).\nWe need to confirm the current president of the Atlanta Braves (the baseball team that inspired the original \"Boston Braves\" name of the Washington Commanders) and verify that the Commanders' original name was indeed taken from the Boston Braves baseball franchise.\nNeed confirmation that the Commanders' original name was taken from the Boston Braves baseball team and the current president of that franchise (Atlanta Braves).\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Washington Commanders name history\",\n \"Washington Commanders Boston Braves origin\",\n \"Terry McGuirk Atlanta Braves president\"\n ],\n \"feedback\": \"We need a source confirming that the franchise was originally named the Boston Braves (inspired by the baseball team) and a source confirming the current president of the Atlanta Braves as of August 1 2024.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 894, + "osl": 449, + "total_tokens": 1343, + "latency_ms": 5710.49, + "tokens_per_second": 78.63 + }, + "context": { + "kept_docs_count": 1, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "fd550deb-1cf4-40d2-824d-3e5a731c5570", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:20:48.197645Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 1, 2024, who is the president of the team that inspired the original name of the Washington Commanders?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] National Football League franchise based in the Washington, D. C., area Washington Commanders Current season Logo Wordmark Uniforms General information Founded July 9, 1932 ; 93 years ago ( 1932-07-09 ) Inaugural season 1932 Stadium Northwest Stadium ( Landover, Maryland ) Headquartered Ashburn, Virginia Colors Burgundy, gold, white, black Fight song \" Hail to the Commanders \" Mascot Major Tuddy Website commanders . com Personnel Owner Josh Harris General manager Adam Peters Head coach Dan Quinn President Mark Clouse Nicknames The Burgundy and Gold The 'Skins (as the Redskins) The Over-the-Hill Gang (1970s) The Fun Bunch (1980s receiving group) The Hogs (1980s offensive line) Team history Boston Braves ( 1932 ) Boston Redskins ( 1933 – 1936 ) Washington Reds\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept document provides the president of the Washington Commanders (Mark Clouse) but does not identify which team inspired the original name of the Commanders, nor does it give any information about the president of that inspiring team (the Boston Braves/Atlanta Braves). Therefore, the required fact—who is the president of the team that inspired the original name—as of August 1 2024 is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 430, + "osl": 97, + "total_tokens": 527, + "latency_ms": 4342.8, + "tokens_per_second": 22.34 + }, + "context": { + "kept_docs_count": 1, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "b13ec70c-1859-4a89-a2f1-60eeec2b952e", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:21:01.618252Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: As of August 1, 2024, who is the president of the team that inspired the original name of the Washington Commanders?\n\nDOCUMENTS:\n\n[DOC 1] National Football League franchise based in the Washington, D. C., area Washington Commanders Current season Logo Wordmark Uniforms General information Founded July 9, 1932 ; 93 years ago ( 1932-07-09 ) Inaugural season 1932 Stadium Northwest Stadium ( Landover, Maryland ) Headquartered Ashburn, Virginia Colors Burgundy, gold, white, black Fight song \" Hail to the Commanders \" Mascot Major Tuddy Website commanders . com Personnel Owner Josh Harris General manager Adam Peters Head coach Dan Quinn President Mark Clouse Nicknames The Burgundy and Gold The 'Skins (as the Redskins) The Over-the-Hill Gang (1970s) The Fun Bunch (1980s receiving group) The Hogs (1980s offensive line) Team history Boston Braves ( 1932 ) Boston Redskins ( 1933 – 1936 ) Washington Reds\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Mark Clouse.", + "finish_reason": "stop" + }, + "metrics": { + "isl": 330, + "osl": 568, + "total_tokens": 898, + "latency_ms": 13419.81, + "tokens_per_second": 42.33 + }, + "context": { + "num_documents_used": 1, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:21:01.619011Z", + "summary": { + "total_llm_calls": 11, + "total_hops": 5, + "total_input_tokens": 6702, + "total_output_tokens": 5972, + "total_tokens": 12674, + "total_latency_ms": 94307.84, + "average_tokens_per_second": 69.43, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Washington_Commanders", + "https://en.wikipedia.org/wiki/Boston_Braves", + "https://en.wikipedia.org/wiki/Atlanta_Braves" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Mark Clouse.", + "ground_truth_answer": "Derek Schiller" + } + }, + { + "query_id": "43", + "original_query": "As of 2023, how many more employees does the company alphabetically first by ticker symbol in the S&P500 have than the company alphabetically 2nd to last by ticker symbol in the S&P500?", + "timestamp_start": "2026-05-16T03:21:01.654043Z", + "llm_calls": [ + { + "call_id": "2049e8e8-84ad-44b2-82ac-5b2e6d1177d4", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:21:26.804100Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of 2023, how many more employees does the company alphabetically first by ticker symbol in the S&P500 have than the company alphabetically 2nd to last by ticker symbol in the S&P500?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"S&P 500 ticker symbols\",\n \"Agilent Technologies employees\",\n \"Zoetis employees\"\n ],\n \"feedback\": \"First query will retrieve the full list of S&P 500 constituents with their ticker symbols, allowing identification of the alphabetically first ticker (likely \\\"A\\\" for Agilent Technologies) and the second‑to‑last ticker (likely \\\"ZTS\\\" for Zoetis). The second and third queries fetch the employee counts for those two companies as of 2023, enabling the calculation of the difference.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 466, + "osl": 999, + "total_tokens": 1465, + "latency_ms": 25149.94, + "tokens_per_second": 39.72 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "ae4c09ff-fe62-41bb-883a-3927f188e855", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:21:31.985135Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of 2023, how many more employees does the company alphabetically first by ticker symbol in the S&P500 have than the company alphabetically 2nd to last by ticker symbol in the S&P500?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] b c ^ ^ ^ a b External links Standard & Poor's page on S&P 500 index\n\n[NEW 2] ▄▄▄ ▄▄▄ ▄▄▄ ▄ , which some telegraphers unofficially use for an exclamation mark ! , is shared with unofficial letters Ö , Ó , and Ø used in some non-Latin alphabets. Currency symbols The ITU has never formally codified any currency symbols into Morse code: The unambiguous ISO 4217 currency codes are preferred for transmission (e. g. CNY , EUR , GBP , JPY , KRW , USD , etc.). The dollar sign ($) was represented in the Phillips Code collection of abbreviations as two characters \" SX \". Eventually operators dropped the intervening space and merged the two letter code or abbreviation into the single unofficial punctuation encoding SX ( ▄ ▄ ▄ ▄▄▄ ▄ ▄ ▄▄▄ ). Ampersand ( & ) The suggested unofficial encoding of the ampersand & sign listed above, often shown as AS\n\n[NEW 3] American technology company Agilent Technologies, Inc. Headquarters lobby in Santa Clara, California Company type Public Traded as NYSE : A S&P 500 component Industry Healthcare equipment and services Predecessors Test & Measurement, Semiconductor Groups of HP Founded 1999 ; 27 years ago ( 1999 ) Headquarters Santa Clara, California , U. S. Area served Worldwide Key people Padraig McDonnell ( CEO ) Koh Boon Hwee ( chairman ) Products Instruments, software, services and consumables for laboratory use Revenue US$ 6.95 billion (2025) Operating income US$1.48 billion (2025) Net income US$1.30 billion (2025) Total assets US$12.7 billion (2025) Total equity US$6.74 billion (2025) Number of employees 18,100 (2025) Divisions Agilent CrossLab Group Diagnostics & Geno\n\n[NEW 4] sident of business operations – Bryan Wiedmeier Vice president of player personnel – Rick Spielman Director of college scouting – Ron Labadie Director of pro personnel – George Paton Assistant director of pro personnel – Tom Heckert, Sr. Head coaches Head coach – Dave Wannstedt Offensive coaches Offensive coordinator – Chan Gailey Quarterbacks – Mike Shula Running backs – Joel Collier Wide receivers – Robert Ford Tight ends – Pat Jones Offensive line – Paul Boudreau Offensive assistant – Judd Garrett Defensive coaches Defensive coordinator – Jim Bates Defensive line – Clarence Brooks Assistant defensive line/defensive assistant – Robert Nunn Linebackers – Randy Shannon Secondary – Mel Phillips Defensive nickel package – Bill Lewis Special teams coaches Speci\n\n[NEW 5] esident/general manager – Eric DeCosta Executive vice president – Ozzie Newsome Vice president of player personnel – George Kokinis Director of player personnel – Mark Azevedo Director of college scouting – Andrew Raphael Assistant director of college scouting – Joey Cleary Assistant director of pro personnel – Corey Frazier Vice president of football administration – Nick Matteo Senior personnel executive – Bobby Vega Vice president of research and development – David McDonald Director of learning and development – Steve Clagett Director of data and decision science – Derrick Yam Director of football systems – James Oncea Senior director of football information – Megan McLaughlin Consultant – Pat Moriarty Chief of staff to the head coach – Christina Deruyte\n\n[NEW 6] e: # Employer # of employees 1 State of Oklahoma (State Capital) 37,600 2 Tinker Air Force Base 26,000 3 Oklahoma State University-Stillwater 13,940 4 University of Oklahoma-Norman 11,530 5 Integris Health 11,000 6 Amazon 8,000 7 Hobby Lobby Stores (HQ) 6,500 8 Mercy Health Center (HQ) 6,500 9 SSM Health Care (Regional HQ) 5,600 10 FAA Mike Monroney Aeronautical Center 5,150 11 University of Oklahoma Health Sciences Center 5000 12 City of Oklahoma City 4,500 13 OU Medical Center 4,360 14 Paycom (HQ) 4,200 15 The Boeing Company 3,740 16 Midfirst Bank (HQ) 3,100 17 Norman Regional Hospital 2,740 18 AT&T 2,700 19 OGE Energy Corp (HQ) 2,240 20 Dell 2,100 Other major corporations with a significant presence (over 1,000 employees) in the city of Oklahoma City incl\n\n[NEW 7] s employed as the Receptionist, but has character traits similar to other \"Assistant Regional Managers\" ^ Greta is employed in Sales, but is in the story role of being the Senior Sale Rep's love interest. ^ Sofi is employed in customer service, but is in the story role of being the Senior Sale Rep's love interest. ^ Angeles is not an accountant, she is a quality manager, but her role is similar to Angela Martin from the American series. ^ In The Office Saudi Arabia , the receptionists' fiancé is replaced with the receptionists' brother to comply with Sharia law . References ^ ^ a b c d ^ ^ ^ a b ^ ^ ^ a b ^ ^ ^ {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ ^ ^ ^ The 2001 British Academy Television Awards , winners and nominees. ^ The 200\n\n[NEW 8] yne Huizenga President/COO – Bryan Wiedmeier Vice chairman – Don Shula Executive vice president of football operations – Bill Parcells General manager – Jeff Ireland Head coaches Head coach – Tony Sparano Assistant head coach/secondary – Todd Bowles Offensive coaches Offensive coordinator – Dan Henning Quarterbacks – David Lee Running backs – James Saxon Wide receivers – Karl Dorrell Tight ends – George DeLeone Offensive line – Mike Maser Offensive quality control – Steve Bush Defensive coaches Defensive coordinator – Paul Pasqualoni Defensive line – Kacy Rodgers Inside linebackers – George Edwards Outside linebackers – Jim Reid Defensive quality control – David Corrao Special teams coaches Special teams coordinator – John Bonamego Kicking – Steve Hoffman St\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 8 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 0, 1, 0, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1564, + "osl": 1087, + "total_tokens": 2651, + "latency_ms": 4658.72, + "tokens_per_second": 233.33 + }, + "context": { + "num_documents_evaluated": 8, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "6bc935b3-13db-4e16-8323-67c17af107cc", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:21:43.319650Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of 2023, how many more employees does the company alphabetically first by ticker symbol in the S&P500 have than the company alphabetically 2nd to last by ticker symbol in the S&P500?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] b c ^ ^ ^ a b External links Standard & Poor's page on S&P 500 index\n\n[DOC 2] American technology company Agilent Technologies, Inc. Headquarters lobby in Santa Clara, California Company type Public Traded as NYSE : A S&P 500 component Industry Healthcare equipment and services Predecessors Test & Measurement, Semiconductor Groups of HP Founded 1999 ; 27 years ago ( 1999 ) Headquarters Santa Clara, California , U. S. Area served Worldwide Key people Padraig McDonnell ( CEO ) Koh Boon Hwee ( chairman ) Products Instruments, software, services and consumables for laboratory use Revenue US$ 6.95 billion (2025) Operating income US$1.48 billion (2025) Net income US$1.30 billion (2025) Total assets US$12.7 billion (2025) Total equity US$6.74 billion (2025) Number of employees 18,100 (2025) Divisions Agilent CrossLab Group Diagnostics & Geno\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide only a link to an S&P 500 page (DOC 1) without listing ticker symbols, and details for Agilent Technologies (ticker \\\"A\\\", the alphabetically first ticker) with employee count for 2025 (DOC 2). The question requires the 2023 employee counts for both the first ticker (Agilent) and the second‑to‑last ticker in the S&P 500, as well as identification of the second‑to‑last ticker company. None of these facts are present in the kept documents.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 520, + "osl": 474, + "total_tokens": 994, + "latency_ms": 11333.67, + "tokens_per_second": 41.82 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "b69b64b1-b65d-4138-a7d9-79dd2de69780", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:21:51.018090Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of 2023, how many more employees does the company alphabetically first by ticker symbol in the S&P500 have than the company alphabetically 2nd to last by ticker symbol in the S&P500?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] b c ^ ^ ^ a b External links Standard & Poor's page on S&P 500 index\n\n[DOC 2] American technology company Agilent Technologies, Inc. Headquarters lobby in Santa Clara, California Company type Public Traded as NYSE : A S&P 500 component Industry Healthcare equipment and services Predecessors Test & Measurement, Semiconductor Groups of HP Founded 1999 ; 27 years ago ( 1999 ) Headquarters Santa Clara, California , U. S. Area served Worldwide Key people Padraig McDonnell ( CEO ) Koh Boon Hwee ( chairman ) Products Instruments, software, services and consumables for laboratory use Revenue US$ 6.95 billion (2025) Operating income US$1.48 billion (2025) Net income US$1.30 billion (2025) Total assets US$12.7 billion (2025) Total equity US$6.74 billion (2025) Number of employees 18,100 (2025) Divisions Agilent CrossLab Group Diagnostics & Geno\n\n\nSEARCH HISTORY: - Query: 'S&P 500 ticker symbols' → 2 docs\n- Query: 'Agilent Technologies employees' → 1 docs\n- Query: 'Zoetis employees' → 5 docs\n\nFEEDBACK: First query will retrieve the full list of S&P 500 constituents with their ticker symbols, allowing identification of the alphabetically first ticker (likely \"A\" for Agilent Technologies) and the second‑to‑last ticker (likely \"ZTS\" for Zoetis). The second and third queries fetch the employee counts for those two companies as of 2023, enabling the calculation of the difference.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"S&P 500 constituents ticker symbols alphabetical\",\n \"Agilent Technologies employees 2023\",\n \"Zoetis employees 2023\"\n ],\n \"feedback\": \"First query will retrieve the ordered list of S&P 500 ticker symbols to confirm the alphabetically first (likely \\\"A\\\") and the second‑to‑last (likely \\\"ZTS\\\"). The second and third queries will obtain the 2023 employee counts for those two companies, enabling the calculation of the difference.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 750, + "osl": 111, + "total_tokens": 861, + "latency_ms": 7697.76, + "tokens_per_second": 14.42 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "1997deea-b9b1-4965-b6fa-015be090e2e2", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T03:21:57.688494Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of 2023, how many more employees does the company alphabetically first by ticker symbol in the S&P500 have than the company alphabetically 2nd to last by ticker symbol in the S&P500?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] d May 6, 2024. ^ a b McGilligan , p. 231 ^ ^ a b ^ Levy 2025 , p. 7. ^ Smith , p. 116 ^ ^ Schickel , p. 27 ^ a b Zmijewsky and Pfeiffer , p. 12 ^ Eliot , p. 15 ^ ^ a b McGilligan , p. 34 ^ McGilligan , p. 40 ^ a b ^ McGilligan , p. 191 ^ McGilligan , p. 38 ^ Kapsis and Coblentz , p. 123 (interviewer Tim Cahill) ^ McGilligan , p. 36 ^ ^ Eliot , p. 17 ^ ^ Eliot , pp. 18–19 ^ a b McGilligan , p. 49 ^ ^ Schickel , p. 53 ^ McGilligan , p. 50 ^ ^ a b c McGilligan , p. 52 ^ McGilligan , p. 53 ^ a b McGilligan , p. 60 ^ McGilligan , p. 62 ^ McGilligan , p. 63 ^ McGilligan , p. 64 ^ ^ McGilligan , p. 80 ^ Levy 2025 , p. 41. ^ McGilligan , p. 86 ^ Eliot , p. 36 ^ a b McGilligan , p. 85 ^ a b McGilligan , p. 87 ^ Frayling , p. 45 ^ O'Brien , p. 40 ^ McGilligan , p. 93\n\n[NEW 2] est 2024 . ^ Middleton & Mitchell 2024 . ^ Steerpike 2024 . ^ ^ ^ ^ ^ ^ ^ ^ Diamond et al. 2023 , p. 2. ^ Elliott 2022 . ^ Gilchrist 2022 . ^ Islam 2022 . ^ Taylor 2022 . ^ Mason, Elgot & Allegretti 2021 . ^ McAuley 2022 . ^ Diver 2021 . ^ Chaplain 2022 . ^ Landler 2022 ; Adler 2022 ; Bray 2022 ; Mitter 2022 . ^ Courea 2022 . ^ Ni 2022 . ^ Holly 2022 . ^ Thykjaer & Landauro 2022 . ^ James & Macaskill 2022 . ^ Wintour 2020 . ^ Dathan 2021 . ^ Cole & Heale 2022 , p. 222. ^ Plummer 2022 . ^ Cole & Heale 2022 , pp. 120–124. ^ Mathers 2021 . ^ Pickard 2022 . Sources Books and journals News Websites and others Further reading External links Liz Truss at Wikipedia's sister projects Media from Commons News from Wikinews Quotations from Wikiquote Official website Pro\n\n[NEW 3] earch Networks & Centres Archived 27 September 2023 at the Wayback Machine . Official site. ^ a b ^ Equality, Diversity and Inclusion Committee (EDIC) . Official site. ^ Staff Networks Archived 27 September 2023 at the Wayback Machine . Official site. ^ Disabled Staff Network Archived 23 September 2023 at the Wayback Machine . ^ ^ Edinburgh Race Equality Network Archived 28 September 2023 at the Wayback Machine , ^ Jewish Staff Network Archived 16 July 2024 at the Wayback Machine . ^ University & College Union Archived 6 September 2023 at the Wayback Machine . ^ UCU Archived 28 September 2023 at the Wayback Machine . ^ ^ Support for Technicians Archived 22 August 2024 at the Wayback Machine . ^ ^ ^ ^ ^ ^ ^ Paterson, Kirsteen. (18 November 2024). 'Edinburgh U\n\n[NEW 4] eTwelve Studios Annapurna Interactive Vampire Survivors poncle poncle 2023 (27th) Baldur's Gate 3 Larian Studios Larian Studios Alan Wake 2 Remedy Entertainment Epic Games Cocoon Geometric Interactive Annapurna Interactive Marvel's Spider-Man 2 Insomniac Games Sony Interactive Entertainment The Legend of Zelda: Tears of the Kingdom Nintendo EPD Nintendo 2024 (28th) Astro Bot Team Asobi Sony Interactive Entertainment Balatro LocalThunk Playstack Black Myth: Wukong Game Science Game Science Helldivers 2 Arrowhead Game Studios Sony Interactive Entertainment Indiana Jones and the Great Circle MachineGames Bethesda Softworks 2025 (29th) Clair Obscur: Expedition 33 Sandfall Interactive Kepler Interactive ARC Raiders Embark Studios Embark Studios Blue Prince Dogubo\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] b c ^ ^ ^ a b External links Standard & Poor's page on S&P 500 index...\n[KEPT 2] American technology company Agilent Technologies, Inc. Headquarters lobby in Santa Clara, California Company type Public Traded as NYSE : A S&P 500 component Industry Healthcare equipment and services Predecessors Test & Measurement, Semiconductor Groups of HP Founded 1999 ; 27 years ago ( 1999 ) He...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 4 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[0,0,0,0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1362, + "osl": 685, + "total_tokens": 2047, + "latency_ms": 5760.45, + "tokens_per_second": 118.91 + }, + "context": { + "num_documents_evaluated": 4, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "401f6b5e-0b53-4c72-af64-17a90dc9ecff", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:22:01.435637Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of 2023, how many more employees does the company alphabetically first by ticker symbol in the S&P500 have than the company alphabetically 2nd to last by ticker symbol in the S&P500?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] b c ^ ^ ^ a b External links Standard & Poor's page on S&P 500 index\n\n[DOC 2] American technology company Agilent Technologies, Inc. Headquarters lobby in Santa Clara, California Company type Public Traded as NYSE : A S&P 500 component Industry Healthcare equipment and services Predecessors Test & Measurement, Semiconductor Groups of HP Founded 1999 ; 27 years ago ( 1999 ) Headquarters Santa Clara, California , U. S. Area served Worldwide Key people Padraig McDonnell ( CEO ) Koh Boon Hwee ( chairman ) Products Instruments, software, services and consumables for laboratory use Revenue US$ 6.95 billion (2025) Operating income US$1.48 billion (2025) Net income US$1.30 billion (2025) Total assets US$12.7 billion (2025) Total equity US$6.74 billion (2025) Number of employees 18,100 (2025) Divisions Agilent CrossLab Group Diagnostics & Geno\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents provide the employee count (for 2025, not 2023) for Agilent Technologies (ticker \\\"A\\\"), which is likely the alphabetically first S&P 500 ticker, but they do not contain a list of S&P 500 ticker symbols to identify the company that is alphabetically second‑to‑last, nor any employee count for that company. Therefore the necessary facts to compute the employee difference as of 2023 are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 532, + "osl": 436, + "total_tokens": 968, + "latency_ms": 3746.25, + "tokens_per_second": 116.38 + }, + "context": { + "kept_docs_count": 2, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "7046b06a-16be-440f-a0df-96a76cabe6e8", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:22:05.742249Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of 2023, how many more employees does the company alphabetically first by ticker symbol in the S&P500 have than the company alphabetically 2nd to last by ticker symbol in the S&P500?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] b c ^ ^ ^ a b External links Standard & Poor's page on S&P 500 index\n\n[DOC 2] American technology company Agilent Technologies, Inc. Headquarters lobby in Santa Clara, California Company type Public Traded as NYSE : A S&P 500 component Industry Healthcare equipment and services Predecessors Test & Measurement, Semiconductor Groups of HP Founded 1999 ; 27 years ago ( 1999 ) Headquarters Santa Clara, California , U. S. Area served Worldwide Key people Padraig McDonnell ( CEO ) Koh Boon Hwee ( chairman ) Products Instruments, software, services and consumables for laboratory use Revenue US$ 6.95 billion (2025) Operating income US$1.48 billion (2025) Net income US$1.30 billion (2025) Total assets US$12.7 billion (2025) Total equity US$6.74 billion (2025) Number of employees 18,100 (2025) Divisions Agilent CrossLab Group Diagnostics & Geno\n\n\nSEARCH HISTORY: - Query: 'S&P 500 ticker symbols' → 2 docs\n- Query: 'Agilent Technologies employees' → 1 docs\n- Query: 'Zoetis employees' → 5 docs\n- Query: 'S&P 500 constituents ticker symbols alphabetical' → 0 docs\n- Query: 'Agilent Technologies employees 2023' → 0 docs\n- Query: 'Zoetis employees 2023' → 4 docs\n\nFEEDBACK: First query will retrieve the full list of S&P 500 constituents with their ticker symbols, allowing identification of the alphabetically first ticker (likely \"A\" for Agilent Technologies) and the second‑to‑last ticker (likely \"ZTS\" for Zoetis). The second and third queries fetch the employee counts for those two companies as of 2023, enabling the calculation of the difference.\nFirst query will retrieve the ordered list of S&P 500 ticker symbols to confirm the alphabetically first (likely \"A\") and the second‑to‑last (likely \"ZTS\"). The second and third queries will obtain the 2023 employee counts for those two companies, enabling the calculation of the difference.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"S&P 500 constituents list\",\n \"Agilent Technologies Wikipedia\",\n \"Zoetis Wikipedia\"\n ],\n \"feedback\": \"First query will provide the full alphabetical list of S&P 500 ticker symbols to identify the first (ticker \\\"A\\\") and second‑to‑last (ticker \\\"ZTS\\\") companies. The second and third queries retrieve each company's Wikipedia page, which typically includes the most recent employee count (2023) in the infobox, enabling calculation of the difference.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 920, + "osl": 632, + "total_tokens": 1552, + "latency_ms": 4305.94, + "tokens_per_second": 146.77 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "0914815c-0ec6-4ab6-99d2-68489d90010d", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T03:22:08.958413Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of 2023, how many more employees does the company alphabetically first by ticker symbol in the S&P500 have than the company alphabetically 2nd to last by ticker symbol in the S&P500?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] 9-18 at the Wayback Machine . Bristolzoo. org. uk. Retrieved on 2011-12-07. ^ ^ ^ ^ Erick J. Lundgren, Daniel Ramp, John Rowan, Owen Middleton, Simon D. Schowanek, Oscar Sanisidro, Scott P. Carroll, Matt Davis, Christopher J. Sandom, Jens-Christian Svenning, Arian D. Wallach, James A. Estes, 2020, Introduced herbivores restore Late Pleistocene ecological functions , PNAS, 117 (14), pp.7871-7878, Proceedings of the National Academy of Sciences of the United States of America ^ ^ a b ^ ^ a b c Capybara. Hydrochaeris hydrochaeris . San Francisco Zoo ^ a b ^ a b c d e ^ ^ Burton M and Burton R. (2002) The International Wildlife Encyclopedia . Marshall Cavendish, ISBN 0-7614-7269-X , p. 384 ^ Capybara, the master of the grasses: pest or prey Sounds and Colours. R\n\n[NEW 2] 9-18 at the Wayback Machine . Bristolzoo. org. uk. Retrieved on 2011-12-07. ^ ^ ^ ^ Erick J. Lundgren, Daniel Ramp, John Rowan, Owen Middleton, Simon D. Schowanek, Oscar Sanisidro, Scott P. Carroll, Matt Davis, Christopher J. Sandom, Jens-Christian Svenning, Arian D. Wallach, James A. Estes, 2020, Introduced herbivores restore Late Pleistocene ecological functions , PNAS, 117 (14), pp.7871-7878, Proceedings of the National Academy of Sciences of the United States of America ^ ^ a b ^ ^ a b c Capybara. Hydrochaeris hydrochaeris . San Francisco Zoo ^ a b ^ a b c d e ^ ^ Burton M and Burton R. (2002) The International Wildlife Encyclopedia . Marshall Cavendish, ISBN 0-7614-7269-X , p. 384 ^ Capybara, the master of the grasses: pest or prey Sounds and Colours. R\n\n[NEW 3] Dennis & Piesman (2005) : p. 5 [ permanent dead link ] ^ a b c ^ a b Aeschlimann & Freyvogel, 1995 : p. 182 ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ Dennis & Piesman, 2005 : p. 3 [ permanent dead link ] ^ a b ^ ^ a b Material was copied from this source, which is available under a Creative Commons Attribution 4.0 International License . ^ ^ Duffy et al. (1992) ^ ^ ^ Further reading [ permanent dead link ] [ permanent dead link ] External links Media related to Ixodida at Wikimedia Commons Parasitic Insects, Mites and Ticks: Genera of Medical and Veterinary Importance at Wikibooks\n\n[NEW 4] ^ a b {{ cite book }} : CS1 maint: multiple names: authors list ( link ) ^ ^ ^ ^ a b {{ cite book }} : CS1 maint: multiple names: authors list ( link ) ^ ^ ^ ^ ^ ^ ^ ^ a b c d ^ ^ ^ ^ a b ^ ^ External links Wikispecies has information related to Equus quagga . Wikimedia Commons has media related to Equus quagga .\n\n[NEW 5] ssed August 2015. ^ Fleckvieh/Spain . Domestic Animal Diversity Information System of the Food and Agriculture Organization of the United Nations. Accessed August 2015. ^ Swiss Fleckvieh (seit 2009)/Switzerland . Domestic Animal Diversity Information System of the Food and Agriculture Organization of the United Nations. Accessed August 2015. ^ ^ Fleckvieh/Paraguay . Domestic Animal Diversity Information System of the Food and Agriculture Organization of the United Nations. Accessed August 2015. ^ Fleckvieh/Peru . Domestic Animal Diversity Information System of the Food and Agriculture Organization of the United Nations. Accessed August 2015. ^ Simmental-Fleckvieh/Spain . Domestic Animal Diversity Information System of the Food and Agriculture Organization of\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] b c ^ ^ ^ a b External links Standard & Poor's page on S&P 500 index...\n[KEPT 2] American technology company Agilent Technologies, Inc. Headquarters lobby in Santa Clara, California Company type Public Traded as NYSE : A S&P 500 component Industry Healthcare equipment and services Predecessors Test & Measurement, Semiconductor Groups of HP Founded 1999 ; 27 years ago ( 1999 ) He...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 5 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1280, + "osl": 359, + "total_tokens": 1639, + "latency_ms": 2685.93, + "tokens_per_second": 133.66 + }, + "context": { + "num_documents_evaluated": 5, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "14b6b6db-9ef2-4c60-911e-4b49b905c789", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:22:15.498969Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of 2023, how many more employees does the company alphabetically first by ticker symbol in the S&P500 have than the company alphabetically 2nd to last by ticker symbol in the S&P500?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] b c ^ ^ ^ a b External links Standard & Poor's page on S&P 500 index\n\n[DOC 2] American technology company Agilent Technologies, Inc. Headquarters lobby in Santa Clara, California Company type Public Traded as NYSE : A S&P 500 component Industry Healthcare equipment and services Predecessors Test & Measurement, Semiconductor Groups of HP Founded 1999 ; 27 years ago ( 1999 ) Headquarters Santa Clara, California , U. S. Area served Worldwide Key people Padraig McDonnell ( CEO ) Koh Boon Hwee ( chairman ) Products Instruments, software, services and consumables for laboratory use Revenue US$ 6.95 billion (2025) Operating income US$1.48 billion (2025) Net income US$1.30 billion (2025) Total assets US$12.7 billion (2025) Total equity US$6.74 billion (2025) Number of employees 18,100 (2025) Divisions Agilent CrossLab Group Diagnostics & Geno\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Missing: the 2023 employee count for the alphabetically first S&P 500 ticker (Agilent Technologies) – the document provides a 2025 figure only. Also missing: identification and 2023 employee count for the alphabetically second‑to‑last ticker symbol in the S&P 500, which is not present in any kept document.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 535, + "osl": 408, + "total_tokens": 943, + "latency_ms": 6539.69, + "tokens_per_second": 62.39 + }, + "context": { + "kept_docs_count": 2, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "e7f22bc0-d2dd-4de6-b945-838a1f28683a", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:22:17.771131Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of 2023, how many more employees does the company alphabetically first by ticker symbol in the S&P500 have than the company alphabetically 2nd to last by ticker symbol in the S&P500?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] b c ^ ^ ^ a b External links Standard & Poor's page on S&P 500 index\n\n[DOC 2] American technology company Agilent Technologies, Inc. Headquarters lobby in Santa Clara, California Company type Public Traded as NYSE : A S&P 500 component Industry Healthcare equipment and services Predecessors Test & Measurement, Semiconductor Groups of HP Founded 1999 ; 27 years ago ( 1999 ) Headquarters Santa Clara, California , U. S. Area served Worldwide Key people Padraig McDonnell ( CEO ) Koh Boon Hwee ( chairman ) Products Instruments, software, services and consumables for laboratory use Revenue US$ 6.95 billion (2025) Operating income US$1.48 billion (2025) Net income US$1.30 billion (2025) Total assets US$12.7 billion (2025) Total equity US$6.74 billion (2025) Number of employees 18,100 (2025) Divisions Agilent CrossLab Group Diagnostics & Geno\n\n\nSEARCH HISTORY: - Query: 'S&P 500 ticker symbols' → 2 docs\n- Query: 'Agilent Technologies employees' → 1 docs\n- Query: 'Zoetis employees' → 5 docs\n- Query: 'S&P 500 constituents ticker symbols alphabetical' → 0 docs\n- Query: 'Agilent Technologies employees 2023' → 0 docs\n- Query: 'Zoetis employees 2023' → 4 docs\n- Query: 'S&P 500 constituents list' → 0 docs\n- Query: 'Agilent Technologies Wikipedia' → 0 docs\n- Query: 'Zoetis Wikipedia' → 5 docs\n\nFEEDBACK: First query will retrieve the full list of S&P 500 constituents with their ticker symbols, allowing identification of the alphabetically first ticker (likely \"A\" for Agilent Technologies) and the second‑to‑last ticker (likely \"ZTS\" for Zoetis). The second and third queries fetch the employee counts for those two companies as of 2023, enabling the calculation of the difference.\nFirst query will retrieve the ordered list of S&P 500 ticker symbols to confirm the alphabetically first (likely \"A\") and the second‑to‑last (likely \"ZTS\"). The second and third queries will obtain the 2023 employee counts for those two companies, enabling the calculation of the difference.\nFirst query will provide the full alphabetical list of S&P 500 ticker symbols to identify the first (ticker \"A\") and second‑to‑last (ticker \"ZTS\") companies. The second and third queries retrieve each company's Wikipedia page, which typically includes the most recent employee count (2023) in the infobox, enabling calculation of the difference.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"S&P 500 constituents alphabetical\",\n \"Agilent Technologies Wikipedia\",\n \"Zoetis Wikipedia\"\n ],\n \"feedback\": \"Need the ordered list of S&P 500 ticker symbols to confirm the first and second‑to‑last companies, and the Wikipedia pages for Agilent Technologies and Zoetis, where 2023 employee counts are usually listed in the infobox.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1049, + "osl": 422, + "total_tokens": 1471, + "latency_ms": 2271.55, + "tokens_per_second": 185.78 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "638efee0-cfb4-4542-8ee3-20bef5cee97b", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:22:36.057743Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of 2023, how many more employees does the company alphabetically first by ticker symbol in the S&P500 have than the company alphabetically 2nd to last by ticker symbol in the S&P500?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] b c ^ ^ ^ a b External links Standard & Poor's page on S&P 500 index\n\n[DOC 2] American technology company Agilent Technologies, Inc. Headquarters lobby in Santa Clara, California Company type Public Traded as NYSE : A S&P 500 component Industry Healthcare equipment and services Predecessors Test & Measurement, Semiconductor Groups of HP Founded 1999 ; 27 years ago ( 1999 ) Headquarters Santa Clara, California , U. S. Area served Worldwide Key people Padraig McDonnell ( CEO ) Koh Boon Hwee ( chairman ) Products Instruments, software, services and consumables for laboratory use Revenue US$ 6.95 billion (2025) Operating income US$1.48 billion (2025) Net income US$1.30 billion (2025) Total assets US$12.7 billion (2025) Total equity US$6.74 billion (2025) Number of employees 18,100 (2025) Divisions Agilent CrossLab Group Diagnostics & Geno\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Missing: identification of the S&P 500 company with the alphabetically second‑to‑last ticker symbol and its 2023 employee count. Only the first ticker symbol (Agilent Technologies, ticker \\\"A\\\") with employee data (2025) is present, which is insufficient to compute the required difference.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 520, + "osl": 609, + "total_tokens": 1129, + "latency_ms": 17755.2, + "tokens_per_second": 34.3 + }, + "context": { + "kept_docs_count": 2, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "5ba71eae-1387-4158-bfd1-5d948514e510", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:22:44.253657Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: As of 2023, how many more employees does the company alphabetically first by ticker symbol in the S&P500 have than the company alphabetically 2nd to last by ticker symbol in the S&P500?\n\nDOCUMENTS:\n\n[DOC 1] b c ^ ^ ^ a b External links Standard & Poor's page on S&P 500 index\n\n[DOC 2] American technology company Agilent Technologies, Inc. Headquarters lobby in Santa Clara, California Company type Public Traded as NYSE : A S&P 500 component Industry Healthcare equipment and services Predecessors Test & Measurement, Semiconductor Groups of HP Founded 1999 ; 27 years ago ( 1999 ) Headquarters Santa Clara, California , U. S. Area served Worldwide Key people Padraig McDonnell ( CEO ) Koh Boon Hwee ( chairman ) Products Instruments, software, services and consumables for laboratory use Revenue US$ 6.95 billion (2025) Operating income US$1.48 billion (2025) Net income US$1.30 billion (2025) Total assets US$12.7 billion (2025) Total equity US$6.74 billion (2025) Number of employees 18,100 (2025) Divisions Agilent CrossLab Group Diagnostics & Geno\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 330, + "osl": 1, + "total_tokens": 331, + "latency_ms": 8194.94, + "tokens_per_second": 0.12 + }, + "context": { + "num_documents_used": 2, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:22:44.254491Z", + "summary": { + "total_llm_calls": 12, + "total_hops": 5, + "total_input_tokens": 9828, + "total_output_tokens": 6223, + "total_tokens": 16051, + "total_latency_ms": 100100.04, + "average_tokens_per_second": 93.97, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/List_of_S%26P_500_companies", + "https://en.wikipedia.org/wiki/Agilent_Technologies", + "https://en.wikipedia.org/wiki/Zebra_Technologies" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "8,350" + } + }, + { + "query_id": "44", + "original_query": "I am moving to the G40 postcode area - what train stations are nearby, as of 2024?", + "timestamp_start": "2026-05-16T03:22:44.289841Z", + "llm_calls": [ + { + "call_id": "132f3362-5ac3-4947-9bd9-bacd8b73a8b9", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:22:49.647305Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: I am moving to the G40 postcode area - what train stations are nearby, as of 2024?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"G40 postcode train stations\",\n \"train stations near Gloucester G40\",\n \"railway stations G40 area 2024\"\n ],\n \"feedback\": \"No previous attempts; these concise queries target the specific postcode (G40) and aim to retrieve current (2024) listings of nearby railway stations.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 468, + "osl": 314, + "total_tokens": 782, + "latency_ms": 5357.29, + "tokens_per_second": 58.61 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "3e840fea-c289-4652-b8ff-3eee79cf3a93", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:23:04.518165Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: I am moving to the G40 postcode area - what train stations are nearby, as of 2024?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] gh 64,860 64,884 Leicester 127 Dudley 64,270 64,275 West Midlands 128 Dewsbury 63,720 63,722 West Yorkshire 129 Mansfield 63,445 63,438 Nottinghamshire 130 Margate 63,320 63,322 Kent 131 Kettering 63,150 63,144 Northamptonshire 132 Cannock 63,065 63,054 Staffordshire 133 Sale 62,550 62,547 Greater Manchester 134 Taunton 61,665 61,674 Somerset 135 Runcorn 61,645 61,635 Cheshire 136 Farnborough 60,655 60,652 Hampshire 137 Tynemouth 60,605 60,600 Tyne and Wear 138 Hereford 60,480 60,475 Herefordshire 139 Halesowen 60,110 60,097 West Midlands 140 Widnes 59,935 59,939 Cheshire 141 Huyton with Roby 59,845 59,846 Merseyside 142 Scarborough 59,505 59,497 North Yorkshire 143 Gravesend 58,105 58,102 Kent 144 Bebington 57,600 57,597 Merseyside 145 Kidderminster 57,560\n\n[NEW 2] School ^ Office/depot at the A47/A4040 junction next to the River Tame at Ward End near M6, and next to the former LDV factory. ^ Genting Group, Star City near Washwood Heath. ^ Goodrich Engine Controls, Hall Green , Birmingham ^ in Kings Norton ^ Kitts Green (towards Solihull), next to the River Cole . ^ at Longbridge . ^ Maypoint Business Park, next to the railway on the opposite side of the A38 ^ Midpoint Park, south of A38 ^ a b Sutton Coldfield ^ Birmingham ^ National Highways, Quinton , Birmingham. ^ Ishida, Woodgate Business Park ^ Severn Trent, 2 St John Street, Coventry ^ Westwood Business Park in Westwood Heath ^ Bladon Jets, Pinley ^ Axeon UK, Coventry ^ Edgwick and Great Heath ^ Wayside Business Park, Longford, Coventry ^ Whitmore Park , Coventr\n\n[NEW 3] 3SS Sony / Cycleon returns XX40 3WW Temu XX40 1ZZ Sky XX40 4UU Biocentre (COVID-19 testing) XX40 4FL AstraZeneca (COVID-19 testing) XX40 8AZ Biocentre (COVID-19 testing) Scotland XX50 5FL Overseas territories Certain British Overseas Territories introduced single postal codes for their territory or major sub-sections of it. These are not UK postcodes, even though many are formatted in a similar fashion: Territory Postcode Anguilla AI-2640 Saint Helena, Ascension and Tristan da Cunha : Ascension Island Saint Helena Tristan da Cunha ASCN 1ZZ STHL 1ZZ TDCU 1ZZ British Indian Ocean Territory BBND 1ZZ British Antarctic Territory BIQQ 1ZZ Falkland Islands FIQQ 1ZZ Gibraltar GX11 1AA Pitcairn Islands PCRN 1ZZ South Georgia and the South Sandwich Islands SIQQ 1ZZ Tu\n\n[NEW 4] station, and planning permission was granted in July 2021. Work on the station commenced by late 2023. The station opened in August 2025, as the 32nd station on the line. Operation The DART service is operated by a mixed fleet of electric multiple unit trains. As of 2025, the trains run every ten minutes on weekdays with a reduced service on weekends. Trains north of Howth Junction are split between Howth and Malahide while the Malahide service is supplemented by Northern Commuter trains. Trains are typically run as four-car, six-car or eight-car sets during the 07.00–09.30 and 17.00–19.00 weekday peak periods. Capacity is reduced during off-peak periods and at weekends, with up to four eight-car sets running. Four-car sets typically consist of a single 8500\n\n[NEW 5] Railway line in Okayama and Hiroshima prefectures Geibi Line A Geibi Line KiHa 120 DMU Overview Native name 芸備線 Status In operation Owner JR West Locale Hiroshima and Okayama Prefectures Termini Bitchū-Kōjiro Hiroshima Stations 44 Service Type Commuter rail line System Hiroshima City Network (Karuga–Hiroshima) Operator(s) JR West Rolling stock KiHa 120 series DMU, KiHa 40 series DMU History Opened 28 April 1915 ; 110 years ago ( 1915-04-28 ) Technical Line length 159.1 km (98.9 mi) Number of tracks Entirely Single-tracked Character Rural and urban Track gauge 1,067 mm ( 3 ft 6 in ) Electrification None Operating speed 85 km/h (53 mph) Signalling Automatic closed block Train protection system ATS-SW Route map km - Niimi - Nunohara Two tunnels Hakubi Line 0.0\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 5 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1353, + "osl": 692, + "total_tokens": 2045, + "latency_ms": 14280.91, + "tokens_per_second": 48.46 + }, + "context": { + "num_documents_evaluated": 5, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "a73cfd17-7ecc-4f7f-947b-06575de6f7db", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:23:05.843520Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: I am moving to the G40 postcode area - what train stations are nearby, as of 2024?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: - Query: 'G40 postcode train stations' → 3 docs\n- Query: 'train stations near Gloucester G40' → 0 docs\n- Query: 'railway stations G40 area 2024' → 2 docs\n\nFEEDBACK: No previous attempts; these concise queries target the specific postcode (G40) and aim to retrieve current (2024) listings of nearby railway stations.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Gloucester railway stations\",\n \"G40 postcode map stations\",\n \"National Rail stations Gloucester\"\n ],\n \"feedback\": \"Previous queries focused on G40 + train stations but returned few or irrelevant results. The new queries broaden the scope to the city (Gloucester) and use map or National Rail resources, which should provide a comprehensive list of stations covering the G40 postcode area as of 2024.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 529, + "osl": 481, + "total_tokens": 1010, + "latency_ms": 1324.54, + "tokens_per_second": 363.14 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "0ef1cd63-6faf-421c-b39c-207ba31202ac", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T03:23:10.235469Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: I am moving to the G40 postcode area - what train stations are nearby, as of 2024?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] f a mile from Lydbrook and its viaduct on the Severn and Wye Railway . It was located approximately 4 miles and 34 chains along the railway from Ross-on-Wye station . In 1874 the Severn and Wye Railway opened a branch from Serridge Junction and Cinderford , passenger services commenced in 1875. All passenger trains along the S&W branch were withdrawn from 1929. See also Lower Lydbrook Viaduct References ^ ^ In interviews, with Fr Michael Foster, Vicar of the Parish from 1989–1997 ^ ^ ^ Stanley C Jenkins, The Ross, Monmouth and Pontypool Road Line , revised second edition 2009, ISBN 978-0-85361-692-4 External links Wikimedia Commons has media related to Lydbrook . Old photos of Lydbrook Lydbrook Band The Pubs of Lydbrook photos of Lydbrook and surrounding are\n\n[NEW 2] approximate coverage of the postcode districts: Postcode district Post town Coverage Local authority area(s) G1 GLASGOW Former C1 district: Merchant City Glasgow City G2 GLASGOW Former C2 district: Blythswood Hill , Anderston (part) Glasgow City G3 GLASGOW Former C3 district: Anderston , Finnieston , Garnethill , Kelvingrove , Park , Woodlands (part), Yorkhill Glasgow City G4 GLASGOW Former C4 district: Calton (part), Cowcaddens (part), Drygate, Kelvinbridge , Townhead , Woodlands (part), Woodside (part) Glasgow City G5 GLASGOW Former C5 district: Gorbals Glasgow City G9 GLASGOW Newspaper Competitions non-geographic G11 GLASGOW Former W1 district: Broomhill , Partick , Partickhill Glasgow City G12 GLASGOW Former W2 district: West End (part), Cleveden, Dowan\n\n[NEW 3] entre to Shankill , via Blackrock (Friday & Saturday only) See also List of railway stations in Ireland References ^ External links Irish Rail Seapoint Station Website Preceding station Iarnród Éireann Following station Blackrock DART Salthill & Monkstown\n\n[NEW 4] Market town in Gloucestershire, England Human settlement in England Stow-on-the-Wold Market Square Stow-on-the-Wold Location within Gloucestershire Population 2,042 (2011 Census) Civil parish Stow-on-the-Wold District Cotswold Shire county Gloucestershire Region South West Country England Sovereign state United Kingdom Post town CHELTENHAM Postcode district GL54 Dialling code 01451 Police Gloucestershire Fire Gloucestershire Ambulance South Western UK Parliament North Cotswolds List of places UK England Gloucestershire 51°55′48′′N 01°43′21′′W  /  51.93000°N 1.72250°W  / 51.93000; -1.72250 Stow-on-the-Wold is a market town and civil parish in Gloucestershire , England, on top of an 800-foot (244 m) hill at the junction of main roads through the Cotswolds ,\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 4 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 940, + "osl": 535, + "total_tokens": 1475, + "latency_ms": 3866.35, + "tokens_per_second": 138.37 + }, + "context": { + "num_documents_evaluated": 4, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "1500efb7-578a-40c9-8b9e-c71b3e2acec0", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:23:27.033968Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: I am moving to the G40 postcode area - what train stations are nearby, as of 2024?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: - Query: 'G40 postcode train stations' → 3 docs\n- Query: 'train stations near Gloucester G40' → 0 docs\n- Query: 'railway stations G40 area 2024' → 2 docs\n- Query: 'Gloucester railway stations' → 1 docs\n- Query: 'G40 postcode map stations' → 1 docs\n- Query: 'National Rail stations Gloucester' → 2 docs\n\nFEEDBACK: No previous attempts; these concise queries target the specific postcode (G40) and aim to retrieve current (2024) listings of nearby railway stations.\nPrevious queries focused on G40 + train stations but returned few or irrelevant results. The new queries broaden the scope to the city (Gloucester) and use map or National Rail resources, which should provide a comprehensive list of stations covering the G40 postcode area as of 2024.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Gloucestershire railway stations list\",\n \"National Rail station finder Gloucester\",\n \"Railway stations within G40\"\n ],\n \"feedback\": \"Previous queries were too narrow or duplicated. These broaden to a county-wide station list, use the official National Rail locator for Gloucester, and directly ask for stations inside the G40 postcode.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 569, + "osl": 80, + "total_tokens": 649, + "latency_ms": 16797.68, + "tokens_per_second": 4.76 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "9dee0f8e-b203-45a9-8836-7b3ca172ab2b", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T03:23:33.430048Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: I am moving to the G40 postcode area - what train stations are nearby, as of 2024?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] neaster passenger train at Ocean Park, Maine , as viewed from the cab of a northbound train The Downeaster passenger train, operated by Amtrak , provides passenger service between Brunswick and Boston's North Station , with stops in Freeport, Portland, Old Orchard Beach , Saco, and Wells . The Downeaster makes five daily trips. Freight Freight service throughout the state is provided by a handful of regional and shortline carriers: Pan Am Railways (formerly known as Guilford Rail System), which operates the former Boston and Maine and Maine Central railroads; St. Lawrence and Atlantic Railroad ; Maine Eastern Railroad ; Central Maine and Quebec Railway ; and New Brunswick Southern Railway . Shipping Cargo The International Marine Terminal in Portland provide\n\n[NEW 2] Railway station in Glasgow, Scotland Bridgeton Scottish Gaelic : Baile na Drochaid Bridgeton station in 2018, looking northwest towards Argyle Street General information Location Bridgeton , Glasgow Scotland Coordinates 55°50′54′′N 4°13′30′′W  /  55.8484°N 4.2250°W  / 55.8484; -4.2250 Grid reference NS607639 Managed by ScotRail Transit authority SPT Platforms 2 Other information Station code BDG Key dates 1 November 1895 Opened 5 October 1964 Closed 5 November 1979 Re-opened Passengers 2020/21 0.273 million 2021/22 0.476 million 2022/23 0.500 million 2023/24 0.768 million 2024/25 0.803 million Location Notes Passenger statistics from the Office of Rail and Road Bridgeton railway station serves the Bridgeton district of Glasgow , Scotland and is a station\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 2 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[0,1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 632, + "osl": 533, + "total_tokens": 1165, + "latency_ms": 5871.49, + "tokens_per_second": 90.78 + }, + "context": { + "num_documents_evaluated": 2, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "b2cde1e9-4308-42dc-bad7-475355f26111", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:23:37.082217Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: I am moving to the G40 postcode area - what train stations are nearby, as of 2024?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Railway station in Glasgow, Scotland Bridgeton Scottish Gaelic : Baile na Drochaid Bridgeton station in 2018, looking northwest towards Argyle Street General information Location Bridgeton , Glasgow Scotland Coordinates 55°50′54′′N 4°13′30′′W  /  55.8484°N 4.2250°W  / 55.8484; -4.2250 Grid reference NS607639 Managed by ScotRail Transit authority SPT Platforms 2 Other information Station code BDG Key dates 1 November 1895 Opened 5 October 1964 Closed 5 November 1979 Re-opened Passengers 2020/21 0.273 million 2021/22 0.476 million 2022/23 0.500 million 2023/24 0.768 million 2024/25 0.803 million Location Notes Passenger statistics from the Office of Rail and Road Bridgeton railway station serves the Bridgeton district of Glasgow , Scotland and is a station\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept document only provides details about Bridgeton railway station, which is located within the G40 postcode area. It does not list any other nearby train stations, nor does it give a comprehensive view of all stations serving the G40 area as of 2024. Additional station information is needed to answer the question fully.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 462, + "osl": 80, + "total_tokens": 542, + "latency_ms": 3651.44, + "tokens_per_second": 21.91 + }, + "context": { + "kept_docs_count": 1, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "86c9237c-5a80-4559-85aa-ac22cf90ec09", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:23:53.444388Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: I am moving to the G40 postcode area - what train stations are nearby, as of 2024?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Railway station in Glasgow, Scotland Bridgeton Scottish Gaelic : Baile na Drochaid Bridgeton station in 2018, looking northwest towards Argyle Street General information Location Bridgeton , Glasgow Scotland Coordinates 55°50′54′′N 4°13′30′′W  /  55.8484°N 4.2250°W  / 55.8484; -4.2250 Grid reference NS607639 Managed by ScotRail Transit authority SPT Platforms 2 Other information Station code BDG Key dates 1 November 1895 Opened 5 October 1964 Closed 5 November 1979 Re-opened Passengers 2020/21 0.273 million 2021/22 0.476 million 2022/23 0.500 million 2023/24 0.768 million 2024/25 0.803 million Location Notes Passenger statistics from the Office of Rail and Road Bridgeton railway station serves the Bridgeton district of Glasgow , Scotland and is a station\n\n\nSEARCH HISTORY: - Query: 'G40 postcode train stations' → 3 docs\n- Query: 'train stations near Gloucester G40' → 0 docs\n- Query: 'railway stations G40 area 2024' → 2 docs\n- Query: 'Gloucester railway stations' → 1 docs\n- Query: 'G40 postcode map stations' → 1 docs\n- Query: 'National Rail stations Gloucester' → 2 docs\n- Query: 'Gloucestershire railway stations list' → 0 docs\n- Query: 'National Rail station finder Gloucester' → 2 docs\n- Query: 'Railway stations within G40' → 0 docs\n\nFEEDBACK: No previous attempts; these concise queries target the specific postcode (G40) and aim to retrieve current (2024) listings of nearby railway stations.\nPrevious queries focused on G40 + train stations but returned few or irrelevant results. The new queries broaden the scope to the city (Gloucester) and use map or National Rail resources, which should provide a comprehensive list of stations covering the G40 postcode area as of 2024.\nPrevious queries were too narrow or duplicated. These broaden to a county-wide station list, use the official National Rail locator for Gloucester, and directly ask for stations inside the G40 postcode.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Gloucester railway station\",\n \"List of railway stations in Gloucestershire\",\n \"G40 postcode map\"\n ],\n \"feedback\": \"The first query fetches the main station serving the city. The second provides a comprehensive list of all stations in the county, which can be cross‑referenced with the G40 area. The third supplies a detailed postcode map to identify which of those stations fall within the G40 boundaries as of 2024.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 933, + "osl": 330, + "total_tokens": 1263, + "latency_ms": 16361.66, + "tokens_per_second": 20.17 + }, + "context": { + "kept_docs_count": 1, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "0de72ae3-c64a-4999-a2f6-b29cc2da751e", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T03:24:10.375668Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: I am moving to the G40 postcode area - what train stations are nearby, as of 2024?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] ery, were merged into Carlsberg Marston’s Brewing Company in 2020 , and Wychwood Brewery was shut down in November 2023, its brands continuing to be brewed elsewhere in the CMBC network. Railways The Witney Railway opened Witney's first station in 1861, linking the town to Yarnton where the line joined the Oxford, Worcester and Wolverhampton Railway . In 1873 the East Gloucestershire Railway opened from a new station, linking Witney with Lechlade and Fairford . The Great Western Railway operated services on both lines and eventually took them over. In 1962 British Railways closed the EGR completely and withdrew passenger services from the Witney Railway. In 1970 British Railways closed the Witney Railway completely and it was dismantled. Reopening proposal I\n\n[NEW 2] tance line in Great Britain. The main station for the city was, however, not located in the city centre but at Wednesfield Heath , now Heath Town on the east side of the city. This station was considered to be a First Class station, though its location was obviously not ideal and it became a goods station after passenger services ceased in 1873. The station buildings were demolished in 1965, but the main station area is now a nature reserve just off Powell Street, called Station Fields and part of the edge of the northbound platform is still in situ. The track running through the station site is, however, still in use. The first station in the city centre was opened by the Shrewsbury and Birmingham Railway in 1849. This station was only intended to be tempor\n\n\nKEPT DOCUMENTS (context only):\n[1 documents already kept as relevant]\n[KEPT 1] Railway station in Glasgow, Scotland Bridgeton Scottish Gaelic : Baile na Drochaid Bridgeton station in 2018, looking northwest towards Argyle Street General information Location Bridgeton , Glasgow Scotland Coordinates 55°50′54′′N 4°13′30′′W  /  55.8484°N 4.2250°W  / 55.8484; -4.2250 Grid refere...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 2 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 700, + "osl": 718, + "total_tokens": 1418, + "latency_ms": 16373.35, + "tokens_per_second": 43.85 + }, + "context": { + "num_documents_evaluated": 2, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 1 + } + }, + { + "call_id": "2a8c8fac-669b-4c6f-917c-48375ac6be2a", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:24:13.541214Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: I am moving to the G40 postcode area - what train stations are nearby, as of 2024?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Railway station in Glasgow, Scotland Bridgeton Scottish Gaelic : Baile na Drochaid Bridgeton station in 2018, looking northwest towards Argyle Street General information Location Bridgeton , Glasgow Scotland Coordinates 55°50′54′′N 4°13′30′′W  /  55.8484°N 4.2250°W  / 55.8484; -4.2250 Grid reference NS607639 Managed by ScotRail Transit authority SPT Platforms 2 Other information Station code BDG Key dates 1 November 1895 Opened 5 October 1964 Closed 5 November 1979 Re-opened Passengers 2020/21 0.273 million 2021/22 0.476 million 2022/23 0.500 million 2023/24 0.768 million 2024/25 0.803 million Location Notes Passenger statistics from the Office of Rail and Road Bridgeton railway station serves the Bridgeton district of Glasgow , Scotland and is a station\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Missing: comprehensive list of train stations near the G40 postcode area as of 2024. Only Bridgeton railway station is provided; other nearby stations (e.g., Glasgow Central, Glasgow Queen Street, High Street, Bellgrove, etc.) are not covered.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 515, + "osl": 642, + "total_tokens": 1157, + "latency_ms": 3164.72, + "tokens_per_second": 202.86 + }, + "context": { + "kept_docs_count": 1, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "fa34ac87-5cfd-42eb-8ded-049330a2a1ac", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:24:17.930978Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: I am moving to the G40 postcode area - what train stations are nearby, as of 2024?\n\nDOCUMENTS:\n\n[DOC 1] Railway station in Glasgow, Scotland Bridgeton Scottish Gaelic : Baile na Drochaid Bridgeton station in 2018, looking northwest towards Argyle Street General information Location Bridgeton , Glasgow Scotland Coordinates 55°50′54′′N 4°13′30′′W  /  55.8484°N 4.2250°W  / 55.8484; -4.2250 Grid reference NS607639 Managed by ScotRail Transit authority SPT Platforms 2 Other information Station code BDG Key dates 1 November 1895 Opened 5 October 1964 Closed 5 November 1979 Re-opened Passengers 2020/21 0.273 million 2021/22 0.476 million 2022/23 0.500 million 2023/24 0.768 million 2024/25 0.803 million Location Notes Passenger statistics from the Office of Rail and Road Bridgeton railway station serves the Bridgeton district of Glasgow , Scotland and is a station\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 357, + "osl": 138, + "total_tokens": 495, + "latency_ms": 4389.27, + "tokens_per_second": 31.44 + }, + "context": { + "num_documents_used": 1, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:24:17.931612Z", + "summary": { + "total_llm_calls": 11, + "total_hops": 5, + "total_input_tokens": 7458, + "total_output_tokens": 4543, + "total_tokens": 12001, + "total_latency_ms": 91438.7, + "average_tokens_per_second": 93.12, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/List_of_postcode_areas_in_the_United_Kingdom", + "https://en.wikipedia.org/wiki/G_postcode_area", + "https://en.wikipedia.org/wiki/Bridgeton,_Glasgow", + "https://en.wikipedia.org/wiki/Bridgeton_railway_station", + "https://en.wikipedia.org/wiki/Calton,_Glasgow", + "https://en.wikipedia.org/wiki/Dalmarnock" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "Bridgeton Railway Station and Dalmarnock Railway Station." + } + }, + { + "query_id": "45", + "original_query": "How old was Stephen Baldwin when Hailey and Justin got married?", + "timestamp_start": "2026-05-16T03:24:17.970881Z", + "llm_calls": [ + { + "call_id": "d0ab08dc-437d-43f6-a4af-e466a67e3a4c", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:24:28.008034Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: How old was Stephen Baldwin when Hailey and Justin got married?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Stephen Baldwin\",\n \"Hailey Baldwin wedding\",\n \"Stephen Baldwin age 2023\"\n ],\n \"feedback\": \"First query gets Stephen Baldwin's birthdate. Second gets the exact wedding date of Hailey and Justin. Third fetches Stephen Baldwin's age in 2023, allowing calculation of his age at the wedding.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 435, + "osl": 432, + "total_tokens": 867, + "latency_ms": 10037.01, + "tokens_per_second": 43.04 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "e137d683-b782-459b-b319-28ab6fab2fae", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:24:34.432502Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: How old was Stephen Baldwin when Hailey and Justin got married?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] ns References ^ ^ ^ a b c d e ^ a b ^ ^ ^ a b ^ a b ^ a b ^ ^ ^ ^ a b c ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ \"Brendan Fraser's eclectic career\" . Archived February 24, 2019, at the Wayback Machine . South Coast Today . ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c d e ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ a b ^ ^ External links Wikimedia Commons has media related to Brendan Fraser . Wikiquote has quotations related to Brendan Fraser . Brendan Fraser – official site Brendan Fraser at IMDb Brendan Fraser best movies – blog (archive)\n\n[NEW 2] Isaac Newton .\n\n[NEW 3] m n o p q r s t u ^ a b ^ ^ a b c ^ Wolfe 2014 , p. 29. ^ Wolfe 2014 , p. 50. ( Jim Dietz ) ^ {{ cite news }} : CS1 maint: deprecated archival service ( link ) ^ ^ a b c Kuenster 2006 , p. 406. ^ a b c d e f g h i j k l m n ^ [ permanent dead link ] ^ a b c ^ Wolfe 2014 , p. 142. ( Jack McKeon ) ^ Wolfe 2014 , p. 147. ( Bob Cluck ) ^ a b c ^ ^ a b c d e ^ Chandler, Swank 2012 , p. 327. ^ a b c d e f ^ Wolfe 2014 , p. 97. (Pete Brown) ^ a b Wolfe 2014 , p. 95. (Jim Buchan) ^ a b ^ ^ a b c d e f g h i ^ a b c d e f g h i j k l m ^ a b c Staples, Herschlag 2007 , p. 385. ^ a b c d e f g h ^ ^ a b c Kuenster 2006 , p. 409. ^ a b c d ^ a b c (subscription required) ^ a b ^ a b ^ a b ^ ^ ^ ^ a b ^ ^ Naiman, Porter 2010 , p. 179. ^ ^ ^ a b ^ a b ^ ^ ^ a b c d e f\n\n[NEW 4] mentary at The Guardian\n\n[NEW 5] p. 162. ^ McGettigan 2005 , p. 44. ^ Hegarty 2010 , pp. 12–14. ^ a b c d e Hegarty 2010 , p. 14. ^ a b Ó Fiaich 1989 , p. 3; Hegarty 2010 , p. 14. ^ Ó Fiaich 1989 , p. 3; Kerney Walsh 1996 , pp. 68–71. ^ a b Kerney Walsh 1996 , pp. 73–74. ^ a b c d e f g Hegarty 2010 , p. 15. ^ Casway 2003 , p. 66. ^ Casway 2003 , p. 69. ^ Kerney Walsh 1996 , pp. 7, 79; O'Byrne 2009 , 4th paragraph. ^ Carroll 2017 , pp. 23–50. ^ a b Casway 1981 , p. 50. ^ Ó Fearghail 2009 , p. 45. ^ a b c ^ Harris 1980 , p. 58. ^ Harris 1980 , p. 50. ^ Harris 1980 , p. 49. ^ a b Canny 1971 , p. 398. ^ Harris 1980 , p. 48. ^ Kerney Walsh 1996 , p. 71. ^ Harris 1980 , p. 54. ^ Gillespie 2010 , 5th–6th paragraphs. ^ a b Connolly 2013 , pp. 296–298. ^ Hegarty 2010 , p. 18. ^ Gillespie 2010 , 5t\n\n[NEW 6] American model (born 1996) Hailey Bieber Bieber in 2026 Born Hailey Rhode Baldwin ( 1996-11-22 ) November 22, 1996 (age 29) Tucson, Arizona , U. S. Occupations Model socialite media personality businesswoman Years active 2014–present Spouse Justin Bieber ( m. 2018) Children 1 Father Stephen Baldwin Relatives Eumir Deodato (maternal grandfather) Alec Baldwin (paternal uncle) Pattie Mallette (mother-in-law) Ireland Baldwin (cousin) Family Baldwin Modeling information Height 5 ft 8 in (1.73 m) Hair color Light brown Eye color Brown Agency IMG Models Hailey Rhode Bieber ( née Baldwin ; born November 22, 1996) is an American model, socialite, creative director, and businesswoman. She is the founder and chief creative officer (CCO) of skin care brand Rhode , which\n\n[NEW 7] daughters, Alaia Baldwin and Hailey Bieber ; both are models. His father-in-law is Brazilian composer Eumir Deodato , and Canadian singer Justin Bieber is his son-in-law through marriage to his daughter Hailey. Through his daughters, Baldwin has two grandchildren. In 2006, Baldwin launched a campaign to prevent an adult bookstore from opening in Nyack. He has a tattoo on his left shoulder of the initials \"HM\" for Hannah Montana . He got the tattoo after making a pact with Miley Cyrus that he would be allowed to cameo on the show if he had the initials tattooed on him. He revealed the tattoo to Cyrus at a book signing in Nashville on November 10, 2008. He was never given the opportunity to appear on the show and has since said that he regrets getting the tatt\n\n[NEW 8] a b ^ a b ^ ^ ^ ^ ^ Jacket copy, Choices , Gemstone Entertainment, 1992 ^ a b c ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b c d ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ a b c ^ ^ ^ ^ ^ ^ ^ ^ ^ a b ^ a b ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ Anderson, Susan Heller. \"Chronicle\" Archived October 30, 2022, at the Wayback Machine . The New York Times . July 11, 1991. Retrieved March 28, 2008. ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ See \"Demi Moore's wedding might coincide with trial\", Daily News , Oct. 16, 1986 ^ ^ ^ ^ ^ Moore 2019 , p. 166. ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^\n\n[NEW 9] d May 6, 2024. ^ a b McGilligan , p. 231 ^ ^ a b ^ Levy 2025 , p. 7. ^ Smith , p. 116 ^ ^ Schickel , p. 27 ^ a b Zmijewsky and Pfeiffer , p. 12 ^ Eliot , p. 15 ^ ^ a b McGilligan , p. 34 ^ McGilligan , p. 40 ^ a b ^ McGilligan , p. 191 ^ McGilligan , p. 38 ^ Kapsis and Coblentz , p. 123 (interviewer Tim Cahill) ^ McGilligan , p. 36 ^ ^ Eliot , p. 17 ^ ^ Eliot , pp. 18–19 ^ a b McGilligan , p. 49 ^ ^ Schickel , p. 53 ^ McGilligan , p. 50 ^ ^ a b c McGilligan , p. 52 ^ McGilligan , p. 53 ^ a b McGilligan , p. 60 ^ McGilligan , p. 62 ^ McGilligan , p. 63 ^ McGilligan , p. 64 ^ ^ McGilligan , p. 80 ^ Levy 2025 , p. 41. ^ McGilligan , p. 86 ^ Eliot , p. 36 ^ a b McGilligan , p. 85 ^ a b McGilligan , p. 87 ^ Frayling , p. 45 ^ O'Brien , p. 40 ^ McGilligan , p. 93\n\n[NEW 10] ney Utah Association of Counties Public Lands Attorney Deputy Washington County Attorney Soil conservationist Southern Utah University ( BS ) Brigham Young University ( JD ) November 28, 2023 ( special ) Cedar City Utah 3 Mike Kennedy Republican ( 1969-02-02 ) February 2, 1969 (age 57) Utah Senate Utah House Brigham Young University ( BS , JD ) Michigan State University ( MD ) January 3, 2025 Alpine Utah 4 Burgess Owens Republican ( 1951-08-02 ) August 2, 1951 (age 74) Businessman National Football League player University of Miami ( BS ) January 3, 2021 Herriman Vermont at-large Becca Balint Democratic ( 1968-05-04 ) May 4, 1968 (age 57) President pro tempore of the Vermont Senate Majority leader of the Vermont Senate Smith College ( BA ) Harvard University\n\n[NEW 11] 5 March 2025 Pretoria 19 years, 34 days 10 10.00 +1.6 Trentavis Friday United States 5 July 2014 Eugene 19 years, 30 days +1.7 Sorato Shimizu Japan 26 July 2025 Hiroshima 16 years, 168 days +0.9 Gout Gout Australia 21 February 2026 Brisbane 18 years, 54 days 13 10.01 0.0 Darrel Brown Trinidad and Tobago 24 August 2003 Saint-Denis 18 years, 317 days +1.6 Jeff Demps United States 28 June 2008 Eugene 18 years, 172 days +0.9 Yoshihide Kiryu Japan 28 April 2013 Hiroshima 17 years, 134 days +1.1 Brayden Williams United States 3 May 2025 Austin 18 years, 31 days 10.01 A +1.9 Renan Gallina Brazil 19 May 2023 Bogotá 19 years, 65 days 18 10.03 +0.7 Marcus Rowland United States 31 July 2009 Port of Spain 19 years, 142 days +1.7 Lalu Muhammad Zohri Indonesia 19 May 2019\n\n[NEW 12] Gross 2024 . ^ a b Pham 2009 . ^ Hawkes 2015 . ^ a b MBS TV 2016 . ^ Kono 2017 . ^ McCarthy 2019 . ^ Trafí-Prats 2016 . ^ DeWeese-Boyd 2009 . ^ Mayumi & Solomon 2005 . ^ Mumcu & Yılmaz 2018 . ^ Bye 2014 . ^ Noviana 2020 . ^ a b c d e f g h i Schellhase 2014 . ^ Loy & Goodhew 2004 , p. 68. ^ Reinders 2016 , p. 181. ^ Romano 2013 . ^ Miyazaki 2002 , p. 15. ^ McDougall 2018 . ^ Cappello 2005 . ^ Schilling 2008 . ^ Ghibli World 2008 . ^ Dwivedi 2017 . ^ Akimoto 2013 . ^ Loy & Goodhew 2004 . ^ Nonaka 1998 , 22:05. ^ Denham 2016 . ^ Bellot 2016 . ^ Toscano 2014 . ^ a b c d Mes 2002 . ^ Ebert 2002 . ^ a b c d Andrews 2005 . ^ Calvario 2016 . ^ Watzky 2023 . ^ Nakamura 1999 , pp. 38–41. ^ Schley 2019 . ^ Bose 2023 . ^ a b The Japan Times 2006 . ^ Animage 2006 . ^ Po\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 12 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0,0,0,0,0,1,1,0,0,0,0,0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 3050, + "osl": 603, + "total_tokens": 3653, + "latency_ms": 5773.03, + "tokens_per_second": 104.45 + }, + "context": { + "num_documents_evaluated": 12, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "d7dd81dd-c90d-4351-acbb-3dd83c97de30", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:24:37.922034Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: How old was Stephen Baldwin when Hailey and Justin got married?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American model (born 1996) Hailey Bieber Bieber in 2026 Born Hailey Rhode Baldwin ( 1996-11-22 ) November 22, 1996 (age 29) Tucson, Arizona , U. S. Occupations Model socialite media personality businesswoman Years active 2014–present Spouse Justin Bieber ( m. 2018) Children 1 Father Stephen Baldwin Relatives Eumir Deodato (maternal grandfather) Alec Baldwin (paternal uncle) Pattie Mallette (mother-in-law) Ireland Baldwin (cousin) Family Baldwin Modeling information Height 5 ft 8 in (1.73 m) Hair color Light brown Eye color Brown Agency IMG Models Hailey Rhode Bieber ( née Baldwin ; born November 22, 1996) is an American model, socialite, creative director, and businesswoman. She is the founder and chief creative officer (CCO) of skin care brand Rhode , which\n\n[DOC 2] daughters, Alaia Baldwin and Hailey Bieber ; both are models. His father-in-law is Brazilian composer Eumir Deodato , and Canadian singer Justin Bieber is his son-in-law through marriage to his daughter Hailey. Through his daughters, Baldwin has two grandchildren. In 2006, Baldwin launched a campaign to prevent an adult bookstore from opening in Nyack. He has a tattoo on his left shoulder of the initials \"HM\" for Hannah Montana . He got the tattoo after making a pact with Miley Cyrus that he would be allowed to cameo on the show if he had the initials tattooed on him. He revealed the tattoo to Cyrus at a book signing in Nashville on November 10, 2008. He was never given the opportunity to appear on the show and has since said that he regrets getting the tatt\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide that Stephen Baldwin is Hailey's father and that Hailey married Justin Bieber in 2018, but they do not include Stephen Baldwin's birth date or age. Without Stephen Baldwin's birth year, we cannot calculate his age at the time of the 2018 marriage.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 603, + "osl": 73, + "total_tokens": 676, + "latency_ms": 3488.8, + "tokens_per_second": 20.92 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "f122e64d-691c-4f49-9244-1b71a7c49117", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:24:43.331744Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: How old was Stephen Baldwin when Hailey and Justin got married?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American model (born 1996) Hailey Bieber Bieber in 2026 Born Hailey Rhode Baldwin ( 1996-11-22 ) November 22, 1996 (age 29) Tucson, Arizona , U. S. Occupations Model socialite media personality businesswoman Years active 2014–present Spouse Justin Bieber ( m. 2018) Children 1 Father Stephen Baldwin Relatives Eumir Deodato (maternal grandfather) Alec Baldwin (paternal uncle) Pattie Mallette (mother-in-law) Ireland Baldwin (cousin) Family Baldwin Modeling information Height 5 ft 8 in (1.73 m) Hair color Light brown Eye color Brown Agency IMG Models Hailey Rhode Bieber ( née Baldwin ; born November 22, 1996) is an American model, socialite, creative director, and businesswoman. She is the founder and chief creative officer (CCO) of skin care brand Rhode , which\n\n[DOC 2] daughters, Alaia Baldwin and Hailey Bieber ; both are models. His father-in-law is Brazilian composer Eumir Deodato , and Canadian singer Justin Bieber is his son-in-law through marriage to his daughter Hailey. Through his daughters, Baldwin has two grandchildren. In 2006, Baldwin launched a campaign to prevent an adult bookstore from opening in Nyack. He has a tattoo on his left shoulder of the initials \"HM\" for Hannah Montana . He got the tattoo after making a pact with Miley Cyrus that he would be allowed to cameo on the show if he had the initials tattooed on him. He revealed the tattoo to Cyrus at a book signing in Nashville on November 10, 2008. He was never given the opportunity to appear on the show and has since said that he regrets getting the tatt\n\n\nSEARCH HISTORY: - Query: 'Stephen Baldwin' → 5 docs\n- Query: 'Hailey Baldwin wedding' → 3 docs\n- Query: 'Stephen Baldwin age 2023' → 4 docs\n\nFEEDBACK: First query gets Stephen Baldwin's birthdate. Second gets the exact wedding date of Hailey and Justin. Third fetches Stephen Baldwin's age in 2023, allowing calculation of his age at the wedding.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Stephen Baldwin born\",\n \"Hailey Justin wedding date\",\n \"Stephen Baldwin age 2018\"\n ],\n \"feedback\": \"Missing: Stephen Baldwin's birthdate (to calculate his age at the wedding) and the exact date of Hailey Baldwin and Justin Bieber's marriage.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 905, + "osl": 315, + "total_tokens": 1220, + "latency_ms": 5409.08, + "tokens_per_second": 58.24 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "4c546a3c-bafe-41d9-8a0f-59a859a0b7ec", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T03:24:47.025525Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: How old was Stephen Baldwin when Hailey and Justin got married?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] s an American realist painter, and an active painter for six decades. His work is distinguished by a mixture of realism and luminism , favoring industrial and working scenes, but always imbued with atmospheric light. Geographically, his career spanned the length of the eastern Atlantic and beyond. Childhood and education Stephen Etnier was born on September 11, 1903, in York, Pennsylvania . His grandfather was E. Morgan Smith, the founder of Vioth Hydro . He was raised in a privileged environment in Wyndham Estate (now Wyndham Hills, Pennsylvania ). From 1915 to 1922 he attended the Haverford and Hill schools in Pennsylvania, and Roxbury Tutoring School in Connecticut. He transferring to Yale School of Art in December 1922, and matriculated into Yale Univers\n\n[NEW 2] British film director, writer and producer Stephen Weeks is a British film director, writer, and producer. He started making films when was 16 and made his featured debut aged 22 with I, Monster . Select filmography I, Monster (1971) Gawain and the Green Knight (1973) Ghost Story (1974) Scars (1976) Sword of the Valiant (1983) The Bengal Lancers! (1984) References ^ New Blood Pirie, David. Sight and Sound; London Vol. 40, Iss. 2, (Spring 1971): 73. ^ External links Stephen Weeks at IMDb This article about a British film director is a stub . You can help Wikipedia by adding missing information . v t e\n\n[NEW 3] couple's non-profit made a grant of US$10,000 to the ACLU of New Mexico for same sex efforts. In November 2013, a pop-up retail store for 'Tie The Knot' was located at the Los Angeles Beverly Center featuring the fourth collection from the couple, including professional athlete designs with Scott Fujita and Chris Kluwe , and Brittney Griner . Personal life Ferguson with Justin Mikita in 2014 Ferguson uses his full name, as there was already an actor called Jesse Ferguson in the actors' union when he joined. In September 2012, he announced his engagement to lawyer Justin Mikita, his boyfriend of nearly two years. They married in Manhattan on July 20, 2013, with the playwright and screenwriter Tony Kushner officiating at their wedding. The couple have two chil\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] American model (born 1996) Hailey Bieber Bieber in 2026 Born Hailey Rhode Baldwin ( 1996-11-22 ) November 22, 1996 (age 29) Tucson, Arizona , U. S. Occupations Model socialite media personality businesswoman Years active 2014–present Spouse Justin Bieber ( m. 2018) Children 1 Father Stephen Baldwin ...\n[KEPT 2] daughters, Alaia Baldwin and Hailey Bieber ; both are models. His father-in-law is Brazilian composer Eumir Deodato , and Canadian singer Justin Bieber is his son-in-law through marriage to his daughter Hailey. Through his daughters, Baldwin has two grandchildren. In 2006, Baldwin launched a campaig...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 3 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 920, + "osl": 393, + "total_tokens": 1313, + "latency_ms": 3155.95, + "tokens_per_second": 124.53 + }, + "context": { + "num_documents_evaluated": 3, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "4c77da4e-f70b-4c5b-8245-809d122b14f8", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:24:50.781008Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: How old was Stephen Baldwin when Hailey and Justin got married?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American model (born 1996) Hailey Bieber Bieber in 2026 Born Hailey Rhode Baldwin ( 1996-11-22 ) November 22, 1996 (age 29) Tucson, Arizona , U. S. Occupations Model socialite media personality businesswoman Years active 2014–present Spouse Justin Bieber ( m. 2018) Children 1 Father Stephen Baldwin Relatives Eumir Deodato (maternal grandfather) Alec Baldwin (paternal uncle) Pattie Mallette (mother-in-law) Ireland Baldwin (cousin) Family Baldwin Modeling information Height 5 ft 8 in (1.73 m) Hair color Light brown Eye color Brown Agency IMG Models Hailey Rhode Bieber ( née Baldwin ; born November 22, 1996) is an American model, socialite, creative director, and businesswoman. She is the founder and chief creative officer (CCO) of skin care brand Rhode , which\n\n[DOC 2] daughters, Alaia Baldwin and Hailey Bieber ; both are models. His father-in-law is Brazilian composer Eumir Deodato , and Canadian singer Justin Bieber is his son-in-law through marriage to his daughter Hailey. Through his daughters, Baldwin has two grandchildren. In 2006, Baldwin launched a campaign to prevent an adult bookstore from opening in Nyack. He has a tattoo on his left shoulder of the initials \"HM\" for Hannah Montana . He got the tattoo after making a pact with Miley Cyrus that he would be allowed to cameo on the show if he had the initials tattooed on him. He revealed the tattoo to Cyrus at a book signing in Nashville on November 10, 2008. He was never given the opportunity to appear on the show and has since said that he regrets getting the tatt\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide the marriage year of Hailey and Justin (2018) and confirm Stephen Baldwin is Hailey's father, but they do not include Stephen Baldwin's birth date or age, which is required to calculate how old he was in 2018.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 663, + "osl": 254, + "total_tokens": 917, + "latency_ms": 3754.62, + "tokens_per_second": 67.65 + }, + "context": { + "kept_docs_count": 2, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "03623681-ebff-4c3a-a99b-eb9503bc6809", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:24:58.950144Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: How old was Stephen Baldwin when Hailey and Justin got married?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American model (born 1996) Hailey Bieber Bieber in 2026 Born Hailey Rhode Baldwin ( 1996-11-22 ) November 22, 1996 (age 29) Tucson, Arizona , U. S. Occupations Model socialite media personality businesswoman Years active 2014–present Spouse Justin Bieber ( m. 2018) Children 1 Father Stephen Baldwin Relatives Eumir Deodato (maternal grandfather) Alec Baldwin (paternal uncle) Pattie Mallette (mother-in-law) Ireland Baldwin (cousin) Family Baldwin Modeling information Height 5 ft 8 in (1.73 m) Hair color Light brown Eye color Brown Agency IMG Models Hailey Rhode Bieber ( née Baldwin ; born November 22, 1996) is an American model, socialite, creative director, and businesswoman. She is the founder and chief creative officer (CCO) of skin care brand Rhode , which\n\n[DOC 2] daughters, Alaia Baldwin and Hailey Bieber ; both are models. His father-in-law is Brazilian composer Eumir Deodato , and Canadian singer Justin Bieber is his son-in-law through marriage to his daughter Hailey. Through his daughters, Baldwin has two grandchildren. In 2006, Baldwin launched a campaign to prevent an adult bookstore from opening in Nyack. He has a tattoo on his left shoulder of the initials \"HM\" for Hannah Montana . He got the tattoo after making a pact with Miley Cyrus that he would be allowed to cameo on the show if he had the initials tattooed on him. He revealed the tattoo to Cyrus at a book signing in Nashville on November 10, 2008. He was never given the opportunity to appear on the show and has since said that he regrets getting the tatt\n\n\nSEARCH HISTORY: - Query: 'Stephen Baldwin' → 5 docs\n- Query: 'Hailey Baldwin wedding' → 3 docs\n- Query: 'Stephen Baldwin age 2023' → 4 docs\n- Query: 'Stephen Baldwin born' → 2 docs\n- Query: 'Hailey Justin wedding date' → 1 docs\n- Query: 'Stephen Baldwin age 2018' → 0 docs\n\nFEEDBACK: First query gets Stephen Baldwin's birthdate. Second gets the exact wedding date of Hailey and Justin. Third fetches Stephen Baldwin's age in 2023, allowing calculation of his age at the wedding.\nMissing: Stephen Baldwin's birthdate (to calculate his age at the wedding) and the exact date of Hailey Baldwin and Justin Bieber's marriage.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Stephen Baldwin birthdate\",\n \"Hailey Baldwin wedding date\",\n \"Justin Bieber marriage date\"\n ],\n \"feedback\": \"Missing: Stephen Baldwin's exact birthdate and the precise date of Hailey Baldwin and Justin Bieber's marriage. These three concise queries aim to retrieve those specifics.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 979, + "osl": 476, + "total_tokens": 1455, + "latency_ms": 8168.57, + "tokens_per_second": 58.27 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "26b799b7-9bf9-402a-9ec2-493c268051dd", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T03:25:10.279222Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: How old was Stephen Baldwin when Hailey and Justin got married?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] n September 2009, he filed for divorce, citing irreconcilable differences. The divorce was finalized on December 28, 2010. Rossum dated Counting Crows frontman Adam Duritz from 2009 to 2010. In 2013, Rossum began dating writer/director Sam Esmail . They got engaged two years later, in August 2015. They married on May 29, 2017, at the Central Synagogue in New York City. The couple has two children: a daughter, born May 2021, and a son, born April 2023. Rossum has celiac disease and predominantly hyperactive ADHD and has been on medication for the latter since childhood. The designer Vera Wang was married to Rossum's cousin, investor and real-estate developer Arthur P. Becker from 1989 to 2012. Rossum has described herself as \" a spiritual person, but not espe\n\n[NEW 2] rey suffered a miscarriage . At 35 weeks into her next pregnancy, she gave birth to their fraternal twins on April 30, 2011, via caesarean section . In August 2014, Cannon confirmed he and Carey had separated. He filed for divorce on December 12, 2014, which was finalized in 2016. In 2015, Carey began dating Australian billionaire James Packer and, on January 21, 2016, she announced they were engaged. By October, however, they had called off the engagement. In October 2016, she began dating American choreographer Bryan Tanaka . On December 26, 2023, Tanaka confirmed that he and Carey had parted ways after seven years of dating. In April 2018, Carey opened up about taking therapy sessions and medication for her struggle with bipolar II disorder . She was diag\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] American model (born 1996) Hailey Bieber Bieber in 2026 Born Hailey Rhode Baldwin ( 1996-11-22 ) November 22, 1996 (age 29) Tucson, Arizona , U. S. Occupations Model socialite media personality businesswoman Years active 2014–present Spouse Justin Bieber ( m. 2018) Children 1 Father Stephen Baldwin ...\n[KEPT 2] daughters, Alaia Baldwin and Hailey Bieber ; both are models. His father-in-law is Brazilian composer Eumir Deodato , and Canadian singer Justin Bieber is his son-in-law through marriage to his daughter Hailey. Through his daughters, Baldwin has two grandchildren. In 2006, Baldwin launched a campaig...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 2 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 791, + "osl": 565, + "total_tokens": 1356, + "latency_ms": 10791.62, + "tokens_per_second": 52.36 + }, + "context": { + "num_documents_evaluated": 2, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "faa10e5b-6e45-48f1-96e0-92c525641bda", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:25:15.984040Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: How old was Stephen Baldwin when Hailey and Justin got married?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American model (born 1996) Hailey Bieber Bieber in 2026 Born Hailey Rhode Baldwin ( 1996-11-22 ) November 22, 1996 (age 29) Tucson, Arizona , U. S. Occupations Model socialite media personality businesswoman Years active 2014–present Spouse Justin Bieber ( m. 2018) Children 1 Father Stephen Baldwin Relatives Eumir Deodato (maternal grandfather) Alec Baldwin (paternal uncle) Pattie Mallette (mother-in-law) Ireland Baldwin (cousin) Family Baldwin Modeling information Height 5 ft 8 in (1.73 m) Hair color Light brown Eye color Brown Agency IMG Models Hailey Rhode Bieber ( née Baldwin ; born November 22, 1996) is an American model, socialite, creative director, and businesswoman. She is the founder and chief creative officer (CCO) of skin care brand Rhode , which\n\n[DOC 2] daughters, Alaia Baldwin and Hailey Bieber ; both are models. His father-in-law is Brazilian composer Eumir Deodato , and Canadian singer Justin Bieber is his son-in-law through marriage to his daughter Hailey. Through his daughters, Baldwin has two grandchildren. In 2006, Baldwin launched a campaign to prevent an adult bookstore from opening in Nyack. He has a tattoo on his left shoulder of the initials \"HM\" for Hannah Montana . He got the tattoo after making a pact with Miley Cyrus that he would be allowed to cameo on the show if he had the initials tattooed on him. He revealed the tattoo to Cyrus at a book signing in Nashville on November 10, 2008. He was never given the opportunity to appear on the show and has since said that he regrets getting the tatt\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide the marriage year of Hailey and Justin (2018) and identify Stephen Baldwin as Hailey's father, but they do not contain Stephen Baldwin's birth date or age. Without Stephen Baldwin's birth year, we cannot calculate his age at the time of the 2018 marriage.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 663, + "osl": 405, + "total_tokens": 1068, + "latency_ms": 5704.12, + "tokens_per_second": 71.0 + }, + "context": { + "kept_docs_count": 2, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "4dc4a7c6-32f6-4f4c-927b-52bbea1ec4d8", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:25:21.781300Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: How old was Stephen Baldwin when Hailey and Justin got married?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American model (born 1996) Hailey Bieber Bieber in 2026 Born Hailey Rhode Baldwin ( 1996-11-22 ) November 22, 1996 (age 29) Tucson, Arizona , U. S. Occupations Model socialite media personality businesswoman Years active 2014–present Spouse Justin Bieber ( m. 2018) Children 1 Father Stephen Baldwin Relatives Eumir Deodato (maternal grandfather) Alec Baldwin (paternal uncle) Pattie Mallette (mother-in-law) Ireland Baldwin (cousin) Family Baldwin Modeling information Height 5 ft 8 in (1.73 m) Hair color Light brown Eye color Brown Agency IMG Models Hailey Rhode Bieber ( née Baldwin ; born November 22, 1996) is an American model, socialite, creative director, and businesswoman. She is the founder and chief creative officer (CCO) of skin care brand Rhode , which\n\n[DOC 2] daughters, Alaia Baldwin and Hailey Bieber ; both are models. His father-in-law is Brazilian composer Eumir Deodato , and Canadian singer Justin Bieber is his son-in-law through marriage to his daughter Hailey. Through his daughters, Baldwin has two grandchildren. In 2006, Baldwin launched a campaign to prevent an adult bookstore from opening in Nyack. He has a tattoo on his left shoulder of the initials \"HM\" for Hannah Montana . He got the tattoo after making a pact with Miley Cyrus that he would be allowed to cameo on the show if he had the initials tattooed on him. He revealed the tattoo to Cyrus at a book signing in Nashville on November 10, 2008. He was never given the opportunity to appear on the show and has since said that he regrets getting the tatt\n\n\nSEARCH HISTORY: - Query: 'Stephen Baldwin' → 5 docs\n- Query: 'Hailey Baldwin wedding' → 3 docs\n- Query: 'Stephen Baldwin age 2023' → 4 docs\n- Query: 'Stephen Baldwin born' → 2 docs\n- Query: 'Hailey Justin wedding date' → 1 docs\n- Query: 'Stephen Baldwin age 2018' → 0 docs\n- Query: 'Stephen Baldwin birthdate' → 0 docs\n- Query: 'Hailey Baldwin wedding date' → 0 docs\n- Query: 'Justin Bieber marriage date' → 2 docs\n\nFEEDBACK: First query gets Stephen Baldwin's birthdate. Second gets the exact wedding date of Hailey and Justin. Third fetches Stephen Baldwin's age in 2023, allowing calculation of his age at the wedding.\nMissing: Stephen Baldwin's birthdate (to calculate his age at the wedding) and the exact date of Hailey Baldwin and Justin Bieber's marriage.\nMissing: Stephen Baldwin's exact birthdate and the precise date of Hailey Baldwin and Justin Bieber's marriage. These three concise queries aim to retrieve those specifics.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Stephen Baldwin\",\n \"Hailey Bieber wedding date\",\n \"Justin Bieber wedding date\"\n ],\n \"feedback\": \"Need Stephen Baldwin's birthdate (to compute his age at the wedding) and the exact wedding date of Hailey Bieber and Justin Bieber. Prior attempts at specific birthdate queries returned no results; searching the plain name should retrieve the full Wikipedia infobox with birthdate. Similarly, using the married name \\\"Bieber\\\" for Hailey and querying the wedding date directly should yield the precise marriage date.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1054, + "osl": 426, + "total_tokens": 1480, + "latency_ms": 5796.6, + "tokens_per_second": 73.49 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "6818b125-24d2-40bc-abb5-f0b8752d0d76", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:25:24.156899Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: How old was Stephen Baldwin when Hailey and Justin got married?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] American model (born 1996) Hailey Bieber Bieber in 2026 Born Hailey Rhode Baldwin ( 1996-11-22 ) November 22, 1996 (age 29) Tucson, Arizona , U. S. Occupations Model socialite media personality businesswoman Years active 2014–present Spouse Justin Bieber ( m. 2018) Children 1 Father Stephen Baldwin Relatives Eumir Deodato (maternal grandfather) Alec Baldwin (paternal uncle) Pattie Mallette (mother-in-law) Ireland Baldwin (cousin) Family Baldwin Modeling information Height 5 ft 8 in (1.73 m) Hair color Light brown Eye color Brown Agency IMG Models Hailey Rhode Bieber ( née Baldwin ; born November 22, 1996) is an American model, socialite, creative director, and businesswoman. She is the founder and chief creative officer (CCO) of skin care brand Rhode , which\n\n[DOC 2] daughters, Alaia Baldwin and Hailey Bieber ; both are models. His father-in-law is Brazilian composer Eumir Deodato , and Canadian singer Justin Bieber is his son-in-law through marriage to his daughter Hailey. Through his daughters, Baldwin has two grandchildren. In 2006, Baldwin launched a campaign to prevent an adult bookstore from opening in Nyack. He has a tattoo on his left shoulder of the initials \"HM\" for Hannah Montana . He got the tattoo after making a pact with Miley Cyrus that he would be allowed to cameo on the show if he had the initials tattooed on him. He revealed the tattoo to Cyrus at a book signing in Nashville on November 10, 2008. He was never given the opportunity to appear on the show and has since said that he regrets getting the tatt\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide Hailey Baldwin's birth year (1996) and the year she married Justin Bieber (2018), and identify Stephen Baldwin as her father. However, they do not contain Stephen Baldwin's birth date or age, which is required to calculate how old he was in 2018.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 664, + "osl": 421, + "total_tokens": 1085, + "latency_ms": 1746.4, + "tokens_per_second": 241.07 + }, + "context": { + "kept_docs_count": 2, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "72b9c2d2-83eb-48e3-96f3-fd7c3ea5f1c2", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:25:26.176533Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: How old was Stephen Baldwin when Hailey and Justin got married?\n\nDOCUMENTS:\n\n[DOC 1] American model (born 1996) Hailey Bieber Bieber in 2026 Born Hailey Rhode Baldwin ( 1996-11-22 ) November 22, 1996 (age 29) Tucson, Arizona , U. S. Occupations Model socialite media personality businesswoman Years active 2014–present Spouse Justin Bieber ( m. 2018) Children 1 Father Stephen Baldwin Relatives Eumir Deodato (maternal grandfather) Alec Baldwin (paternal uncle) Pattie Mallette (mother-in-law) Ireland Baldwin (cousin) Family Baldwin Modeling information Height 5 ft 8 in (1.73 m) Hair color Light brown Eye color Brown Agency IMG Models Hailey Rhode Bieber ( née Baldwin ; born November 22, 1996) is an American model, socialite, creative director, and businesswoman. She is the founder and chief creative officer (CCO) of skin care brand Rhode , which\n\n[DOC 2] daughters, Alaia Baldwin and Hailey Bieber ; both are models. His father-in-law is Brazilian composer Eumir Deodato , and Canadian singer Justin Bieber is his son-in-law through marriage to his daughter Hailey. Through his daughters, Baldwin has two grandchildren. In 2006, Baldwin launched a campaign to prevent an adult bookstore from opening in Nyack. He has a tattoo on his left shoulder of the initials \"HM\" for Hannah Montana . He got the tattoo after making a pact with Miley Cyrus that he would be allowed to cameo on the show if he had the initials tattooed on him. He revealed the tattoo to Cyrus at a book signing in Nashville on November 10, 2008. He was never given the opportunity to appear on the show and has since said that he regrets getting the tatt\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 517, + "osl": 125, + "total_tokens": 642, + "latency_ms": 2018.8, + "tokens_per_second": 61.92 + }, + "context": { + "num_documents_used": 2, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:25:26.177271Z", + "summary": { + "total_llm_calls": 12, + "total_hops": 5, + "total_input_tokens": 11244, + "total_output_tokens": 4488, + "total_tokens": 15732, + "total_latency_ms": 65844.6, + "average_tokens_per_second": 81.41, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Hailey_Bieber", + "https://en.wikipedia.org/wiki/Stephen_Baldwin" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "52" + } + }, + { + "query_id": "46", + "original_query": "As of August 1, 2024, what is the largest city of the 9th largest country by land area in Europe?", + "timestamp_start": "2026-05-16T03:25:26.412815Z", + "llm_calls": [ + { + "call_id": "f6e97f05-4fa0-4626-80d5-e81e75c50db8", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:25:41.772257Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 1, 2024, what is the largest city of the 9th largest country by land area in Europe?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"European countries by area\",\n \"9th largest European country\",\n \"largest city of Italy\"\n ],\n \"feedback\": \"First query retrieves a ranking list of European nations by land area. The second query isolates the country that ranks ninth on that list. The third query then obtains the name of the largest city in that specific country.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 450, + "osl": 642, + "total_tokens": 1092, + "latency_ms": 15359.26, + "tokens_per_second": 41.8 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "6d547062-fe34-4591-986e-f96d75008dc4", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:25:47.790324Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of August 1, 2024, what is the largest city of the 9th largest country by land area in Europe?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] of countries and dependencies by area List of European countries by area List of metropolitan areas in Asia Notes ^ Figure is for the 3 districts in Northern Asia . Total area is 17,098,246 (6,601,670) including European Russia . Largest country in the world by area. ^ Excluding Taiwan , Hong Kong and Macau . ^ Nine districts totaling 122,176 (47,172) are entirely in Europe and four districts totaling 52,638 (20,324) are partially in Europe. The Asia figure given includes entire districts with significant portions in Asia, with significant figures reduced to reflect the uncertainty. Total area is 2,724,900 (1,052,100) including European part, west of the Ural River . Largest landlocked country in the world. ^ Figure excludes two provinces in Western New Gui\n\n[NEW 2] Below is a list of European countries and dependencies by area in Europe . As a continent , Europe's total geographical area is about 10 million square kilometres. Transcontinental countries are ranked according to the size of their European part only. Inland water is included in area numbers. Relative sizes European countries vary in area over many orders of magnitude , ranging from Russia which covers almost 4 000 000 km 2 of territory within Europe according to \"Definition\" below , to Vatican City , which has a total area of less than 1 km 2 : List of European countries and dependencies by area Figures are from the United Nations unless otherwise specified. Country or dependency % total Europe area km 2 mi 2 – Europe 100% 10,014,000 3,866,000 1 K Russia 3\n\n[NEW 3] 9.52 9.18 9.37 9.58 9.58 9.58 9.50 9.58 9.58 9.65 9.65 9.65 9.65 9.65 9.71 8 Ireland Full democracy 9.19 9.19 9.13 9.00 9.05 9.24 9.15 9.15 9.15 8.85 8.72 8.68 8.56 8.56 8.79 9.01 9.01 37 Italy Flawed democracy 7.58 7.69 7.69 7.68 7.74 7.52 7.71 7.98 7.98 7.98 7.85 7.85 7.74 7.74 7.83 7.98 7.73 10 Luxembourg Full democracy 8.88 8.81 8.81 8.68 8.68 8.81 8.81 8.81 8.81 8.88 8.88 8.88 8.88 8.88 8.88 9.10 9.10 27 Malta Flawed democracy 7.93 7.93 7.70 7.57 7.68 7.95 8.21 8.15 8.39 8.39 8.39 8.28 8.28 8.28 8.28 8.39 8.39 9 Netherlands Full democracy 9.00 9.00 9.00 8.88 8.96 9.01 8.89 8.89 8.80 8.92 8.92 8.84 8.99 8.99 8.99 9.53 9.66 1 Norway Full democracy 9.81 9.81 9.81 9.75 9.81 9.87 9.87 9.87 9.93 9.93 9.93 9.93 9.93 9.80 9.80 9.68 9.55 23 Portugal Full democr\n\n[NEW 4] 10 1 10 3 10 6 10 4 7 1 3 2 Latvia 64 36 28 3 8 5 4 4 8 1 1 2 Greece 126 41 85 7 2 4 2 12 4 7 3 United Kingdom 46 46 0 4 2 3 4 6 8 3 4 4 8 Norway 16 12 4 6 1 1 2 2 Italy 268 164 104 2 5 6 10 8 6 10 6 7 1 5 2 8 3 7 6 10 8 3 6 10 5 7 7 3 6 7 Serbia 54 22 32 3 1 2 5 4 1 1 5 Finland 38 7 31 4 3 Portugal 152 139 13 3 12 3 5 1 12 5 3 3 3 10 8 4 7 4 6 8 12 2 4 1 5 6 8 4 Armenia 183 101 82 2 8 8 6 3 7 7 7 6 4 4 3 7 6 3 8 5 7 Cyprus 78 34 44 1 7 2 3 1 6 2 10 2 Switzerland 591 365 226 12 10 12 12 12 12 12 10 5 10 12 12 12 5 7 10 12 7 12 12 12 12 5 12 12 12 10 6 12 12 12 6 12 10 12 Slovenia 27 15 12 3 10 2 Croatia 547 210 337 4 8 6 4 10 3 2 4 8 8 6 6 6 1 8 8 8 7 8 2 8 10 10 6 7 8 12 10 12 10 Georgia 34 15 19 7 2 2 1 3 France 445 218 227 6 10 6 4 4 1 10 7 10 10 12 12 1\n\n[NEW 5] Britain 1 Finland – – – – – – 9th – – – – – – – – – – – – – – – – – – 1 France 6th 4th 5th – 4th 8th 11th – 10th – 10th 12th – – – – – – – – – – – – 11th 10 Germany 5th – 3rd – 2nd – 5th – 1st 4th 5th 3rd 1st 1st 3rd 4th 2nd 13 Great Britain – 1st – – – 2nd 3rd 4th 4th 9th 12th 6th – – 3rd 1st 6th 7th 6th 9th 5th 4th 9th 5th 7th 19 Hong Kong – – – – – – – – – 15th – – – – – – – – – – – – – – – 1 Hungary – – – – 8th – – – – – – – – – – – – – – – – – – – – 1 India – – 1st 1st 1st 1st 1st 1st 2nd 1st 3rd 3rd 7th 1st 5th 6th 7th 8th 7th 7th – 12th 8th 3rd 3rd 22 Ireland 2nd – – – – – – – – – – – – – – – – – – – – – 10th – 10th 3 Italy – – – – – – 11th – 13th – – – – – – – – – – – – – – – – 2 Japan – – – 2nd 7th – – – 14th 7th 13th – – – – – – – – – – – – 11th –\n\n[NEW 6] rights groups such as Amnesty International and Human Rights Watch described the law as discriminatory towards Muslims. However, it is supported by most of the population. Economy La Défense was in 2017 ranked by Ernst & Young as the leading central business district in continental Europe, and fourth in the world. France has a social market economy characterised by sizeable government involvement and diversified sectors . For two centuries, it has consistently ranked among the ten largest globally; as of 2025 [update] it is the world's ninth largest by purchasing power parity and second largest in the EU, after Germany. Considered a great power with considerable economic strength , it is a member of the Group of Seven leading industrialised countries, the O\n\n[NEW 7] Ancient city near modern Naples, Italy Pompeii View of Pompeii and Mount Vesuvius Pompeii Shown within Italy Location Pompei , Metropolitan City of Naples , Campania , Italy Coordinates 40°45′0′′N 14°29′10′′E  /  40.75000°N 14.48611°E  / 40.75000; 14.48611 Type Settlement Area 64 to 67 ha (170 acres) History Founded 7th–6th century BC Abandoned AD 79 Site notes Website www . pompeiisites . org UNESCO World Heritage Site Official name Archaeological Areas of Pompeii, Herculaneum , and Torre Annunziata Type Cultural Criteria iii, iv, v Designated 1997 (21st session ) Reference no. 829 Region Europe Pompeii ( / p ɒ m ˈ p eɪ ( i )/ i ; Latin: [pɔmˈpei̯. iː] ) was a city in what is now the municipality of Pompei , near Naples , in the Campania region of Italy.\n\n[NEW 8] Enclaved Holy See's independent city-state Vatican City State Stato della Città del Vaticano ( Italian ) Status Civitatis Vaticanae ( Latin ) Flag Coat of arms Anthem: Inno Pontificio ( Italian ) \"Pontifical Hymn\" noicon National Seal Sigillo dello Stato della Città del Vaticano ( Italian ) Sigillum Status Civitatis Vaticanae ( Latin ) National Seal of Vatican City Location of the Vatican City in Europe Capital Vatican City ( city-state ) 41°54.2′N 12°27.2′E  /  41.9033°N 12.4533°E  / 41.9033; 12.4533 Official languages Italian National language Italian ( de facto ) Latin ( de jure at Holy See ) Religion Catholicism ( state religion ) Demonyms None ( de jure ) Vatican ( de facto ) Government Theocratic elective absolute monarchy • Sovereign entity Holy Se\n\n[NEW 9] ly 1982) New South Wales , Australia (since May 1984) Seoul , South Korea (since September 1988) Jakarta , Indonesia (since October 1989) São Paulo State , Brazil (since June 1990) Cairo , Egypt (since October 1990) Moscow , Russia (since July 1991) Berlin , Germany (since May 1994) Rome , Italy (since July 1996) London , England (since October 2015) Friendship and cooperation agreements Tomsk Oblast , Russia (since May 2015) Brussels , Belgium (since October 2016) Los Angeles County , United States (since August 2021) See also Tokyo portal Japan portal Cities portal List of cities proper by population List of cities with the most skyscrapers List of tallest structures in Tokyo List of development projects in Tokyo List of largest cities List of metropolitan\n\n[NEW 10] and is the second-highest in Canada after Vancouver. Sister cities Partnership cities Chicago , Illinois , United States (1991) Chongqing , China (1986) Frankfurt , Germany (1989) Milan , Italy (2003) Ho Chi Minh City , Vietnam (2006) Kyiv , Ukraine (1992) Quito , Ecuador (2006) Rio de Janeiro , Brazil (2015) Sagamihara , Japan (1991) Warsaw , Poland (1990) International project agreement Lisbon , Portugal Matera , Italy Notable people See also Ontario portal Cities portal Canada portal Great Lakes megalopolis – Cities around the Great Lakes of North America Largest cities in the Americas List of metropolitan areas in the Americas Outline of Toronto – Overview of and topical guide to Toronto Notes ^ The motto is typically rendered without punctuation, while\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 10 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 3056, + "osl": 800, + "total_tokens": 3856, + "latency_ms": 5311.67, + "tokens_per_second": 150.61 + }, + "context": { + "num_documents_evaluated": 10, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "2b0d4938-0092-4cfb-ba93-6895b2b12336", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:25:52.159089Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 1, 2024, what is the largest city of the 9th largest country by land area in Europe?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] of countries and dependencies by area List of European countries by area List of metropolitan areas in Asia Notes ^ Figure is for the 3 districts in Northern Asia . Total area is 17,098,246 (6,601,670) including European Russia . Largest country in the world by area. ^ Excluding Taiwan , Hong Kong and Macau . ^ Nine districts totaling 122,176 (47,172) are entirely in Europe and four districts totaling 52,638 (20,324) are partially in Europe. The Asia figure given includes entire districts with significant portions in Asia, with significant figures reduced to reflect the uncertainty. Total area is 2,724,900 (1,052,100) including European part, west of the Ural River . Largest landlocked country in the world. ^ Figure excludes two provinces in Western New Gui\n\n[DOC 2] Below is a list of European countries and dependencies by area in Europe . As a continent , Europe's total geographical area is about 10 million square kilometres. Transcontinental countries are ranked according to the size of their European part only. Inland water is included in area numbers. Relative sizes European countries vary in area over many orders of magnitude , ranging from Russia which covers almost 4 000 000 km 2 of territory within Europe according to \"Definition\" below , to Vatican City , which has a total area of less than 1 km 2 : List of European countries and dependencies by area Figures are from the United Nations unless otherwise specified. Country or dependency % total Europe area km 2 mi 2 – Europe 100% 10,014,000 3,866,000 1 K Russia 3\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The provided documents list European countries by land area, but they do not contain a complete ranking to identify the 9th largest country, nor do they provide information about the largest city of any country. Therefore, the necessary facts to answer the question are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 648, + "osl": 316, + "total_tokens": 964, + "latency_ms": 4367.95, + "tokens_per_second": 72.35 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "f2d26458-73ed-4ee0-893d-76440cbb9821", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:25:58.296874Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 1, 2024, what is the largest city of the 9th largest country by land area in Europe?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] of countries and dependencies by area List of European countries by area List of metropolitan areas in Asia Notes ^ Figure is for the 3 districts in Northern Asia . Total area is 17,098,246 (6,601,670) including European Russia . Largest country in the world by area. ^ Excluding Taiwan , Hong Kong and Macau . ^ Nine districts totaling 122,176 (47,172) are entirely in Europe and four districts totaling 52,638 (20,324) are partially in Europe. The Asia figure given includes entire districts with significant portions in Asia, with significant figures reduced to reflect the uncertainty. Total area is 2,724,900 (1,052,100) including European part, west of the Ural River . Largest landlocked country in the world. ^ Figure excludes two provinces in Western New Gui\n\n[DOC 2] Below is a list of European countries and dependencies by area in Europe . As a continent , Europe's total geographical area is about 10 million square kilometres. Transcontinental countries are ranked according to the size of their European part only. Inland water is included in area numbers. Relative sizes European countries vary in area over many orders of magnitude , ranging from Russia which covers almost 4 000 000 km 2 of territory within Europe according to \"Definition\" below , to Vatican City , which has a total area of less than 1 km 2 : List of European countries and dependencies by area Figures are from the United Nations unless otherwise specified. Country or dependency % total Europe area km 2 mi 2 – Europe 100% 10,014,000 3,866,000 1 K Russia 3\n\n\nSEARCH HISTORY: - Query: 'European countries by area' → 2 docs\n- Query: '9th largest European country' → 4 docs\n- Query: 'largest city of Italy' → 4 docs\n\nFEEDBACK: First query retrieves a ranking list of European nations by land area. The second query isolates the country that ranks ninth on that list. The third query then obtains the name of the largest city in that specific country.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"European countries by area list\",\n \"ninth largest European country by area\",\n \"largest city of the ninth largest European country\"\n ],\n \"feedback\": \"Need to determine which country ranks 9th in Europe by land area, then find its largest city as of Aug 1 2024.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 880, + "osl": 672, + "total_tokens": 1552, + "latency_ms": 6137.13, + "tokens_per_second": 109.5 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "02e36791-aaa5-46cd-82a3-2ec672269020", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T03:26:05.507635Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of August 1, 2024, what is the largest city of the 9th largest country by land area in Europe?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] 1,806,279 Europe Southern Europe Albania , Montenegro , North Macedonia , and Serbia 4 2 Kyrgyzstan 199,951 6,071,750 Asia Central Asia China , Kazakhstan , Tajikistan , and Uzbekistan 4 1 Laos 236,800 7,749,595 Asia South-eastern Asia Cambodia , China , Myanmar , Thailand , and Vietnam 5 5 Lesotho 30,355 2,281,454 Africa Southern Africa South Africa 1 1 Liechtenstein 160 35,789 Europe Western Europe Austria and Switzerland 2 0 Luxembourg 2,586 502,202 Europe Belgium , France , and Germany 3 3 Malawi 118,484 20,091,635 Africa Eastern Africa Mozambique , Tanzania , and Zambia 3 2 Mali 1,240,192 21,473,764 Africa Western Africa Algeria , Burkina Faso , Côte d'Ivoire , Guinea , Mauritania , Niger , and Senegal 7 5 Moldova 33,846 3,559,500 Europe Eastern Europe\n\n[NEW 2] Europe Belarus 9,173,237 9,115,680 −0.63% Europe Eastern Europe Switzerland 8,792,182 8,870,561 +0.89% Europe Western Europe Sierra Leone 8,276,807 8,460,512 +2.22% Africa Western Africa Laos 7,559,007 7,664,993 +1.40% Asia South-eastern Asia Hong Kong ( China ) 7,465,915 7,442,734 −0.31% Asia Eastern Asia Turkmenistan 7,230,193 7,364,438 +1.86% Asia Central Asia Libya 7,223,805 7,305,659 +1.13% Africa Northern Africa Kyrgyzstan 6,955,788 7,073,516 +1.69% Asia Central Asia Paraguay 6,760,464 6,844,146 +1.24% Americas South America Nicaragua 6,730,654 6,823,613 +1.38% Americas Central America Bulgaria 6,825,864 6,795,803 −0.44% Europe Eastern Europe Serbia 6,791,213 6,773,201 −0.27% Europe Southern Europe El Salvador 6,280,319 6,309,624 +0.47% Americas Centr\n\n[NEW 3] as been a parliamentary republic composed of 28 provinces , with a high degree of political, administrative, and economic centralisation . Its high-income economy is part of the European Single Market and is largely based on services, followed by manufacturing , mining , and agriculture . Bulgaria has been influenced by its role as a transit country for natural gas and oil pipelines , as well as its strategic location on the Black Sea . Its foreign relations have been shaped by its geographical location and its modern membership; Bulgaria is a member state of the European Union , in which it is a member of the Schengen Area and the eurozone , and also of NATO , as well as various regional suborganizations such as the Bucharest Nine . Etymology The name Bulga\n\n[NEW 4] the European Union (EU), with over 38 million people, and the fifth largest EU country by land area, covering 312,696 km 2 (120,733 sq mi). The capital and largest city is Warsaw ; other major cities include Kraków , Wrocław , Łódź , Poznań , and Gdańsk . Prehistoric human activity on Polish soil dates to the Lower Paleolithic , with continuous settlement since the end of the Last Glacial Period . Culturally diverse throughout late antiquity , in the early medieval period the region became inhabited by the West Slavic tribal Polans , who gave Poland its name . The process of establishing statehood coincided with the conversion of a pagan ruler of the Polans to Christianity in 966 under the auspices of the Roman Catholic Church . Since then, Poland has belon\n\n[NEW 5] 13 Vaduz Liechtenstein 47.47 Budapest Hungary 47.92 Ulaanbaatar Mongolia 48.13 Bratislava Slovakia 48.2 Vienna Austria 48.35 Strasbourg European Union Seat of the European Parliament , Eurocorps , European Ombudsman of the European Union ; so also the Council of Europe , European Court of Human Rights , European Directorate for the Quality of Medicines & HealthCare and the European Audiovisual Observatory 48.85 Paris France 49.18 St. Helier Jersey British Crown Dependency 49.45 St. Peter Port Guernsey British Crown Dependency 49.6 Luxembourg Luxembourg 50.08 Prague Czech Republic 50.45 Kyiv Ukraine 50.85 Brussels Belgium Also the de facto capital of the European Union 51.17 Astana Kazakhstan 51.48 Cardiff Wales Constituent country of the United Kingdom 51.5\n\n[NEW 6] rriage in 2001. Its mixed-market advanced economy has the eleventh-highest per capita income globally. The Hague holds the seat of the States General , cabinet , and Supreme Court . The Port of Rotterdam is the busiest in Europe . Schiphol is the busiest airport in the Netherlands , and the fourth busiest in Europe . Being a developed country, the Netherlands is a founding member of the European Union , eurozone , G10 , NATO , OECD , and WTO , as well as a part of the Schengen Area and the trilateral Benelux Union. It hosts intergovernmental organisations and international courts , many of which are in The Hague. Etymology Netherlands and the Low Countries The countries that comprise the region called the Low Countries (Netherlands, Belgium , and Luxembourg\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] of countries and dependencies by area List of European countries by area List of metropolitan areas in Asia Notes ^ Figure is for the 3 districts in Northern Asia . Total area is 17,098,246 (6,601,670) including European Russia . Largest country in the world by area. ^ Excluding Taiwan , Hong Kong a...\n[KEPT 2] Below is a list of European countries and dependencies by area in Europe . As a continent , Europe's total geographical area is about 10 million square kilometres. Transcontinental countries are ranked according to the size of their European part only. Inland water is included in area numbers. Relat...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 6 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1602, + "osl": 1638, + "total_tokens": 3240, + "latency_ms": 6560.75, + "tokens_per_second": 249.67 + }, + "context": { + "num_documents_evaluated": 6, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "3a4120c2-c77a-4abc-bd99-b94817b080b7", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:26:11.346662Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 1, 2024, what is the largest city of the 9th largest country by land area in Europe?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] of countries and dependencies by area List of European countries by area List of metropolitan areas in Asia Notes ^ Figure is for the 3 districts in Northern Asia . Total area is 17,098,246 (6,601,670) including European Russia . Largest country in the world by area. ^ Excluding Taiwan , Hong Kong and Macau . ^ Nine districts totaling 122,176 (47,172) are entirely in Europe and four districts totaling 52,638 (20,324) are partially in Europe. The Asia figure given includes entire districts with significant portions in Asia, with significant figures reduced to reflect the uncertainty. Total area is 2,724,900 (1,052,100) including European part, west of the Ural River . Largest landlocked country in the world. ^ Figure excludes two provinces in Western New Gui\n\n[DOC 2] Below is a list of European countries and dependencies by area in Europe . As a continent , Europe's total geographical area is about 10 million square kilometres. Transcontinental countries are ranked according to the size of their European part only. Inland water is included in area numbers. Relative sizes European countries vary in area over many orders of magnitude , ranging from Russia which covers almost 4 000 000 km 2 of territory within Europe according to \"Definition\" below , to Vatican City , which has a total area of less than 1 km 2 : List of European countries and dependencies by area Figures are from the United Nations unless otherwise specified. Country or dependency % total Europe area km 2 mi 2 – Europe 100% 10,014,000 3,866,000 1 K Russia 3\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents provide general information about European countries by area, but they do not explicitly list the ranking to identify the 9th largest country, nor do they contain data about the largest cities of the countries. Therefore, the specific fact required—namely the name of the largest city of the 9th largest European country as of August 1, 2024—is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 648, + "osl": 398, + "total_tokens": 1046, + "latency_ms": 5838.19, + "tokens_per_second": 68.17 + }, + "context": { + "kept_docs_count": 2, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "81613b4b-129b-4dc2-bf37-bb4635ff6932", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:26:17.236655Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 1, 2024, what is the largest city of the 9th largest country by land area in Europe?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] of countries and dependencies by area List of European countries by area List of metropolitan areas in Asia Notes ^ Figure is for the 3 districts in Northern Asia . Total area is 17,098,246 (6,601,670) including European Russia . Largest country in the world by area. ^ Excluding Taiwan , Hong Kong and Macau . ^ Nine districts totaling 122,176 (47,172) are entirely in Europe and four districts totaling 52,638 (20,324) are partially in Europe. The Asia figure given includes entire districts with significant portions in Asia, with significant figures reduced to reflect the uncertainty. Total area is 2,724,900 (1,052,100) including European part, west of the Ural River . Largest landlocked country in the world. ^ Figure excludes two provinces in Western New Gui\n\n[DOC 2] Below is a list of European countries and dependencies by area in Europe . As a continent , Europe's total geographical area is about 10 million square kilometres. Transcontinental countries are ranked according to the size of their European part only. Inland water is included in area numbers. Relative sizes European countries vary in area over many orders of magnitude , ranging from Russia which covers almost 4 000 000 km 2 of territory within Europe according to \"Definition\" below , to Vatican City , which has a total area of less than 1 km 2 : List of European countries and dependencies by area Figures are from the United Nations unless otherwise specified. Country or dependency % total Europe area km 2 mi 2 – Europe 100% 10,014,000 3,866,000 1 K Russia 3\n\n\nSEARCH HISTORY: - Query: 'European countries by area' → 2 docs\n- Query: '9th largest European country' → 4 docs\n- Query: 'largest city of Italy' → 4 docs\n- Query: 'European countries by area list' → 1 docs\n- Query: 'ninth largest European country by area' → 2 docs\n- Query: 'largest city of the ninth largest European country' → 3 docs\n\nFEEDBACK: First query retrieves a ranking list of European nations by land area. The second query isolates the country that ranks ninth on that list. The third query then obtains the name of the largest city in that specific country.\nNeed to determine which country ranks 9th in Europe by land area, then find its largest city as of Aug 1 2024.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"European countries by area Wikipedia\",\n \"9th largest country Europe area\",\n \"largest city of Poland\"\n ],\n \"feedback\": \"The first query should retrieve a full table of European nations sorted by land area. The second query isolates the country that ranks ninth on that list. The third query then provides the name of the largest city in that identified country (Poland).\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 959, + "osl": 919, + "total_tokens": 1878, + "latency_ms": 5889.34, + "tokens_per_second": 156.04 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "b8bfef95-6c8f-4605-8026-9202610a1dd9", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:26:19.593137Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 1, 2024, what is the largest city of the 9th largest country by land area in Europe?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] of countries and dependencies by area List of European countries by area List of metropolitan areas in Asia Notes ^ Figure is for the 3 districts in Northern Asia . Total area is 17,098,246 (6,601,670) including European Russia . Largest country in the world by area. ^ Excluding Taiwan , Hong Kong and Macau . ^ Nine districts totaling 122,176 (47,172) are entirely in Europe and four districts totaling 52,638 (20,324) are partially in Europe. The Asia figure given includes entire districts with significant portions in Asia, with significant figures reduced to reflect the uncertainty. Total area is 2,724,900 (1,052,100) including European part, west of the Ural River . Largest landlocked country in the world. ^ Figure excludes two provinces in Western New Gui\n\n[DOC 2] Below is a list of European countries and dependencies by area in Europe . As a continent , Europe's total geographical area is about 10 million square kilometres. Transcontinental countries are ranked according to the size of their European part only. Inland water is included in area numbers. Relative sizes European countries vary in area over many orders of magnitude , ranging from Russia which covers almost 4 000 000 km 2 of territory within Europe according to \"Definition\" below , to Vatican City , which has a total area of less than 1 km 2 : List of European countries and dependencies by area Figures are from the United Nations unless otherwise specified. Country or dependency % total Europe area km 2 mi 2 – Europe 100% 10,014,000 3,866,000 1 K Russia 3\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents only contain partial excerpts of lists of European countries by area and do not provide a clear ranking to identify the 9th largest country, nor do they mention the largest city of that country. Therefore, the required facts to answer the question are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 657, + "osl": 433, + "total_tokens": 1090, + "latency_ms": 1666.99, + "tokens_per_second": 259.75 + }, + "context": { + "kept_docs_count": 2, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "5de2c6e6-c2fa-4c62-a047-96719e0085e1", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:26:23.085579Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of August 1, 2024, what is the largest city of the 9th largest country by land area in Europe?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] of countries and dependencies by area List of European countries by area List of metropolitan areas in Asia Notes ^ Figure is for the 3 districts in Northern Asia . Total area is 17,098,246 (6,601,670) including European Russia . Largest country in the world by area. ^ Excluding Taiwan , Hong Kong and Macau . ^ Nine districts totaling 122,176 (47,172) are entirely in Europe and four districts totaling 52,638 (20,324) are partially in Europe. The Asia figure given includes entire districts with significant portions in Asia, with significant figures reduced to reflect the uncertainty. Total area is 2,724,900 (1,052,100) including European part, west of the Ural River . Largest landlocked country in the world. ^ Figure excludes two provinces in Western New Gui\n\n[DOC 2] Below is a list of European countries and dependencies by area in Europe . As a continent , Europe's total geographical area is about 10 million square kilometres. Transcontinental countries are ranked according to the size of their European part only. Inland water is included in area numbers. Relative sizes European countries vary in area over many orders of magnitude , ranging from Russia which covers almost 4 000 000 km 2 of territory within Europe according to \"Definition\" below , to Vatican City , which has a total area of less than 1 km 2 : List of European countries and dependencies by area Figures are from the United Nations unless otherwise specified. Country or dependency % total Europe area km 2 mi 2 – Europe 100% 10,014,000 3,866,000 1 K Russia 3\n\n\nSEARCH HISTORY: - Query: 'European countries by area' → 2 docs\n- Query: '9th largest European country' → 4 docs\n- Query: 'largest city of Italy' → 4 docs\n- Query: 'European countries by area list' → 1 docs\n- Query: 'ninth largest European country by area' → 2 docs\n- Query: 'largest city of the ninth largest European country' → 3 docs\n- Query: 'European countries by area Wikipedia' → 0 docs\n- Query: '9th largest country Europe area' → 0 docs\n- Query: 'largest city of Poland' → 0 docs\n\nFEEDBACK: First query retrieves a ranking list of European nations by land area. The second query isolates the country that ranks ninth on that list. The third query then obtains the name of the largest city in that specific country.\nNeed to determine which country ranks 9th in Europe by land area, then find its largest city as of Aug 1 2024.\nThe first query should retrieve a full table of European nations sorted by land area. The second query isolates the country that ranks ninth on that list. The third query then provides the name of the largest city in that identified country (Poland).\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"List of European countries by area\",\n \"Poland largest city\",\n \"Warsaw population 2024\"\n ],\n \"feedback\": \"First query gets the ranking list to confirm the 9th largest European country. Second query directly retrieves the name of the largest city in that country. Third query verifies the city’s status and provides up‑to‑date population data as of Aug 1 2024.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1052, + "osl": 482, + "total_tokens": 1534, + "latency_ms": 3491.62, + "tokens_per_second": 138.04 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "8e18121b-a4f1-427d-a67f-23aa31ddd1c5", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T03:26:37.834791Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of August 1, 2024, what is the largest city of the 9th largest country by land area in Europe?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] ion pyramid in 2021 In December 2020, the estimated population of Wrocław was 641,928, of which 342,215 were women and 299,713 were men. Since 2011, the population has been steadily rising, with a 0.142% increase between 2019 and 2020, and a 2.167% increase in the years 2011–2020. In 2018, the crude birth rate stood at 11.8 and the mortality rate at 11.1 per 1,000 residents. The median age in 2018 was 43 years. The city's population is aging significantly; between 2013 and 2018, the number of seniors , per Statistics Poland – men aged 65 or above and women aged 60 or above, surged from 21.5% to 24.2%. Historically, the city's population grew rapidly throughout the 19th and 20th centuries. In 1900, approximately 422,709 people were registered as residents. In\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] of countries and dependencies by area List of European countries by area List of metropolitan areas in Asia Notes ^ Figure is for the 3 districts in Northern Asia . Total area is 17,098,246 (6,601,670) including European Russia . Largest country in the world by area. ^ Excluding Taiwan , Hong Kong a...\n[KEPT 2] Below is a list of European countries and dependencies by area in Europe . As a continent , Europe's total geographical area is about 10 million square kilometres. Transcontinental countries are ranked according to the size of their European part only. Inland water is included in area numbers. Relat...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 1 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 630, + "osl": 1261, + "total_tokens": 1891, + "latency_ms": 14155.02, + "tokens_per_second": 89.09 + }, + "context": { + "num_documents_evaluated": 1, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "aee33f33-2272-44ac-8f3a-ff5c3fc4c66d", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:26:58.236276Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of August 1, 2024, what is the largest city of the 9th largest country by land area in Europe?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] of countries and dependencies by area List of European countries by area List of metropolitan areas in Asia Notes ^ Figure is for the 3 districts in Northern Asia . Total area is 17,098,246 (6,601,670) including European Russia . Largest country in the world by area. ^ Excluding Taiwan , Hong Kong and Macau . ^ Nine districts totaling 122,176 (47,172) are entirely in Europe and four districts totaling 52,638 (20,324) are partially in Europe. The Asia figure given includes entire districts with significant portions in Asia, with significant figures reduced to reflect the uncertainty. Total area is 2,724,900 (1,052,100) including European part, west of the Ural River . Largest landlocked country in the world. ^ Figure excludes two provinces in Western New Gui\n\n[DOC 2] Below is a list of European countries and dependencies by area in Europe . As a continent , Europe's total geographical area is about 10 million square kilometres. Transcontinental countries are ranked according to the size of their European part only. Inland water is included in area numbers. Relative sizes European countries vary in area over many orders of magnitude , ranging from Russia which covers almost 4 000 000 km 2 of territory within Europe according to \"Definition\" below , to Vatican City , which has a total area of less than 1 km 2 : List of European countries and dependencies by area Figures are from the United Nations unless otherwise specified. Country or dependency % total Europe area km 2 mi 2 – Europe 100% 10,014,000 3,866,000 1 K Russia 3\n\n[DOC 3] ion pyramid in 2021 In December 2020, the estimated population of Wrocław was 641,928, of which 342,215 were women and 299,713 were men. Since 2011, the population has been steadily rising, with a 0.142% increase between 2019 and 2020, and a 2.167% increase in the years 2011–2020. In 2018, the crude birth rate stood at 11.8 and the mortality rate at 11.1 per 1,000 residents. The median age in 2018 was 43 years. The city's population is aging significantly; between 2013 and 2018, the number of seniors , per Statistics Poland – men aged 65 or above and women aged 60 or above, surged from 21.5% to 24.2%. Historically, the city's population grew rapidly throughout the 19th and 20th centuries. In 1900, approximately 422,709 people were registered as residents. In\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents list European countries by area but do not provide a clear ranking to identify the 9th largest country, nor do they state the largest city of that country. While Doc3 mentions a city (Wrocław) in Poland, it does not confirm that Poland is the 9th largest country nor identify Warsaw as its largest city. Therefore, the required facts to answer the question are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 857, + "osl": 843, + "total_tokens": 1700, + "latency_ms": 20400.69, + "tokens_per_second": 41.32 + }, + "context": { + "kept_docs_count": 3, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "7a39e445-1d1b-4b56-a481-a189949dd252", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:27:21.319842Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: As of August 1, 2024, what is the largest city of the 9th largest country by land area in Europe?\n\nDOCUMENTS:\n\n[DOC 1] of countries and dependencies by area List of European countries by area List of metropolitan areas in Asia Notes ^ Figure is for the 3 districts in Northern Asia . Total area is 17,098,246 (6,601,670) including European Russia . Largest country in the world by area. ^ Excluding Taiwan , Hong Kong and Macau . ^ Nine districts totaling 122,176 (47,172) are entirely in Europe and four districts totaling 52,638 (20,324) are partially in Europe. The Asia figure given includes entire districts with significant portions in Asia, with significant figures reduced to reflect the uncertainty. Total area is 2,724,900 (1,052,100) including European part, west of the Ural River . Largest landlocked country in the world. ^ Figure excludes two provinces in Western New Gui\n\n[DOC 2] Below is a list of European countries and dependencies by area in Europe . As a continent , Europe's total geographical area is about 10 million square kilometres. Transcontinental countries are ranked according to the size of their European part only. Inland water is included in area numbers. Relative sizes European countries vary in area over many orders of magnitude , ranging from Russia which covers almost 4 000 000 km 2 of territory within Europe according to \"Definition\" below , to Vatican City , which has a total area of less than 1 km 2 : List of European countries and dependencies by area Figures are from the United Nations unless otherwise specified. Country or dependency % total Europe area km 2 mi 2 – Europe 100% 10,014,000 3,866,000 1 K Russia 3\n\n[DOC 3] ion pyramid in 2021 In December 2020, the estimated population of Wrocław was 641,928, of which 342,215 were women and 299,713 were men. Since 2011, the population has been steadily rising, with a 0.142% increase between 2019 and 2020, and a 2.167% increase in the years 2011–2020. In 2018, the crude birth rate stood at 11.8 and the mortality rate at 11.1 per 1,000 residents. The median age in 2018 was 43 years. The city's population is aging significantly; between 2013 and 2018, the number of seniors , per Statistics Poland – men aged 65 or above and women aged 60 or above, surged from 21.5% to 24.2%. Historically, the city's population grew rapidly throughout the 19th and 20th centuries. In 1900, approximately 422,709 people were registered as residents. In\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 711, + "osl": 671, + "total_tokens": 1382, + "latency_ms": 23082.86, + "tokens_per_second": 29.07 + }, + "context": { + "num_documents_used": 3, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:27:21.320711Z", + "summary": { + "total_llm_calls": 12, + "total_hops": 5, + "total_input_tokens": 12150, + "total_output_tokens": 9075, + "total_tokens": 21225, + "total_latency_ms": 112261.47, + "average_tokens_per_second": 117.12, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/List_of_European_countries_by_area", + "https://en.wikipedia.org/wiki/Poland" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "The largest city of the 9th largest country in Europe is Warsaw." + } + }, + { + "query_id": "47", + "original_query": "What was the running time of the first cartoon in the series that inspired the name of the Looney Tunes franchise?", + "timestamp_start": "2026-05-16T03:27:21.359650Z", + "llm_calls": [ + { + "call_id": "311de993-9473-4a13-a327-04e48f63c6ee", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:27:38.037418Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What was the running time of the first cartoon in the series that inspired the name of the Looney Tunes franchise?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Merrie Melodies first cartoon\",\n \"Merrie Melodies debut cartoon runtime\",\n \"list Merrie Melodies cartoons\"\n ],\n \"feedback\": \"Need to identify the inaugural Merrie Melodies short (the series that inspired the Looney Tunes name) and discover its running time.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 445, + "osl": 636, + "total_tokens": 1081, + "latency_ms": 16677.59, + "tokens_per_second": 38.14 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "a8a09be4-d20e-4062-a5f2-8fc5f1331972", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:27:54.662852Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: What was the running time of the first cartoon in the series that inspired the name of the Looney Tunes franchise?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] n Arts Animation (1967–1969) Warner Bros. Animation (1987–2014) Distributed by Warner Bros. Pictures The Vitaphone Corporation Vitagraph Company of America Release date April 19, 1930—June 10, 2014 Running time 6–10 minutes (one reel) Looney Tunes and Merrie Melodies were so named as a reference to Disney's Silly Symphonies and were initially developed to showcase tracks from Warner Bros.' extensive music library; the title of the first Looney Tunes short, Sinkin' in the Bathtub (1930), is a pun on Singin' in the Bathtub . Between 1934 and 1943, Merrie Melodies were produced in color and Looney Tunes in black-and-white. After 1943, both series were produced in color and became virtually indistinguishable, varying only in their opening theme music and titles.\n\n[NEW 2] imation Silly Symphony Swings at California Adventure List of Disney animated shorts Silly Symphonies the newspaper comic strip , featuring adaptations of the animated shorts Merrie Melodies , from Warner Bros. Pictures Happy Harmonies , from Metro-Goldwyn-Mayer Color Classics , from Fleischer Studios Color Rhapsody , from Screen Gems References ^ a b c d ^ ^ ^ Silly Symphonies, 1929–1935 - San Francisco Silent Film Festival ^ ^ ^ ^ ^ {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ ^ ^ ^ ^ a b ^ a b ^ ^ ^ Walt Disney: The Animated Man by Michael Barrier ^ ^ ^ ^ ^ a b ^ ^ Further reading Maltin, Leonard: The Disney Films . (Fourth edition.) New York: Disney Editions, 2000. ISBN 0-7868-8527-0 . Merritt, Russel – Kaufman, J. B.: Walt Disney's\n\n[NEW 3] pore Oddbods 3 180 United States Ghostbusters 8 180 United States Steven Universe 6 180 United States Muppet Babies (1984) / Muppet Babies (2018) 11 (8+3) 178 (107+71) United States The Lion King 6 159 United States Littlest Pet Shop 5 156 United States Sabrina the Teenage Witch 4 148 Canada Caillou 5 144 United States How to Train Your Dragon 10 144 France The Adventures of Tintin 10 141 United States Cow and Chicken 8 141 United Kingdom The Clangers 5 130 United States Wacky Races 5 129 United Kingdom Numberblocks 8 124 United States Busytown 7 117 United States Kung Fu Panda 5 117 United States Avatar: The Last Airbender 8 116 United States Winnie the Pooh 7 113 United Kingdom The Wombles 5 112 United States Heathcliff 4 112 United States Super Friends 9\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 3 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 1, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 875, + "osl": 701, + "total_tokens": 1576, + "latency_ms": 16072.88, + "tokens_per_second": 43.61 + }, + "context": { + "num_documents_evaluated": 3, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "0ba0dac8-5a09-4722-b36e-4274a5ff3944", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:28:06.052180Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What was the running time of the first cartoon in the series that inspired the name of the Looney Tunes franchise?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] n Arts Animation (1967–1969) Warner Bros. Animation (1987–2014) Distributed by Warner Bros. Pictures The Vitaphone Corporation Vitagraph Company of America Release date April 19, 1930—June 10, 2014 Running time 6–10 minutes (one reel) Looney Tunes and Merrie Melodies were so named as a reference to Disney's Silly Symphonies and were initially developed to showcase tracks from Warner Bros.' extensive music library; the title of the first Looney Tunes short, Sinkin' in the Bathtub (1930), is a pun on Singin' in the Bathtub . Between 1934 and 1943, Merrie Melodies were produced in color and Looney Tunes in black-and-white. After 1943, both series were produced in color and became virtually indistinguishable, varying only in their opening theme music and titles.\n\n[DOC 2] imation Silly Symphony Swings at California Adventure List of Disney animated shorts Silly Symphonies the newspaper comic strip , featuring adaptations of the animated shorts Merrie Melodies , from Warner Bros. Pictures Happy Harmonies , from Metro-Goldwyn-Mayer Color Classics , from Fleischer Studios Color Rhapsody , from Screen Gems References ^ a b c d ^ ^ ^ Silly Symphonies, 1929–1935 - San Francisco Silent Film Festival ^ ^ ^ ^ ^ {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ ^ ^ ^ ^ a b ^ a b ^ ^ ^ Walt Disney: The Animated Man by Michael Barrier ^ ^ ^ ^ ^ a b ^ ^ Further reading Maltin, Leonard: The Disney Films . (Fourth edition.) New York: Disney Editions, 2000. ISBN 0-7868-8527-0 . Merritt, Russel – Kaufman, J. B.: Walt Disney's\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents confirm that Looney Tunes was named after Disney's Silly Symphonies, but they do not provide the running time of the first Silly Symphonies cartoon. The necessary detail (runtime of that first cartoon) is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 680, + "osl": 324, + "total_tokens": 1004, + "latency_ms": 11388.43, + "tokens_per_second": 28.45 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "1d79d416-332e-4992-85c9-8c49c03b110c", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:28:09.061121Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What was the running time of the first cartoon in the series that inspired the name of the Looney Tunes franchise?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] n Arts Animation (1967–1969) Warner Bros. Animation (1987–2014) Distributed by Warner Bros. Pictures The Vitaphone Corporation Vitagraph Company of America Release date April 19, 1930—June 10, 2014 Running time 6–10 minutes (one reel) Looney Tunes and Merrie Melodies were so named as a reference to Disney's Silly Symphonies and were initially developed to showcase tracks from Warner Bros.' extensive music library; the title of the first Looney Tunes short, Sinkin' in the Bathtub (1930), is a pun on Singin' in the Bathtub . Between 1934 and 1943, Merrie Melodies were produced in color and Looney Tunes in black-and-white. After 1943, both series were produced in color and became virtually indistinguishable, varying only in their opening theme music and titles.\n\n[DOC 2] imation Silly Symphony Swings at California Adventure List of Disney animated shorts Silly Symphonies the newspaper comic strip , featuring adaptations of the animated shorts Merrie Melodies , from Warner Bros. Pictures Happy Harmonies , from Metro-Goldwyn-Mayer Color Classics , from Fleischer Studios Color Rhapsody , from Screen Gems References ^ a b c d ^ ^ ^ Silly Symphonies, 1929–1935 - San Francisco Silent Film Festival ^ ^ ^ ^ ^ {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ ^ ^ ^ ^ a b ^ a b ^ ^ ^ Walt Disney: The Animated Man by Michael Barrier ^ ^ ^ ^ ^ a b ^ ^ Further reading Maltin, Leonard: The Disney Films . (Fourth edition.) New York: Disney Editions, 2000. ISBN 0-7868-8527-0 . Merritt, Russel – Kaufman, J. B.: Walt Disney's\n\n\nSEARCH HISTORY: - Query: 'Merrie Melodies first cartoon' → 1 docs\n- Query: 'Merrie Melodies debut cartoon runtime' → 0 docs\n- Query: 'list Merrie Melodies cartoons' → 2 docs\n\nFEEDBACK: Need to identify the inaugural Merrie Melodies short (the series that inspired the Looney Tunes name) and discover its running time.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"first Silly Symphony\",\n \"Silly Symphony debut\",\n \"Silly Symphony first short runtime\"\n ],\n \"feedback\": \"Need to identify the inaugural Silly Symphony cartoon (the series that inspired the Looney Tunes name) and obtain its running time.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 920, + "osl": 411, + "total_tokens": 1331, + "latency_ms": 3008.27, + "tokens_per_second": 136.62 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "badd4a82-777f-423b-b862-0aa8269754a2", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:28:10.138633Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What was the running time of the first cartoon in the series that inspired the name of the Looney Tunes franchise?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] n Arts Animation (1967–1969) Warner Bros. Animation (1987–2014) Distributed by Warner Bros. Pictures The Vitaphone Corporation Vitagraph Company of America Release date April 19, 1930—June 10, 2014 Running time 6–10 minutes (one reel) Looney Tunes and Merrie Melodies were so named as a reference to Disney's Silly Symphonies and were initially developed to showcase tracks from Warner Bros.' extensive music library; the title of the first Looney Tunes short, Sinkin' in the Bathtub (1930), is a pun on Singin' in the Bathtub . Between 1934 and 1943, Merrie Melodies were produced in color and Looney Tunes in black-and-white. After 1943, both series were produced in color and became virtually indistinguishable, varying only in their opening theme music and titles.\n\n[DOC 2] imation Silly Symphony Swings at California Adventure List of Disney animated shorts Silly Symphonies the newspaper comic strip , featuring adaptations of the animated shorts Merrie Melodies , from Warner Bros. Pictures Happy Harmonies , from Metro-Goldwyn-Mayer Color Classics , from Fleischer Studios Color Rhapsody , from Screen Gems References ^ a b c d ^ ^ ^ Silly Symphonies, 1929–1935 - San Francisco Silent Film Festival ^ ^ ^ ^ ^ {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ ^ ^ ^ ^ a b ^ a b ^ ^ ^ Walt Disney: The Animated Man by Michael Barrier ^ ^ ^ ^ ^ a b ^ ^ Further reading Maltin, Leonard: The Disney Films . (Fourth edition.) New York: Disney Editions, 2000. ISBN 0-7868-8527-0 . Merritt, Russel – Kaufman, J. B.: Walt Disney's\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents explain that Looney Tunes was named as a reference to Disney's Silly Symphonies and give a running time range (6–10 minutes) for Looney Tunes shorts, but they do not provide the running time of the first Silly Symphonies cartoon. The required specific runtime of the inaugural Silly Symphonies short is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 693, + "osl": 427, + "total_tokens": 1120, + "latency_ms": 560.06, + "tokens_per_second": 762.42 + }, + "context": { + "kept_docs_count": 2, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "379ce884-85bb-42c2-98a3-54ef198cefd2", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:28:14.122954Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What was the running time of the first cartoon in the series that inspired the name of the Looney Tunes franchise?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] n Arts Animation (1967–1969) Warner Bros. Animation (1987–2014) Distributed by Warner Bros. Pictures The Vitaphone Corporation Vitagraph Company of America Release date April 19, 1930—June 10, 2014 Running time 6–10 minutes (one reel) Looney Tunes and Merrie Melodies were so named as a reference to Disney's Silly Symphonies and were initially developed to showcase tracks from Warner Bros.' extensive music library; the title of the first Looney Tunes short, Sinkin' in the Bathtub (1930), is a pun on Singin' in the Bathtub . Between 1934 and 1943, Merrie Melodies were produced in color and Looney Tunes in black-and-white. After 1943, both series were produced in color and became virtually indistinguishable, varying only in their opening theme music and titles.\n\n[DOC 2] imation Silly Symphony Swings at California Adventure List of Disney animated shorts Silly Symphonies the newspaper comic strip , featuring adaptations of the animated shorts Merrie Melodies , from Warner Bros. Pictures Happy Harmonies , from Metro-Goldwyn-Mayer Color Classics , from Fleischer Studios Color Rhapsody , from Screen Gems References ^ a b c d ^ ^ ^ Silly Symphonies, 1929–1935 - San Francisco Silent Film Festival ^ ^ ^ ^ ^ {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ ^ ^ ^ ^ a b ^ a b ^ ^ ^ Walt Disney: The Animated Man by Michael Barrier ^ ^ ^ ^ ^ a b ^ ^ Further reading Maltin, Leonard: The Disney Films . (Fourth edition.) New York: Disney Editions, 2000. ISBN 0-7868-8527-0 . Merritt, Russel – Kaufman, J. B.: Walt Disney's\n\n\nSEARCH HISTORY: - Query: 'Merrie Melodies first cartoon' → 1 docs\n- Query: 'Merrie Melodies debut cartoon runtime' → 0 docs\n- Query: 'list Merrie Melodies cartoons' → 2 docs\n- Query: 'first Silly Symphony' → 0 docs\n- Query: 'Silly Symphony debut' → 0 docs\n- Query: 'Silly Symphony first short runtime' → 0 docs\n\nFEEDBACK: Need to identify the inaugural Merrie Melodies short (the series that inspired the Looney Tunes name) and discover its running time.\nNeed to identify the inaugural Silly Symphony cartoon (the series that inspired the Looney Tunes name) and obtain its running time.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Silly Symphonies\",\n \"List of Silly Symphonies\",\n \"The Skeleton Dance runtime\"\n ],\n \"feedback\": \"Need to locate the first Silly Symphony cartoon (the series that inspired the Looney Tunes name) and find its running time.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 936, + "osl": 66, + "total_tokens": 1002, + "latency_ms": 3983.54, + "tokens_per_second": 16.57 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "8e3490ed-900d-47c8-adb2-182a20a45b06", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T03:28:32.816157Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: What was the running time of the first cartoon in the series that inspired the name of the Looney Tunes franchise?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] h 2003 , event occurs at 31:00. ^ Allerman 2013 , pp. 246–247. ^ Smith 2003 , event occurs at 35:47. ^ Smith 2003 , event occurs at 36:00. ^ Smith 2003 , event occurs at 40:10. ^ Smith 2003 , event occurs at 40:15. ^ Smith 2003 , event occurs at 41:01. ^ ^ Burnand & Mena 2004 , p. 59. ^ ^ ^ a b {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ Larson 1985 , p. 287. ^ ^ Halloween Soundtrack information from HalloweenMovies. com ; last accessed April 19, 2006. ^ ^ ^ ^ ^ ^ ^ a b Muir 2012 , p. 16. ^ Leeder 2014 , p. 32. ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ a b Martin & Porter 1986 , p. 60. ^ Rockoff 2011 , p. 57. ^ Saturn Award Nominees and\n\n[NEW 2] ter Company for Skeleton Crew . The play won the Edgerton Foundation New Play Award in 2015. Skeleton Crew opened on Broadway in January 2022. It was nominated for the Tony Award for Best Play. Works Play Year Premiered Length Notes Follow Me To Nellie's 2011 Follow me to Nellie's premiered at Premiere Stages , Kean University Zella Fry Theatre in North New Jersey in July 2011 under the direction of John Wooten. Detroit '67 2013 120 Minutes Detroit '67 was first presented Off Broadway at the Public Theater in association with Classical Theatre of Harlem and the National Black Theatre in New York City on March 11, 2013. It was directed by Kwame Kwei-Armah . Sunset Baby 2013 90 Minutes Sunset Baby premiered at the LAByrinth Theatre Company on November 6, 2013\n\n[NEW 3] is reading; the monsters and villains arrive. 00:38:37; 00:43:10 \" Tubular Bells (Part One - Swing)\" Mike Oldfield Mike Oldfield Second to the right, and straight on till morning segment (NHS/Great Ormond Street Hospital) jitterbugging nurses bit 00:40:09 \"Secrets / Far Above the Clouds\" (from Tubular Bells III ) Alistair Mulloy, Luke Oldfield, Ash Soan Musical Director: Robin Smith Mike Oldfield Second to the right, and straight on till morning segment (NHS/Great Ormond Street Hospital) putting the children to bed, moon shape made with beds, Mary Poppinses/Poppi arrive and vanquish the monsters 00:42:18 \" In Dulci Jubilo \" Mike Oldfield Recorder: Andy Findon Traditional 14th-century song Second to the right, and straight on till morning segment (NHS/Great O\n\n[NEW 4] ⁄ 2 minutes. The final episode of season two introduced the first of what would be several \"super-sized\" episodes that had an approximately 28-minute running time for a 40-minute time slot. Season three introduced the first of occasional hour-long episodes (approximately 42-minute running time, also suitable for being shown as two separate normal episodes in reruns). Season Episodes Originally released Rank Average viewership (in millions) First released Last released 1 6 March 24, 2005 ( 2005-03-24 ) April 26, 2005 ( 2005-04-26 ) 102 5.4 2 22 September 20, 2005 ( 2005-09-20 ) May 11, 2006 ( 2006-05-11 ) 67 8.0 3 25 September 21, 2006 ( 2006-09-21 ) May 17, 2007 ( 2007-05-17 ) 68 8.3 4 19 September 27, 2007 ( 2007-09-27 ) May 15, 2008 ( 2008-05-15 ) 77 8.9\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] n Arts Animation (1967–1969) Warner Bros. Animation (1987–2014) Distributed by Warner Bros. Pictures The Vitaphone Corporation Vitagraph Company of America Release date April 19, 1930—June 10, 2014 Running time 6–10 minutes (one reel) Looney Tunes and Merrie Melodies were so named as a reference to ...\n[KEPT 2] imation Silly Symphony Swings at California Adventure List of Disney animated shorts Silly Symphonies the newspaper comic strip , featuring adaptations of the animated shorts Merrie Melodies , from Warner Bros. Pictures Happy Harmonies , from Metro-Goldwyn-Mayer Color Classics , from Fleischer Studi...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 4 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[0,0,0,0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1346, + "osl": 1018, + "total_tokens": 2364, + "latency_ms": 18189.65, + "tokens_per_second": 55.97 + }, + "context": { + "num_documents_evaluated": 4, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "c52126af-0ca1-4528-8ca8-fbb97bd8e15d", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:28:37.388240Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What was the running time of the first cartoon in the series that inspired the name of the Looney Tunes franchise?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] n Arts Animation (1967–1969) Warner Bros. Animation (1987–2014) Distributed by Warner Bros. Pictures The Vitaphone Corporation Vitagraph Company of America Release date April 19, 1930—June 10, 2014 Running time 6–10 minutes (one reel) Looney Tunes and Merrie Melodies were so named as a reference to Disney's Silly Symphonies and were initially developed to showcase tracks from Warner Bros.' extensive music library; the title of the first Looney Tunes short, Sinkin' in the Bathtub (1930), is a pun on Singin' in the Bathtub . Between 1934 and 1943, Merrie Melodies were produced in color and Looney Tunes in black-and-white. After 1943, both series were produced in color and became virtually indistinguishable, varying only in their opening theme music and titles.\n\n[DOC 2] imation Silly Symphony Swings at California Adventure List of Disney animated shorts Silly Symphonies the newspaper comic strip , featuring adaptations of the animated shorts Merrie Melodies , from Warner Bros. Pictures Happy Harmonies , from Metro-Goldwyn-Mayer Color Classics , from Fleischer Studios Color Rhapsody , from Screen Gems References ^ a b c d ^ ^ ^ Silly Symphonies, 1929–1935 - San Francisco Silent Film Festival ^ ^ ^ ^ ^ {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ ^ ^ ^ ^ a b ^ a b ^ ^ ^ Walt Disney: The Animated Man by Michael Barrier ^ ^ ^ ^ ^ a b ^ ^ Further reading Maltin, Leonard: The Disney Films . (Fourth edition.) New York: Disney Editions, 2000. ISBN 0-7868-8527-0 . Merritt, Russel – Kaufman, J. B.: Walt Disney's\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents identify that Looney Tunes was named after Disney's Silly Symphonies, and provide the running time (6–10 minutes) for the first Looney Tunes short, but they do not give the running time of the first Silly Symphony cartoon itself. Therefore the required fact—the running time of the first Silly Symphony cartoon—is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 695, + "osl": 328, + "total_tokens": 1023, + "latency_ms": 4571.14, + "tokens_per_second": 71.75 + }, + "context": { + "kept_docs_count": 2, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "b4a4bdf6-26a6-4871-bad6-6698ecc9f458", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:28:40.861074Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: What was the running time of the first cartoon in the series that inspired the name of the Looney Tunes franchise?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] n Arts Animation (1967–1969) Warner Bros. Animation (1987–2014) Distributed by Warner Bros. Pictures The Vitaphone Corporation Vitagraph Company of America Release date April 19, 1930—June 10, 2014 Running time 6–10 minutes (one reel) Looney Tunes and Merrie Melodies were so named as a reference to Disney's Silly Symphonies and were initially developed to showcase tracks from Warner Bros.' extensive music library; the title of the first Looney Tunes short, Sinkin' in the Bathtub (1930), is a pun on Singin' in the Bathtub . Between 1934 and 1943, Merrie Melodies were produced in color and Looney Tunes in black-and-white. After 1943, both series were produced in color and became virtually indistinguishable, varying only in their opening theme music and titles.\n\n[DOC 2] imation Silly Symphony Swings at California Adventure List of Disney animated shorts Silly Symphonies the newspaper comic strip , featuring adaptations of the animated shorts Merrie Melodies , from Warner Bros. Pictures Happy Harmonies , from Metro-Goldwyn-Mayer Color Classics , from Fleischer Studios Color Rhapsody , from Screen Gems References ^ a b c d ^ ^ ^ Silly Symphonies, 1929–1935 - San Francisco Silent Film Festival ^ ^ ^ ^ ^ {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ ^ ^ ^ ^ a b ^ a b ^ ^ ^ Walt Disney: The Animated Man by Michael Barrier ^ ^ ^ ^ ^ a b ^ ^ Further reading Maltin, Leonard: The Disney Films . (Fourth edition.) New York: Disney Editions, 2000. ISBN 0-7868-8527-0 . Merritt, Russel – Kaufman, J. B.: Walt Disney's\n\n\nSEARCH HISTORY: - Query: 'Merrie Melodies first cartoon' → 1 docs\n- Query: 'Merrie Melodies debut cartoon runtime' → 0 docs\n- Query: 'list Merrie Melodies cartoons' → 2 docs\n- Query: 'first Silly Symphony' → 0 docs\n- Query: 'Silly Symphony debut' → 0 docs\n- Query: 'Silly Symphony first short runtime' → 0 docs\n- Query: 'Silly Symphonies' → 0 docs\n- Query: 'List of Silly Symphonies' → 0 docs\n- Query: 'The Skeleton Dance runtime' → 4 docs\n\nFEEDBACK: Need to identify the inaugural Merrie Melodies short (the series that inspired the Looney Tunes name) and discover its running time.\nNeed to identify the inaugural Silly Symphony cartoon (the series that inspired the Looney Tunes name) and obtain its running time.\nNeed to locate the first Silly Symphony cartoon (the series that inspired the Looney Tunes name) and find its running time.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"The Skeleton Dance runtime\",\n \"The Skeleton Dance Wikipedia\",\n \"Silly Symphonies first cartoon\"\n ],\n \"feedback\": \"Need the running time of the inaugural Silly Symphony (the series that inspired Looney Tunes). Prior attempts to find the first Silly Symphony short and its runtime failed; broader searches for the specific short and its Wikipedia page should retrieve the runtime information.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1009, + "osl": 89, + "total_tokens": 1098, + "latency_ms": 3472.08, + "tokens_per_second": 25.63 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "7fc28104-ef5f-4e5a-a2ad-618fc5df5688", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T03:28:46.597841Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: What was the running time of the first cartoon in the series that inspired the name of the Looney Tunes franchise?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] tal download (\"Pompeii/Waiting All Night\") Brit Awards Ltd. United Kingdom 31 March 2014 Digital download ( Audien remix) Virgin United States See also List of number-one dance singles of 2014 (U. S.) References ^ a b ^ ^ ^ ^ a b ^ ^ ^ \"Brits 2014: Nominations in full\" Archived 24 April 2019 at the Wayback Machine . BBC News. Retrieved 9 January 2014 ^ ^ ^ Bastille – Pompeii Archived 26 November 2016 at the Wayback Machine . YouTube ^ a b ^ a b ^ a b ^ a b ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ ^ ^ {{ cite web }} : CS1 maint: archived copy as title ( link ) ^ ^ ^ ^ ^ \" Bastille – Pompeii \". ARIA Top 50 Singles . Retrieved 30 January 2014. ^ \" Bastille – Pompeii \" (in German). Ö3 Austria Top 40 . Retriev\n\n[NEW 2] Another icon of the Dead is a skeleton dressed as a jester and holding a lute . This image was an airbrush painting, created by Stanley Mouse in 1972. It was originally used for the cover of The Grateful Dead Songbook . \"Dancing\" Bears A series of stylized bears who appear to be dancing was drawn by Bob Thomas as part of the back cover for the album History of the Grateful Dead, Volume One (Bear's Choice) (1973). Thomas reported that he based the bears on a lead sort from an unknown font . The bear is a reference to Owsley \"Bear\" Stanley , who recorded and produced the album. Bear himself wrote, \"the bears on the album cover are not really 'dancing'. I don't know why people think they are; their positions are quite obviously those of a high-stepping march.\"\n\n[NEW 3] 1970 , pp. 351–352. ^ a b Deaville 2005 , pp. 40–41. ^ a b Toth 2016 ^ Engel & Siegmeister 1973 , p. 222. ^ Walker 1970 , pp. 360–361. ^ Deaville 2005 , pp. 31, 254. ^ Deaville 2005 , p. 45. ^ Deaville 2005 , pp. 38–39. ^ Gilliam & Youmans 2001 . ^ Kitchen 2020 , pp. 41–77. ^ Walker 1971 , p. 26. ^ a b c d e f Pesce, Eckhardt & Mueller 2023 , §23. ^ Liszt 2021 , 23 December 1829. Sources (subscription, Wikilibrary access, or UK public library membership required) (subscription required) (subscription, Wikilibrary access, or UK public library membership required) (subscription required) (subscription, Wikilibrary access, or UK public library membership required) (subscription required) (subscription, Wikilibrary access, or UK public library membership require\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] n Arts Animation (1967–1969) Warner Bros. Animation (1987–2014) Distributed by Warner Bros. Pictures The Vitaphone Corporation Vitagraph Company of America Release date April 19, 1930—June 10, 2014 Running time 6–10 minutes (one reel) Looney Tunes and Merrie Melodies were so named as a reference to ...\n[KEPT 2] imation Silly Symphony Swings at California Adventure List of Disney animated shorts Silly Symphonies the newspaper comic strip , featuring adaptations of the animated shorts Merrie Melodies , from Warner Bros. Pictures Happy Harmonies , from Metro-Goldwyn-Mayer Color Classics , from Fleischer Studi...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 3 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1081, + "osl": 477, + "total_tokens": 1558, + "latency_ms": 5139.33, + "tokens_per_second": 92.81 + }, + "context": { + "num_documents_evaluated": 3, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "34e74cf1-9b9e-4d5f-acea-4c22af536020", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:28:52.457485Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: What was the running time of the first cartoon in the series that inspired the name of the Looney Tunes franchise?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] n Arts Animation (1967–1969) Warner Bros. Animation (1987–2014) Distributed by Warner Bros. Pictures The Vitaphone Corporation Vitagraph Company of America Release date April 19, 1930—June 10, 2014 Running time 6–10 minutes (one reel) Looney Tunes and Merrie Melodies were so named as a reference to Disney's Silly Symphonies and were initially developed to showcase tracks from Warner Bros.' extensive music library; the title of the first Looney Tunes short, Sinkin' in the Bathtub (1930), is a pun on Singin' in the Bathtub . Between 1934 and 1943, Merrie Melodies were produced in color and Looney Tunes in black-and-white. After 1943, both series were produced in color and became virtually indistinguishable, varying only in their opening theme music and titles.\n\n[DOC 2] imation Silly Symphony Swings at California Adventure List of Disney animated shorts Silly Symphonies the newspaper comic strip , featuring adaptations of the animated shorts Merrie Melodies , from Warner Bros. Pictures Happy Harmonies , from Metro-Goldwyn-Mayer Color Classics , from Fleischer Studios Color Rhapsody , from Screen Gems References ^ a b c d ^ ^ ^ Silly Symphonies, 1929–1935 - San Francisco Silent Film Festival ^ ^ ^ ^ ^ {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ ^ ^ ^ ^ a b ^ a b ^ ^ ^ Walt Disney: The Animated Man by Michael Barrier ^ ^ ^ ^ ^ a b ^ ^ Further reading Maltin, Leonard: The Disney Films . (Fourth edition.) New York: Disney Editions, 2000. ISBN 0-7868-8527-0 . Merritt, Russel – Kaufman, J. B.: Walt Disney's\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents provide the running time range for Looney Tunes shorts (6–10 minutes) and discuss Silly Symphonies as the series that inspired the Looney Tunes name, but they do not specify the first Silly Symphony cartoon nor its running time. Therefore the required fact – the running time of the first cartoon in the Silly Symphony series – is missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 695, + "osl": 464, + "total_tokens": 1159, + "latency_ms": 5858.9, + "tokens_per_second": 79.2 + }, + "context": { + "kept_docs_count": 2, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "e22e8547-f9a3-41ab-b2d3-a0b0df04204e", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:28:58.190018Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: What was the running time of the first cartoon in the series that inspired the name of the Looney Tunes franchise?\n\nDOCUMENTS:\n\n[DOC 1] n Arts Animation (1967–1969) Warner Bros. Animation (1987–2014) Distributed by Warner Bros. Pictures The Vitaphone Corporation Vitagraph Company of America Release date April 19, 1930—June 10, 2014 Running time 6–10 minutes (one reel) Looney Tunes and Merrie Melodies were so named as a reference to Disney's Silly Symphonies and were initially developed to showcase tracks from Warner Bros.' extensive music library; the title of the first Looney Tunes short, Sinkin' in the Bathtub (1930), is a pun on Singin' in the Bathtub . Between 1934 and 1943, Merrie Melodies were produced in color and Looney Tunes in black-and-white. After 1943, both series were produced in color and became virtually indistinguishable, varying only in their opening theme music and titles.\n\n[DOC 2] imation Silly Symphony Swings at California Adventure List of Disney animated shorts Silly Symphonies the newspaper comic strip , featuring adaptations of the animated shorts Merrie Melodies , from Warner Bros. Pictures Happy Harmonies , from Metro-Goldwyn-Mayer Color Classics , from Fleischer Studios Color Rhapsody , from Screen Gems References ^ a b c d ^ ^ ^ Silly Symphonies, 1929–1935 - San Francisco Silent Film Festival ^ ^ ^ ^ ^ {{ cite web }} : CS1 maint: deprecated archival service ( link ) ^ ^ ^ ^ ^ a b ^ a b ^ ^ ^ Walt Disney: The Animated Man by Michael Barrier ^ ^ ^ ^ ^ a b ^ ^ Further reading Maltin, Leonard: The Disney Films . (Fourth edition.) New York: Disney Editions, 2000. ISBN 0-7868-8527-0 . Merritt, Russel – Kaufman, J. B.: Walt Disney's\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Unknown", + "finish_reason": "stop" + }, + "metrics": { + "isl": 534, + "osl": 173, + "total_tokens": 707, + "latency_ms": 5731.85, + "tokens_per_second": 30.18 + }, + "context": { + "num_documents_used": 2, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:28:58.190741Z", + "summary": { + "total_llm_calls": 12, + "total_hops": 5, + "total_input_tokens": 9909, + "total_output_tokens": 5114, + "total_tokens": 15023, + "total_latency_ms": 94653.72, + "average_tokens_per_second": 115.11, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/Looney_Tunes", + "https://en.wikipedia.org/wiki/Silly_Symphony" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Unknown", + "ground_truth_answer": "5 minutes and 31 seconds" + } + }, + { + "query_id": "48", + "original_query": "The state, whose motto was adopted March 26, 1928, has 0.94% of the population in 2024 speaking a language that is native to which country?", + "timestamp_start": "2026-05-16T03:28:58.230914Z", + "llm_calls": [ + { + "call_id": "27071635-928c-437a-ae19-f85b1093b985", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:28:59.042369Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: The state, whose motto was adopted March 26, 1928, has 0.94% of the population in 2024 speaking a language that is native to which country?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"state motto adopted March 26 1928\",\n \"8 March 26\"\n ],\n \"feedback\": \"These queries aim to identify the specific Ustate motto March 26 1928 United States\",\n \"U.S. state motto 192.S. state whose official motto was adopted on March 26, 1928. Once the state its 2024 population and the country where that language is native.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 473, + "osl": 316, + "total_tokens": 789, + "latency_ms": 811.32, + "tokens_per_second": 389.49 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "9f799546-0418-411d-a4e9-cdaba82dd102", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:29:05.295105Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: The state, whose motto was adopted March 26, 1928, has 0.94% of the population in 2024 speaking a language that is native to which country?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] , both which are in Latin. Kentucky , North Dakota , and Vermont also have two mottos, one in Latin and the other in English. All other states and territories have only one motto, except for Guam and the Northern Mariana Islands , which do not have any mottos. English and Latin are the most-used languages for state mottos, each used by 25 states and territories. Seven states and territories use another language, of which each language is only used once. Eight states and two territories have their mottos on their state quarter ; thirty-eight states and four territories have their mottos on their state seals. The dates given are, where possible, the earliest date that the motto was used in an official sense. Some state mottos are not official but are on the of\n\n[NEW 2] 107,974 8 Ashdod Southern 228,562 18 Modi'in Central 100,052 9 Beersheba Southern 223,587 19 Kfar Saba Central 99,410 10 Holon Tel Aviv 191,829 20 Lod Central 94,189 Language Road sign in Hebrew , Arabic , and English The official language is Hebrew . Hebrew is the primary language of the state and is spoken daily by the majority of the population. Prior to 1948, opposition to Yiddish , the historical language of the Ashkenazi Jews, was common among supporters of the Zionist movement, including the Yishuv, who sought to promote Hebrew's revival as a unifying national language. These sentiments were reflected in the early policies of the Israeli government, which largely banned Yiddish theatre and publications. Until 2018, Arabic was also an official languag\n\n[NEW 3] , 3.7% black or African American , 15.6% American Indian or Alaska Native , 6.5% Asian , 1.4% Native Hawaiian and other Pacific Islander , 7.5% two or more races, and 7.3% Hispanic or Latin American . At the survey estimates, 7.8% of the total population was foreign-born from 2015 to 2019. In 2015, 61.3% were white, 3.4% black or African American, 13.3% American Indian or Alaska Native, 6.2% Asian, 0.9% Native Hawaiian and other Pacific Islander, 0.3% some other race, and 7.7% multiracial. Hispanics and Latin Americans were 7% of the state population in 2015. From 2015 to 2019, the largest Hispanic and Latin American groups were Mexican Americans , Puerto Ricans , and Cuban Americans . The largest Asian groups living in the state were Filipinos , Korean Ame\n\n[NEW 4] i wy-OH -ming ) is a landlocked state in the Mountain West subregion of the Western United States . It borders Montana to the north and northwest, South Dakota and Nebraska to the east, Idaho to the west, Utah to the southwest, and Colorado to the south. With an estimated population of 587,618 as of 2024, Wyoming is the least populous state despite being the tenth-largest by area , and it has the second-lowest population density . The state capital and most populous city is Cheyenne . Wyoming's western half consists mostly of the ranges and rangelands of the Rocky Mountains ; its eastern half consists of high-elevation prairie , and is referred to as the High Plains . Wyoming's climate is semi-arid in some parts and continental in others, making it drier an\n\n[NEW 5] er than state in its full official name ^ a b c d e Represented by a non-voting delegate in the House of Representatives. ^ Although not organized through a federal organic act or other explicit Congressional directive on governance, the people of American Samoa adopted a constitution in 1967, and then in 1977, elected territorial officials for the first time. ^ a b Organized as a commonwealth . ^ Represented by a non-voting resident commissioner in the House of Representatives. ^ Excluding lagoon ^ Although there are no indigenous inhabitants, around 40 United States Fish and Wildlife Service staff and service contractors live on the island at any given time. ^ U. S. sovereignty is disputed by Haiti . ^ Although there are no indigenous inhabitants, between\n\n[NEW 6] theast coast of the state. The term Mainiac is considered by some to be derogatory, but is embraced with pride by others, and is used for a variety of organizations and for events such as the YMCA Mainiac Sprint Triathlon & Duathlon. See also Index of Maine-related articles Outline of Maine Maine portal References Notes ^ In the event of a vacancy in the office of governor, the president of the State Senate is first in line to become governor. ^ Maine is the U. S. state with the highest percentage of French-speaking population . ^ Elevation adjusted to North American Vertical Datum of 1988 Citations ^ ^ ^ a b ^ a b ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ MPBN, \"Rolling Back the Frontier\" Archived July 4, 2011, at the Wayback Machine , The Story of Maine ; ac\n\n[NEW 7] ges of India , is the most widely spoken and the primary official language of the Telangana state, whereas Urdu is recognised as the second official language. Additionally, several tribal languages such as Gondi , Kolami , Koya and Lambadi are spoken in different regions of the state. The economy of Telangana is the eighth largest in India, with a gross state domestic product (GSDP) of ₹ 18 trillion (US$210 billion) and has a GSDP per capita of ₹ 390,564 (US$4,600) for the financial year 2024–25. Telangana has emerged as a major focal point for IT software companies, industry and the services sector . The state is also the main administrative center of many Indian defence aerospace and research labs including Bharat Dynamics Limited , Defence Metallurgical R\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 7 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 0, 0, 0, 0, 1, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1539, + "osl": 690, + "total_tokens": 2229, + "latency_ms": 6236.84, + "tokens_per_second": 110.63 + }, + "context": { + "num_documents_evaluated": 7, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "a205173d-3204-4d8a-bde1-c05bc7a19f50", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:29:09.016098Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: The state, whose motto was adopted March 26, 1928, has 0.94% of the population in 2024 speaking a language that is native to which country?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] , both which are in Latin. Kentucky , North Dakota , and Vermont also have two mottos, one in Latin and the other in English. All other states and territories have only one motto, except for Guam and the Northern Mariana Islands , which do not have any mottos. English and Latin are the most-used languages for state mottos, each used by 25 states and territories. Seven states and territories use another language, of which each language is only used once. Eight states and two territories have their mottos on their state quarter ; thirty-eight states and four territories have their mottos on their state seals. The dates given are, where possible, the earliest date that the motto was used in an official sense. Some state mottos are not official but are on the of\n\n[DOC 2] theast coast of the state. The term Mainiac is considered by some to be derogatory, but is embraced with pride by others, and is used for a variety of organizations and for events such as the YMCA Mainiac Sprint Triathlon & Duathlon. See also Index of Maine-related articles Outline of Maine Maine portal References Notes ^ In the event of a vacancy in the office of governor, the president of the State Senate is first in line to become governor. ^ Maine is the U. S. state with the highest percentage of French-speaking population . ^ Elevation adjusted to North American Vertical Datum of 1988 Citations ^ ^ ^ a b ^ a b ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ MPBN, \"Rolling Back the Frontier\" Archived July 4, 2011, at the Wayback Machine , The Story of Maine ; ac\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The provided documents do not specify which state adopted its motto on March 26, 1928, nor do they give the 2024 language percentage of 0.94% or identify the language and its native country. Therefore, the required facts to answer the question are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 665, + "osl": 280, + "total_tokens": 945, + "latency_ms": 3720.4, + "tokens_per_second": 75.26 + }, + "context": { + "kept_docs_count": 2, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "f93a6320-2c75-4695-bdfb-05a9a0359641", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:29:18.978199Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: The state, whose motto was adopted March 26, 1928, has 0.94% of the population in 2024 speaking a language that is native to which country?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] , both which are in Latin. Kentucky , North Dakota , and Vermont also have two mottos, one in Latin and the other in English. All other states and territories have only one motto, except for Guam and the Northern Mariana Islands , which do not have any mottos. English and Latin are the most-used languages for state mottos, each used by 25 states and territories. Seven states and territories use another language, of which each language is only used once. Eight states and two territories have their mottos on their state quarter ; thirty-eight states and four territories have their mottos on their state seals. The dates given are, where possible, the earliest date that the motto was used in an official sense. Some state mottos are not official but are on the of\n\n[DOC 2] theast coast of the state. The term Mainiac is considered by some to be derogatory, but is embraced with pride by others, and is used for a variety of organizations and for events such as the YMCA Mainiac Sprint Triathlon & Duathlon. See also Index of Maine-related articles Outline of Maine Maine portal References Notes ^ In the event of a vacancy in the office of governor, the president of the State Senate is first in line to become governor. ^ Maine is the U. S. state with the highest percentage of French-speaking population . ^ Elevation adjusted to North American Vertical Datum of 1988 Citations ^ ^ ^ a b ^ a b ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ MPBN, \"Rolling Back the Frontier\" Archived July 4, 2011, at the Wayback Machine , The Story of Maine ; ac\n\n\nSEARCH HISTORY: - Query: 'The state, whose motto was adopted March 26, 1928, has 0.94% of the population in 2024 speaking a language that is native to which country?' → 7 docs\n\nFEEDBACK: Error: Expecting ':' delimiter: line 8 column 1 (char 351)\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"state motto March 26 1928\",\n \"2024 language spoken percentages by state\",\n \"language native country list\"\n ],\n \"feedback\": \"First query identifies the state whose motto was adopted on that date. Second query provides a table of language percentages for each state in 2024, allowing us to locate the 0.94% figure and the corresponding language. Third query determines the country where that language originates.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 876, + "osl": 1663, + "total_tokens": 2539, + "latency_ms": 9961.52, + "tokens_per_second": 166.94 + }, + "context": { + "kept_docs_count": 2, + "iteration_state": "generating_queries", + "query_history_length": 1 + } + }, + { + "call_id": "786e422c-4c1c-408f-943b-3143ce16543c", + "component": "evaluate_document_relevance", + "hop_count": 3, + "timestamp": "2026-05-16T03:30:20.767782Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: The state, whose motto was adopted March 26, 1928, has 0.94% of the population in 2024 speaking a language that is native to which country?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] % Alaska 72,524 91 10 101 0.139% Arizona 499,261 1,613 1,613 0.323% Arkansas 1,949,387 3,814 3,814 0.195% California 6,907,397 17,022 17,022 0.246% Colorado 1,123,296 2,697 2,697 0.240% Connecticut 1,709,242 4,347 4,347 0.254% Delaware 246,505 579 579 0.234% District of Columbia 663,091 3,029 3,029 0.456% Florida 1,897,414 3,540 3,540 0.186% Georgia 3,123,723 5,701 5,701 0.182% Hawaii 422,770 689 68 757 0.179% Idaho 524,873 1,419 1,419 0.270% Illinois 7,897,241 18,601 18,601 0.235% Indiana 3,427,796 8,131 8,131 0.237% Iowa 2,538,268 5,633 5,633 0.221% Kansas 1,801,028 4,526 4,526 0.251% Kentucky 2,845,627 6,802 6,802 0.239% Louisiana 2,363,516 3,964 3,964 0.167% Maine 847,226 2,156 2,156 0.254% Maryland 1,821,244 4,375 4,375 0.240% Massachusetts 4,316,721 10\n\n[NEW 2] mla Hill States ^ Districts: Ambala, Hoshiarpur, Gurdaspur, Sialkot, Gujrat, Jhelum, Rawalpindi, and Attock. Princely States: Kalsia ^ Districts: Montgomery, Shahpur, Mianwali, Lyallpur, Jhang, Multan, Muzaffargarh, and Dera Ghazi Khan. Princely States: Bahawalpur ^ Standard Punjabi : 58.34% Lahnda : 17.59% ^ Standard Punjabi : 58.34% Lahnda : 17.59% ^ Standard Punjabi : 63.49% Lahnda : 1.0% ^ Standard Punjabi : 74.01% Lahnda : 14.76% ^ Lahnda : 60.31% Standard Punjabi : 36.14% ^ Including Hindustani ( Hindi and Urdu ), Braj Bhasha , Haryanvi , and other related languages or dialects References ^ a b c d e f g h i j k l m n o p ^ a b ^ D. R. Bhandarkar, 1989, Some Aspects of Ancient Indian Culture: Sir William Meyers Lectures, 1938-39 Archived 7 October 2022\n\n[NEW 3] mbardy 2023 208,420 (4th) 7.23 6 / 80 8 Majority South Tyrol 2023 1,625 (15th) 0.58 0 / 35 0 No seats Trentino 2023 4,708 (12th) 2.02 0 / 35 1 No seats Veneto 2025 105,375 (4th) 6.3 3 / 51 1 Majority Friuli-Venezia Giulia 2023 26,329 (5th) 6.67 3 / 49 2 Majority Emilia-Romagna 2024 83,998 (3rd) 5.62 2 / 50 1 Opposition Liguria 2024 44,854 (5th) 7.98 3 / 31 2 Majority Tuscany 2025 78,404 (5th) 6.17 (along with UDC ) 2 / 41 1 Opposition Marche 2025 48,823 (3rd) 8.6 3 / 31 1 Majority Umbria 2024 31,128 (3rd) 9.69 2 / 21 1 Opposition Lazio 2023 130,368 (5th) 8.43 3 / 50 3 Majority Abruzzo 2024 77,841 (3rd) 13.44 4 / 31 1 Majority Molise 2023 16,924 (3rd) 11.97 3 / 21 0 Majority Campania 2025 215,419 (3rd) 10.72 6 / 51 4 Opposition Apulia 2025 121,015 (4th) 9.11\n\n[NEW 4] ry List of countries and dependencies and their capitals in native languages List of countries and dependencies by area List of countries and dependencies by population List of countries and territories by the United Nations geoscheme List of country-name etymologies List of governments in exile List of international rankings List of ISO 3166 country codes List of micronations List of national capitals List of national flags of sovereign states List of rebel groups that control territory List of states with limited recognition List of territorial disputes List of territories governed by the United Nations Lists of political entities by century Lists of state leaders by century Member states of the United Nations Sovereign state List of former sovereign state\n\n[NEW 5] 15 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 Lists by location By country Afghanistan Albania Algeria Argentina Mendoza Armenia Australia Azerbaijan Bangladesh Bosnia and Herzegovina Brazil Bulgaria Canada Chile China Sichuan Yunnan Colombia Costa Rica Croatia Cuba Cyprus Democratic Republic of the Congo Dominican Republic East Timor Ecuador Egypt El Salvador Eritrea Ethiopia France Fiji Georgia Germany Ghana Greece Guam Guatemala Haiti Iceland India Indonesia Iran Italy Irpinia Japan Kosovo Kazakhstan Kyrgyzstan Malaysia Mexico Morocco Myanmar Nepal Netherlands New Zealand Nicaragua North Korea Pakistan Panama Papua New Guinea Peru Philippines Portugal Azores Romania Vrancea Russia Kamchatka Samoa Saudi Arabia Serbia Slovenia Solomon Islands So\n\n[NEW 6] list\n\n[NEW 7] Mexico 78 MDA Moldova 34 MON Monaco 4 MGL Mongolia 20 MAR Morocco 55 MOZ Mozambique 5 MYA Myanmar 7 NAM Namibia 11 NRU Nauru 2 NEP Nepal 5 NED Netherlands 231 AHO Netherlands Antilles 7 NZL New Zealand 147 NCA Nicaragua 6 NIG Niger 4 NGR Nigeria 83 PRK North Korea 31 NOR Norway 93 OMA Oman 6 PAK Pakistan 27 PLW Palau 5 PLE Palestine 2 PAN Panama 6 PNG Papua New Guinea 5 PAR Paraguay 5 PER Peru 21 PHI Philippines 20 POL Poland 187 POR Portugal 61 PUR Puerto Rico 29 QAT Qatar 17 ROU Romania 145 RUS Russia 435 RWA Rwanda 5 SKN Saint Kitts and Nevis 2 LCA Saint Lucia 5 VIN Saint Vincent and the Grenadines 4 SAM Samoa 5 SMR San Marino 4 STP São Tomé and Príncipe 2 KSA Saudi Arabia 18 SEN Senegal 26 SEY Seychelles 9 SLE Sierra Leone 3 SIN Singapore 14 SVK Slovakia\n\n[NEW 8] Mongolia 28 MAR Morocco 27 MOZ Mozambique 8 NEP Nepal 16 HOL Netherlands 147 AHO Netherlands Antilles 3 NZL New Zealand 83 NIG Niger 6 NGR Nigeria 69 NOR Norway 69 OMA Oman 8 PAK Pakistan 30 PAN Panama 6 NGU Papua New Guinea 11 PAR Paraguay 10 PER Peru 21 PHI Philippines 31 POL Poland 143 POR Portugal 65 PUR Puerto Rico 47 QAT Qatar 10 ROM Romania 68 RWA Rwanda 6 VIN Saint Vincent and the Grenadines 6 SMR San Marino 11 KSA Saudi Arabia 9 SEN Senegal 23 SLE Sierra Leone 12 SIN Singapore 8 SOL Solomon Islands 4 SOM Somalia 5 URS Soviet Union 481 ESP Spain 229 SRI Sri Lanka 6 SUD Sudan 8 SUR Suriname 6 SWZ Swaziland 11 SWE Sweden 184 SUI Switzerland 99 SYR Syria 13 TPE Chinese Taipei 61 TAN Tanzania 10 THA Thailand 14 TOG Togo 6 TGA Tonga 5 TRI Trinidad and To\n\n\nKEPT DOCUMENTS (context only):\n[2 documents already kept as relevant]\n[KEPT 1] , both which are in Latin. Kentucky , North Dakota , and Vermont also have two mottos, one in Latin and the other in English. All other states and territories have only one motto, except for Guam and the Northern Mariana Islands , which do not have any mottos. English and Latin are the most-used lan...\n[KEPT 2] theast coast of the state. The term Mainiac is considered by some to be derogatory, but is embraced with pride by others, and is used for a variety of organizations and for events such as the YMCA Mainiac Sprint Triathlon & Duathlon. See also Index of Maine-related articles Outline of Maine Maine po...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 8 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [1, 0, 0, 0, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2236, + "osl": 3887, + "total_tokens": 6123, + "latency_ms": 61125.01, + "tokens_per_second": 63.59 + }, + "context": { + "num_documents_evaluated": 8, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 2 + } + }, + { + "call_id": "78de9963-3de5-4157-9ceb-5cc622e9f266", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:30:35.010207Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: The state, whose motto was adopted March 26, 1928, has 0.94% of the population in 2024 speaking a language that is native to which country?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] , both which are in Latin. Kentucky , North Dakota , and Vermont also have two mottos, one in Latin and the other in English. All other states and territories have only one motto, except for Guam and the Northern Mariana Islands , which do not have any mottos. English and Latin are the most-used languages for state mottos, each used by 25 states and territories. Seven states and territories use another language, of which each language is only used once. Eight states and two territories have their mottos on their state quarter ; thirty-eight states and four territories have their mottos on their state seals. The dates given are, where possible, the earliest date that the motto was used in an official sense. Some state mottos are not official but are on the of\n\n[DOC 2] theast coast of the state. The term Mainiac is considered by some to be derogatory, but is embraced with pride by others, and is used for a variety of organizations and for events such as the YMCA Mainiac Sprint Triathlon & Duathlon. See also Index of Maine-related articles Outline of Maine Maine portal References Notes ^ In the event of a vacancy in the office of governor, the president of the State Senate is first in line to become governor. ^ Maine is the U. S. state with the highest percentage of French-speaking population . ^ Elevation adjusted to North American Vertical Datum of 1988 Citations ^ ^ ^ a b ^ a b ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ MPBN, \"Rolling Back the Frontier\" Archived July 4, 2011, at the Wayback Machine , The Story of Maine ; ac\n\n[DOC 3] % Alaska 72,524 91 10 101 0.139% Arizona 499,261 1,613 1,613 0.323% Arkansas 1,949,387 3,814 3,814 0.195% California 6,907,397 17,022 17,022 0.246% Colorado 1,123,296 2,697 2,697 0.240% Connecticut 1,709,242 4,347 4,347 0.254% Delaware 246,505 579 579 0.234% District of Columbia 663,091 3,029 3,029 0.456% Florida 1,897,414 3,540 3,540 0.186% Georgia 3,123,723 5,701 5,701 0.182% Hawaii 422,770 689 68 757 0.179% Idaho 524,873 1,419 1,419 0.270% Illinois 7,897,241 18,601 18,601 0.235% Indiana 3,427,796 8,131 8,131 0.237% Iowa 2,538,268 5,633 5,633 0.221% Kansas 1,801,028 4,526 4,526 0.251% Kentucky 2,845,627 6,802 6,802 0.239% Louisiana 2,363,516 3,964 3,964 0.167% Maine 847,226 2,156 2,156 0.254% Maryland 1,821,244 4,375 4,375 0.240% Massachusetts 4,316,721 10\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents do not provide the state whose motto was adopted on March 26, 1928, nor do they show a 0.94% language-speaking statistic for any state in 2024. Without identifying the specific state and the language percentage, we cannot determine the native country of that language.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1046, + "osl": 507, + "total_tokens": 1553, + "latency_ms": 14241.27, + "tokens_per_second": 35.6 + }, + "context": { + "kept_docs_count": 3, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "fd7d514f-29ee-4660-81d8-ed5e9d7e30ef", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:30:57.766320Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: The state, whose motto was adopted March 26, 1928, has 0.94% of the population in 2024 speaking a language that is native to which country?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] , both which are in Latin. Kentucky , North Dakota , and Vermont also have two mottos, one in Latin and the other in English. All other states and territories have only one motto, except for Guam and the Northern Mariana Islands , which do not have any mottos. English and Latin are the most-used languages for state mottos, each used by 25 states and territories. Seven states and territories use another language, of which each language is only used once. Eight states and two territories have their mottos on their state quarter ; thirty-eight states and four territories have their mottos on their state seals. The dates given are, where possible, the earliest date that the motto was used in an official sense. Some state mottos are not official but are on the of\n\n[DOC 2] theast coast of the state. The term Mainiac is considered by some to be derogatory, but is embraced with pride by others, and is used for a variety of organizations and for events such as the YMCA Mainiac Sprint Triathlon & Duathlon. See also Index of Maine-related articles Outline of Maine Maine portal References Notes ^ In the event of a vacancy in the office of governor, the president of the State Senate is first in line to become governor. ^ Maine is the U. S. state with the highest percentage of French-speaking population . ^ Elevation adjusted to North American Vertical Datum of 1988 Citations ^ ^ ^ a b ^ a b ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ MPBN, \"Rolling Back the Frontier\" Archived July 4, 2011, at the Wayback Machine , The Story of Maine ; ac\n\n[DOC 3] % Alaska 72,524 91 10 101 0.139% Arizona 499,261 1,613 1,613 0.323% Arkansas 1,949,387 3,814 3,814 0.195% California 6,907,397 17,022 17,022 0.246% Colorado 1,123,296 2,697 2,697 0.240% Connecticut 1,709,242 4,347 4,347 0.254% Delaware 246,505 579 579 0.234% District of Columbia 663,091 3,029 3,029 0.456% Florida 1,897,414 3,540 3,540 0.186% Georgia 3,123,723 5,701 5,701 0.182% Hawaii 422,770 689 68 757 0.179% Idaho 524,873 1,419 1,419 0.270% Illinois 7,897,241 18,601 18,601 0.235% Indiana 3,427,796 8,131 8,131 0.237% Iowa 2,538,268 5,633 5,633 0.221% Kansas 1,801,028 4,526 4,526 0.251% Kentucky 2,845,627 6,802 6,802 0.239% Louisiana 2,363,516 3,964 3,964 0.167% Maine 847,226 2,156 2,156 0.254% Maryland 1,821,244 4,375 4,375 0.240% Massachusetts 4,316,721 10\n\n\nSEARCH HISTORY: - Query: 'The state, whose motto was adopted March 26, 1928, has 0.94% of the population in 2024 speaking a language that is native to which country?' → 7 docs\n- Query: 'state motto March 26 1928' → 0 docs\n- Query: '2024 language spoken percentages by state' → 3 docs\n- Query: 'language native country list' → 5 docs\n\nFEEDBACK: Error: Expecting ':' delimiter: line 8 column 1 (char 351)\nFirst query identifies the state whose motto was adopted on that date. Second query provides a table of language percentages for each state in 2024, allowing us to locate the 0.94% figure and the corresponding language. Third query determines the country where that language originates.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"March 26, 1928 state motto\",\n \"2024 language spoken at home by U.S. state percentages\",\n \"\\\"0.94%\\\" language spoken United States state 2024\"\n ],\n \"feedback\": \"We need to locate the state whose official motto was adopted on March 26 1928, then find the 2024 language‑usage table that shows a 0.94 % share for a specific language in that state, and finally identify the country where that language originates.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1388, + "osl": 1306, + "total_tokens": 2694, + "latency_ms": 22755.33, + "tokens_per_second": 57.39 + }, + "context": { + "kept_docs_count": 3, + "iteration_state": "generating_queries", + "query_history_length": 4 + } + }, + { + "call_id": "5d9f4334-d210-442a-83eb-928e76a7f7f5", + "component": "evaluate_document_relevance", + "hop_count": 4, + "timestamp": "2026-05-16T03:31:15.691000Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: The state, whose motto was adopted March 26, 1928, has 0.94% of the population in 2024 speaking a language that is native to which country?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] -Winter Wonderland State motto : Si quaeris peninsulam amoenam circumspice (Latin: \"If you seek a pleasant peninsula, look about you\") adopted in 1835 on the coat-of-arms, but never as an official motto. This is a paraphrase of the epitaph of British architect Sir Christopher Wren about his masterpiece, St. Paul's Cathedral . State song : \" My Michigan \" (official since 1937, but disputed amongst residents), \" Michigan, My Michigan \" (unofficial state song, since the civil war) State bird : American robin (since 1931) State animal : wolverine (traditional) State game animal: white-tailed deer (since 1997) State fish : brook trout (since 1965) State reptile : painted turtle (since 1995) State ducks : wood duck (since 2026) State fossil : mastodon (since 2000)\n\n[NEW 2] linian and Chamorro). In total, 169 Native American languages are spoken in the United States. In Puerto Rico, Spanish is more widely spoken than English. According to the American Community Survey (2020), some 245.4 million people in the U. S. age five and older spoke only English at home. About 41.2 million spoke Spanish at home, making it the second most commonly used language. Other languages spoken at home by one million people or more include Chinese (3.40 million), Tagalog (1.71 million), Vietnamese (1.52 million), Arabic (1.39 million), French (1.18 million), Korean (1.07 million), and Russian (1.04 million). German , spoken by 1 million people at home in 2010, fell to 881,000 estimated total speakers in 2020. Immigration The Mexico–United States bor\n\n[NEW 3] (4.6 people/km 2 ), making it the least densely populated state capital. There were 12,922 housing units at an average density of 4.0 units per square mile (1.5 units/km 2 ). The racial makeup of the city/borough was 64.7% White (62.5% Non-Hispanic White ), 1.0% African American , 10.1% Native American or Alaska Native , 6.7% Asian , 1.3% Pacific Islander , and 14.3% from two or more races. 7.0% of the population were Hispanic or Latino of any race. 2.6% reported speaking Tagalog at home, and 2.4% reported speaking Spanish. The most reported ancestries in 2020 were: German (18.5%) English (17.8%) Irish (17.1%) Tlingit (9.9%) Filipino (7.6%) Scottish (6.3%) Norwegian (4.9%) French (3.7%) Mexican (3.6%) Italian (3.3%) The median income for a household in the\n\n[NEW 4] ish proficiency by language, 2022 Rank Language Number of Speakers 1 Spanish 128,303 2 Vietnamese 16,292 3 Chinese 15,816 4 Russian 8,559 5 Korean 4,903 6 Ukrainian 2,534 7 Arabic 1,480 8 Tagalog 447 9 Marshallese 336 10 Japanese 333 11 Thai 169 12 French 142 13 German 139 Religious and secular communities Religious self-identification in Oregon, per PRRI American Values Atlas (2022) Unaffiliated (42.0%) Protestantism (35.0%) Catholicism (14.0%) Mormonism (2.00%) Judaism (2.00%) New Age (2.00%) Jehovah's Witness (1.00%) Buddhist (1.00%) Oregon has frequently been cited by statistical agencies for having a smaller percentage of religious communities than other U. S. states. According to a 2009 Gallup poll , Oregon was paired with Vermont as the two \"least rel\n\n[NEW 5] ne million (12.1%) are not fully fluent in English. As of the 2024 census estimate, [update] the White non-Hispanic population of the state was less than 50%, making it a majority-minority state. Compared to the U. S. as a whole, the state is more racially and ethnically diverse and has a higher proportion of immigrants. Ethnic composition as of the 2020 census Race and ethnicity Alone Total White (non-Hispanic) 51.9% 54.5% Hispanic or Latino — 21.6% African American (non-Hispanic) 12.4% 13.6% Asian 10.2% 11.0% Native American 0.1% 0.7% Pacific Islander 0.02% 0.1% Other 0.8% 1.8% Map of counties in New Jersey by racial plurality, per the 2020 census Legend Non-Hispanic White 30–40% 40–50% 50–60% 60–70% 70–80% 80–90% Black or African American 40–50% Hispanic\n\n[NEW 6] 8%) Black 70 (1.2%) 115 (2.0%) 118 (2.2%) 133 (2.5%) 108 (2.1%) 137 (2.5%) 116 (2.2%) 123 (2.4%) American Indian 11 (0.2%) 16 (0.3%) 12 (0.2%) 11 (0.2%) 15 (0.3%) 13 (0.2%) 13 (0.2%) 12 (0.2%) Hispanic (any race) 136 (2.3%) 123 (2.2%) 121 (2.2%) 124 (2.3%) 132 (2.6%) 147 (2.7%) 163 (3.1%) 152 (3.0%) Total births 5,756 (100%) 5,655 (100%) 5,432 (100%) 5,361 (100%) 5,133 (100%) 5,384 (100%) 5,316 (100%) 5,065 (100%) Total Fertility rate 1.542 1.519 1.443 1.432 1.359 1.371 1.352 Birth rate 9.2 9.1 8.7 8.6 8.2 8.3 8.2 Dialect Linguists have identified speech patterns found among Vermonters as belonging to Western New England English , a dialect of New England English , which features full pronunciation of all r sounds , pronouncing horse and hoarse the same , an\n\n\nKEPT DOCUMENTS (context only):\n[3 documents already kept as relevant]\n[KEPT 1] , both which are in Latin. Kentucky , North Dakota , and Vermont also have two mottos, one in Latin and the other in English. All other states and territories have only one motto, except for Guam and the Northern Mariana Islands , which do not have any mottos. English and Latin are the most-used lan...\n[KEPT 2] theast coast of the state. The term Mainiac is considered by some to be derogatory, but is embraced with pride by others, and is used for a variety of organizations and for events such as the YMCA Mainiac Sprint Triathlon & Duathlon. See also Index of Maine-related articles Outline of Maine Maine po...\n[KEPT 3] % Alaska 72,524 91 10 101 0.139% Arizona 499,261 1,613 1,613 0.323% Arkansas 1,949,387 3,814 3,814 0.195% California 6,907,397 17,022 17,022 0.246% Colorado 1,123,296 2,697 2,697 0.240% Connecticut 1,709,242 4,347 4,347 0.254% Delaware 246,505 579 579 0.234% District of Columbia 663,091 3,029 3,029 ...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 6 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2034, + "osl": 1795, + "total_tokens": 3829, + "latency_ms": 17155.94, + "tokens_per_second": 104.63 + }, + "context": { + "num_documents_evaluated": 6, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 3 + } + }, + { + "call_id": "b39e1ae3-d795-45a8-bc77-7ac8c3217504", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:31:49.932285Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: The state, whose motto was adopted March 26, 1928, has 0.94% of the population in 2024 speaking a language that is native to which country?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] , both which are in Latin. Kentucky , North Dakota , and Vermont also have two mottos, one in Latin and the other in English. All other states and territories have only one motto, except for Guam and the Northern Mariana Islands , which do not have any mottos. English and Latin are the most-used languages for state mottos, each used by 25 states and territories. Seven states and territories use another language, of which each language is only used once. Eight states and two territories have their mottos on their state quarter ; thirty-eight states and four territories have their mottos on their state seals. The dates given are, where possible, the earliest date that the motto was used in an official sense. Some state mottos are not official but are on the of\n\n[DOC 2] theast coast of the state. The term Mainiac is considered by some to be derogatory, but is embraced with pride by others, and is used for a variety of organizations and for events such as the YMCA Mainiac Sprint Triathlon & Duathlon. See also Index of Maine-related articles Outline of Maine Maine portal References Notes ^ In the event of a vacancy in the office of governor, the president of the State Senate is first in line to become governor. ^ Maine is the U. S. state with the highest percentage of French-speaking population . ^ Elevation adjusted to North American Vertical Datum of 1988 Citations ^ ^ ^ a b ^ a b ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ MPBN, \"Rolling Back the Frontier\" Archived July 4, 2011, at the Wayback Machine , The Story of Maine ; ac\n\n[DOC 3] % Alaska 72,524 91 10 101 0.139% Arizona 499,261 1,613 1,613 0.323% Arkansas 1,949,387 3,814 3,814 0.195% California 6,907,397 17,022 17,022 0.246% Colorado 1,123,296 2,697 2,697 0.240% Connecticut 1,709,242 4,347 4,347 0.254% Delaware 246,505 579 579 0.234% District of Columbia 663,091 3,029 3,029 0.456% Florida 1,897,414 3,540 3,540 0.186% Georgia 3,123,723 5,701 5,701 0.182% Hawaii 422,770 689 68 757 0.179% Idaho 524,873 1,419 1,419 0.270% Illinois 7,897,241 18,601 18,601 0.235% Indiana 3,427,796 8,131 8,131 0.237% Iowa 2,538,268 5,633 5,633 0.221% Kansas 1,801,028 4,526 4,526 0.251% Kentucky 2,845,627 6,802 6,802 0.239% Louisiana 2,363,516 3,964 3,964 0.167% Maine 847,226 2,156 2,156 0.254% Maryland 1,821,244 4,375 4,375 0.240% Massachusetts 4,316,721 10\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The documents mention that Maine has the highest percentage of French‑speaking residents (language native to France) and provide various language percentages for states, but they do not identify which state's motto was adopted on March 26, 1928, nor do they show a 0.94% language‑speaker figure for any state. Without a clear link between the adoption date, the 0.94% figure, and the specific state, the answer cannot be determined.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1046, + "osl": 1202, + "total_tokens": 2248, + "latency_ms": 34240.36, + "tokens_per_second": 35.1 + }, + "context": { + "kept_docs_count": 3, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "12bb87ba-1a59-44da-abfe-5236d49352aa", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:31:55.975114Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: The state, whose motto was adopted March 26, 1928, has 0.94% of the population in 2024 speaking a language that is native to which country?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] , both which are in Latin. Kentucky , North Dakota , and Vermont also have two mottos, one in Latin and the other in English. All other states and territories have only one motto, except for Guam and the Northern Mariana Islands , which do not have any mottos. English and Latin are the most-used languages for state mottos, each used by 25 states and territories. Seven states and territories use another language, of which each language is only used once. Eight states and two territories have their mottos on their state quarter ; thirty-eight states and four territories have their mottos on their state seals. The dates given are, where possible, the earliest date that the motto was used in an official sense. Some state mottos are not official but are on the of\n\n[DOC 2] theast coast of the state. The term Mainiac is considered by some to be derogatory, but is embraced with pride by others, and is used for a variety of organizations and for events such as the YMCA Mainiac Sprint Triathlon & Duathlon. See also Index of Maine-related articles Outline of Maine Maine portal References Notes ^ In the event of a vacancy in the office of governor, the president of the State Senate is first in line to become governor. ^ Maine is the U. S. state with the highest percentage of French-speaking population . ^ Elevation adjusted to North American Vertical Datum of 1988 Citations ^ ^ ^ a b ^ a b ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ MPBN, \"Rolling Back the Frontier\" Archived July 4, 2011, at the Wayback Machine , The Story of Maine ; ac\n\n[DOC 3] % Alaska 72,524 91 10 101 0.139% Arizona 499,261 1,613 1,613 0.323% Arkansas 1,949,387 3,814 3,814 0.195% California 6,907,397 17,022 17,022 0.246% Colorado 1,123,296 2,697 2,697 0.240% Connecticut 1,709,242 4,347 4,347 0.254% Delaware 246,505 579 579 0.234% District of Columbia 663,091 3,029 3,029 0.456% Florida 1,897,414 3,540 3,540 0.186% Georgia 3,123,723 5,701 5,701 0.182% Hawaii 422,770 689 68 757 0.179% Idaho 524,873 1,419 1,419 0.270% Illinois 7,897,241 18,601 18,601 0.235% Indiana 3,427,796 8,131 8,131 0.237% Iowa 2,538,268 5,633 5,633 0.221% Kansas 1,801,028 4,526 4,526 0.251% Kentucky 2,845,627 6,802 6,802 0.239% Louisiana 2,363,516 3,964 3,964 0.167% Maine 847,226 2,156 2,156 0.254% Maryland 1,821,244 4,375 4,375 0.240% Massachusetts 4,316,721 10\n\n\nSEARCH HISTORY: - Query: 'The state, whose motto was adopted March 26, 1928, has 0.94% of the population in 2024 speaking a language that is native to which country?' → 7 docs\n- Query: 'state motto March 26 1928' → 0 docs\n- Query: '2024 language spoken percentages by state' → 3 docs\n- Query: 'language native country list' → 5 docs\n- Query: 'March 26, 1928 state motto' → 1 docs\n- Query: '2024 language spoken at home by U.S. state percentages' → 1 docs\n- Query: '\"0.94%\" language spoken United States state 2024' → 4 docs\n\nFEEDBACK: Error: Expecting ':' delimiter: line 8 column 1 (char 351)\nFirst query identifies the state whose motto was adopted on that date. Second query provides a table of language percentages for each state in 2024, allowing us to locate the 0.94% figure and the corresponding language. Third query determines the country where that language originates.\nWe need to locate the state whose official motto was adopted on March 26 1928, then find the 2024 language‑usage table that shows a 0.94 % share for a specific language in that state, and finally identify the country where that language originates.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"state motto March 26 1928\",\n \"2024 language spoken by state percentages\",\n \"language native country\"\n ],\n \"feedback\": \"First query aims to identify the state whose official motto was adopted on March 26 1928. Second query seeks a table of 2024 language‑at‑home percentages by U.S. state to locate the 0.94 % figure and the associated language. Third query will be used (once the language is known) to determine the country where that language originates.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1508, + "osl": 751, + "total_tokens": 2259, + "latency_ms": 6042.14, + "tokens_per_second": 124.29 + }, + "context": { + "kept_docs_count": 3, + "iteration_state": "generating_queries", + "query_history_length": 7 + } + }, + { + "call_id": "755437e5-acd5-4cb1-9c38-f02dd5121648", + "component": "evaluate_document_relevance", + "hop_count": 5, + "timestamp": "2026-05-16T03:32:05.224754Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: The state, whose motto was adopted March 26, 1928, has 0.94% of the population in 2024 speaking a language that is native to which country?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Sint Eustatius , Papiamentu is an official language in the special municipality of Bonaire , and West Frisian is an official language in the province of Friesland . ^ a b Amsterdam is the constitutional capital, while the government and the royal family are seated in The Hague. ^ Having ratified the European Charter for Regional or Minority Languages in 1996, the Dutch government recognises Dutch Low Saxon, Limburgish, Sinte Romani and Yiddish as regional or non-territorial minority languages. On 1 July 2021, Dutch Sign Language received the status of recognised language. ^ This includes residents who were born in the Netherlands and whose parents both were born in the Netherlands. ^ The area of the Netherlands with overseas territories. European Netherland\n\n[NEW 2] this table, ( † ) means a language is presumed extinct, and (*) means it is possibly extinct, but lacks conclusive supporting evidence. Name Other names Language Location Population census/estimated Year Aikanã Massacá, Tubarão, Columbiara, Mundé, Mondé, Huari, Aikaná Aikanã Rondônia 350 2014 Aikewara Akewara, Akewere, Suruí, Sororos Suruí do Pará Pará 470 2020 Akuntsu Akunt'su Akuntsu Rondônia 3 2022 Amanayé Amanaié, Amanyé, Araradeua Amanayé * Pará 174 2017 Amondawa Amondaua, Amundava, Amundawa, Uru-Eu-Wau-Wau, Mbo'uima'ga, Envuga Southern Kagwahiva ( Amondawa variety) Acre , Rondônia 129 2020 Anacé Ceará 2,018 2014 Anambé Anambé * Pará 182 2020 Anapuru Muypurá Maranhão 150 2021 Aparai Apalai, Apalaí, Apalay, Appirois, Aparathy, Apareilles, Aparai Aparai F\n\n[NEW 3] d in South Africa . Tagalog ( / t ə ˈ ɡ ɑː l ɒ ɡ / tə-GAH -log , native pronunciation: [tɐˈɡaːloɡ] i ; Baybayin : ᜆᜄᜎᜓᜄ᜔ ) is an Austronesian language spoken as a first language by the ethnic Tagalog people , who make up a quarter of the population of the Philippines , and as a second language by the majority. Its standardized and codified form, Filipino , is the national language of the Philippines, and is one of the nation's two official languages , alongside English . Tagalog is closely related to other Philippine languages , such as the Bikol languages , the Bisaya languages , Ilocano , Kapampangan , and Pangasinan , and more distantly to other Austronesian languages, such as the Formosan languages of Taiwan , Indonesian , Malay , Hawaiian , Māori , Mala\n\n[NEW 4] Manipuri Marathi Nepali Odia Punjabi Sanskrit Santali Sindhi Tamil Telugu Urdu State level Kokborok Lepcha Mizo Sikkimese all the 8th scheduled languages – except Sindhi , Kashmiri and Dogri Native languages 424 languages Religion (2011) 79.8% Hinduism 14.2% Islam 2.3% Christianity 1.7% Sikhism 0.7% Buddhism 0.4% Jainism 0.23% unaffiliated 0.65% other Demonyms Indian others Government Federal parliamentary republic • President Droupadi Murmu • Vice President C. P. Radhakrishnan • Prime Minister Narendra Modi Legislature Parliament • Upper house Rajya Sabha • Lower house Lok Sabha Independence from the United Kingdom • Dominion 15 August 1947 • Republic 26 January 1950 Area • Total 3,287,263 km 2 (1,269,219 sq mi) ( 7th ) • Water (%) 9.6 Population • 2023 es\n\n[NEW 5] n , Serbian , and Slovak . The following minority languages are also recognised: Albanian , Bosnian , Bulgarian , German, Hebrew , Macedonian , Montenegrin , Polish , Romanian , Istro-Romanian , Romani , Russian, Rusyn , Slovene , Turkish , and Ukrainian . According to the 2021 Census, 95.25% of citizens declared Croatian as their native language, 1.16% declared Serbian as their native language, while no other language reaches 0.26%. Croatian is a member of the South Slavic languages and is written using the Latin alphabet . There are three major dialects spoken on the territory of Croatia, with standard Croatian based on the Shtokavian dialect. The Chakavian and Kajkavian dialects are distinguished from Shtokavian by their lexicon , phonology and syntax . A\n\n\nKEPT DOCUMENTS (context only):\n[3 documents already kept as relevant]\n[KEPT 1] , both which are in Latin. Kentucky , North Dakota , and Vermont also have two mottos, one in Latin and the other in English. All other states and territories have only one motto, except for Guam and the Northern Mariana Islands , which do not have any mottos. English and Latin are the most-used lan...\n[KEPT 2] theast coast of the state. The term Mainiac is considered by some to be derogatory, but is embraced with pride by others, and is used for a variety of organizations and for events such as the YMCA Mainiac Sprint Triathlon & Duathlon. See also Index of Maine-related articles Outline of Maine Maine po...\n[KEPT 3] % Alaska 72,524 91 10 101 0.139% Arizona 499,261 1,613 1,613 0.323% Arkansas 1,949,387 3,814 3,814 0.195% California 6,907,397 17,022 17,022 0.246% Colorado 1,123,296 2,697 2,697 0.240% Connecticut 1,709,242 4,347 4,347 0.254% Delaware 246,505 579 579 0.234% District of Columbia 663,091 3,029 3,029 ...\n\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 5 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\": [0, 0, 0, 0, 0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1617, + "osl": 1918, + "total_tokens": 3535, + "latency_ms": 8543.54, + "tokens_per_second": 224.5 + }, + "context": { + "num_documents_evaluated": 5, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 3 + } + }, + { + "call_id": "d682bf43-1ce3-474e-b81d-ab5a768b4b06", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:32:17.771093Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: The state, whose motto was adopted March 26, 1928, has 0.94% of the population in 2024 speaking a language that is native to which country?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] , both which are in Latin. Kentucky , North Dakota , and Vermont also have two mottos, one in Latin and the other in English. All other states and territories have only one motto, except for Guam and the Northern Mariana Islands , which do not have any mottos. English and Latin are the most-used languages for state mottos, each used by 25 states and territories. Seven states and territories use another language, of which each language is only used once. Eight states and two territories have their mottos on their state quarter ; thirty-eight states and four territories have their mottos on their state seals. The dates given are, where possible, the earliest date that the motto was used in an official sense. Some state mottos are not official but are on the of\n\n[DOC 2] theast coast of the state. The term Mainiac is considered by some to be derogatory, but is embraced with pride by others, and is used for a variety of organizations and for events such as the YMCA Mainiac Sprint Triathlon & Duathlon. See also Index of Maine-related articles Outline of Maine Maine portal References Notes ^ In the event of a vacancy in the office of governor, the president of the State Senate is first in line to become governor. ^ Maine is the U. S. state with the highest percentage of French-speaking population . ^ Elevation adjusted to North American Vertical Datum of 1988 Citations ^ ^ ^ a b ^ a b ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ MPBN, \"Rolling Back the Frontier\" Archived July 4, 2011, at the Wayback Machine , The Story of Maine ; ac\n\n[DOC 3] % Alaska 72,524 91 10 101 0.139% Arizona 499,261 1,613 1,613 0.323% Arkansas 1,949,387 3,814 3,814 0.195% California 6,907,397 17,022 17,022 0.246% Colorado 1,123,296 2,697 2,697 0.240% Connecticut 1,709,242 4,347 4,347 0.254% Delaware 246,505 579 579 0.234% District of Columbia 663,091 3,029 3,029 0.456% Florida 1,897,414 3,540 3,540 0.186% Georgia 3,123,723 5,701 5,701 0.182% Hawaii 422,770 689 68 757 0.179% Idaho 524,873 1,419 1,419 0.270% Illinois 7,897,241 18,601 18,601 0.235% Indiana 3,427,796 8,131 8,131 0.237% Iowa 2,538,268 5,633 5,633 0.221% Kansas 1,801,028 4,526 4,526 0.251% Kentucky 2,845,627 6,802 6,802 0.239% Louisiana 2,363,516 3,964 3,964 0.167% Maine 847,226 2,156 2,156 0.254% Maryland 1,821,244 4,375 4,375 0.240% Massachusetts 4,316,721 10\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents do not identify which state adopted its motto on March 26 1928, nor do they provide a 0.94% language‑speaker figure for that state linked to a specific country. Without the state name and the language‑country association, the question cannot be answered.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1061, + "osl": 875, + "total_tokens": 1936, + "latency_ms": 12545.5, + "tokens_per_second": 69.75 + }, + "context": { + "kept_docs_count": 3, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "27eef862-d59e-413e-8a50-7fa7139b41e4", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:32:27.040327Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: The state, whose motto was adopted March 26, 1928, has 0.94% of the population in 2024 speaking a language that is native to which country?\n\nDOCUMENTS:\n\n[DOC 1] , both which are in Latin. Kentucky , North Dakota , and Vermont also have two mottos, one in Latin and the other in English. All other states and territories have only one motto, except for Guam and the Northern Mariana Islands , which do not have any mottos. English and Latin are the most-used languages for state mottos, each used by 25 states and territories. Seven states and territories use another language, of which each language is only used once. Eight states and two territories have their mottos on their state quarter ; thirty-eight states and four territories have their mottos on their state seals. The dates given are, where possible, the earliest date that the motto was used in an official sense. Some state mottos are not official but are on the of\n\n[DOC 2] theast coast of the state. The term Mainiac is considered by some to be derogatory, but is embraced with pride by others, and is used for a variety of organizations and for events such as the YMCA Mainiac Sprint Triathlon & Duathlon. See also Index of Maine-related articles Outline of Maine Maine portal References Notes ^ In the event of a vacancy in the office of governor, the president of the State Senate is first in line to become governor. ^ Maine is the U. S. state with the highest percentage of French-speaking population . ^ Elevation adjusted to North American Vertical Datum of 1988 Citations ^ ^ ^ a b ^ a b ^ ^ ^ a b ^ ^ ^ ^ ^ ^ ^ ^ a b ^ ^ ^ ^ ^ MPBN, \"Rolling Back the Frontier\" Archived July 4, 2011, at the Wayback Machine , The Story of Maine ; ac\n\n[DOC 3] % Alaska 72,524 91 10 101 0.139% Arizona 499,261 1,613 1,613 0.323% Arkansas 1,949,387 3,814 3,814 0.195% California 6,907,397 17,022 17,022 0.246% Colorado 1,123,296 2,697 2,697 0.240% Connecticut 1,709,242 4,347 4,347 0.254% Delaware 246,505 579 579 0.234% District of Columbia 663,091 3,029 3,029 0.456% Florida 1,897,414 3,540 3,540 0.186% Georgia 3,123,723 5,701 5,701 0.182% Hawaii 422,770 689 68 757 0.179% Idaho 524,873 1,419 1,419 0.270% Illinois 7,897,241 18,601 18,601 0.235% Indiana 3,427,796 8,131 8,131 0.237% Iowa 2,538,268 5,633 5,633 0.221% Kansas 1,801,028 4,526 4,526 0.251% Kentucky 2,845,627 6,802 6,802 0.239% Louisiana 2,363,516 3,964 3,964 0.167% Maine 847,226 2,156 2,156 0.254% Maryland 1,821,244 4,375 4,375 0.240% Massachusetts 4,316,721 10\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "France", + "finish_reason": "stop" + }, + "metrics": { + "isl": 913, + "osl": 1225, + "total_tokens": 2138, + "latency_ms": 9268.57, + "tokens_per_second": 132.17 + }, + "context": { + "num_documents_used": 3, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:32:27.041206Z", + "summary": { + "total_llm_calls": 13, + "total_hops": 5, + "total_input_tokens": 16402, + "total_output_tokens": 16415, + "total_tokens": 32817, + "total_latency_ms": 206647.74, + "average_tokens_per_second": 122.26, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/List_of_U.S._state_and_territory_mottos", + "https://en.wikipedia.org/wiki/New_Jersey", + "https://en.wikipedia.org/wiki/Tagalog_language" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "France", + "ground_truth_answer": "Philippines" + } + }, + { + "query_id": "49", + "original_query": "As of 2024, at the time of his birth, what was the middle name of the U.S. president who won Alaska, graduated from Yale University, and had a son named Michael?", + "timestamp_start": "2026-05-16T03:32:27.082281Z", + "llm_calls": [ + { + "call_id": "7b849bd9-683d-4ed5-b054-01b5bc6b96c1", + "component": "generate_search_queries", + "hop_count": 1, + "timestamp": "2026-05-16T03:32:41.213016Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of 2024, at the time of his birth, what was the middle name of the U.S. president who won Alaska, graduated from Yale University, and had a son named Michael?\n\nKEPT DOCUMENTS (already marked relevant):\nNone\n\nSEARCH HISTORY: No queries yet\nFEEDBACK: Iteration 1 - Initial search\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Gerald Ford Yale\",\n \"Gerald Ford son Michael\",\n \"Gerald Ford birth name\"\n ],\n \"feedback\": \"Need to identify the president who fits the clues (won Alaska, Yale graduate, son Michael) and then find his middle name at birth.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 415, + "osl": 65, + "total_tokens": 480, + "latency_ms": 14130.58, + "tokens_per_second": 4.6 + }, + "context": { + "kept_docs_count": 0, + "iteration_state": "generating_queries", + "query_history_length": 0 + } + }, + { + "call_id": "3fb9829d-6b05-41b1-a248-a6407ed31788", + "component": "evaluate_document_relevance", + "hop_count": 2, + "timestamp": "2026-05-16T03:33:33.115535Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You are a document relevance classifier. Evaluate if documents are relevant to answering a question.\n\nQUESTION: As of 2024, at the time of his birth, what was the middle name of the U.S. president who won Alaska, graduated from Yale University, and had a son named Michael?\n\nNEW DOCUMENTS TO EVALUATE:\n\n[NEW 1] Gerald Ford in 2006 Same calendar year 1826: Thomas Jefferson and John Adams , both on July 4 1862: John Tyler and Martin Van Buren , on January 18 and July 24, respectively 1901: Benjamin Harrison and William McKinley , on March 13 and September 14, respectively Same age (rounded down to nearest year) 93: Gerald Ford and Ronald Reagan 90: John Adams and Herbert Hoover 78: Andrew Jackson and Dwight D. Eisenhower 71: John Tyler and Grover Cleveland 67: George Washington , Benjamin Harrison , and Woodrow Wilson 64: Franklin Pierce and Lyndon B. Johnson 63: Ulysses S. Grant and Franklin D. Roosevelt 60: Theodore Roosevelt and Calvin Coolidge 57: Chester A. Arthur and Warren G. Harding Died before multiple predecessors William Henry Harrison (L), Abraham Lincoln\n\n[NEW 2] aring-in ceremony Recorded August 9, 1974 Gerald Rudolph Ford Jr. (born Leslie Lynch King Jr. ; July 14, 1913 – December 26, 2006) was the 38th president of the United States , serving from 1974 to 1977. A member of the Republican Party , Ford assumed the presidency after the resignation of Richard Nixon , under whom he had served as the 40th vice president from 1973 to 1974 following the resignation of Spiro Agnew . Prior to that, he served as a member of the U. S. House of Representatives from 1949 to 1973. Ford was born in Omaha, Nebraska , and raised in Grand Rapids, Michigan . He attended the University of Michigan , where he played for the university football team , before eventually attending Yale Law School . Afterward, he served in the U. S. Naval R\n\n[NEW 3] ward Taft Yale University Member of the Yale Corporation 1901–1913 Hampton University Board of Trustees 1909–1930 Warren G. Harding American University Board of Trustees 1921–1923 Calvin Coolidge Amherst College Board of Trustees (life member) 1921–1933 Herbert Hoover Stanford University Board of Trustees 1923–1960 American University Board of Trustees 1945–1950 Franklin D. Roosevelt Harvard University Board of Overseers 1917–1923 Vassar College Board of Trustees 1923–1945 Dwight D. Eisenhower Eisenhower College Namesake, fundraiser 1965–1969 John F. Kennedy Harvard University Board of Overseers 1957–1958 Jimmy Carter Mercer University Board of Trustees 2012–2024 Ronald Reagan Eureka College Board of Trustees 1947–1953, 1967–1973, 1974–1980 See also List of\n\n[NEW 4] Lavelli , Miye Oni and Danny Wolf , football players Calvin Hill , Gary Fenick , Amos Alonzo Stagg , and \"the Father of American Football\" Walter Camp ; ice hockey players Chris Higgins and Olympian Helen Resor ; Olympic figure skating champions Sarah Hughes and Nathan Chen ; nine-time U. S. Squash men's champion Julian Illingworth ; Olympic swimmer Don Schollander ; Olympic rowers Josh West and Rusty Wailes ; Olympic sailor Stuart McNay ; Olympic runner Frank Shorter ; and others. Notable Yale alumni include: 27th President of the United States and Chief Justice William Howard Taft (BA, 1878) 38th President of the United States Gerald Ford (LLB, 1941) 41st President of the United States George H. W. Bush (BA, 1948) 43rd President of the United States Georg\n\n\nKEPT DOCUMENTS (context only):\nNone\n\nTASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant.\n\nBe GENEROUS in marking relevance:\n- Mark 1 if document mentions any entity/person/place/event in the question\n- Mark 1 if document has related information that could help answer\n- Mark 1 if document has facts that could be connected with other docs\n- Only mark 0 if truly unrelated content\n\nOUTPUT FORMAT (JSON only):\n{\"relevance\": [1, 0, 1, 0, 1]}\n\nThe array MUST have exactly 4 elements (one per NEW document).\n" + } + ], + "model": "openai/gpt-oss-20b", + "temperature": 1, + "max_tokens": 4096, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\"relevance\":[1,1,1,0]}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1045, + "osl": 2153, + "total_tokens": 3198, + "latency_ms": 51344.33, + "tokens_per_second": 41.93 + }, + "context": { + "num_documents_evaluated": 4, + "iteration_state": "evaluating_relevance", + "kept_docs_before": 0 + } + }, + { + "call_id": "b620654e-79bd-485a-8db5-abff85b58cb7", + "component": "check_sufficiency", + "hop_count": 2, + "timestamp": "2026-05-16T03:34:08.888068Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of 2024, at the time of his birth, what was the middle name of the U.S. president who won Alaska, graduated from Yale University, and had a son named Michael?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Gerald Ford in 2006 Same calendar year 1826: Thomas Jefferson and John Adams , both on July 4 1862: John Tyler and Martin Van Buren , on January 18 and July 24, respectively 1901: Benjamin Harrison and William McKinley , on March 13 and September 14, respectively Same age (rounded down to nearest year) 93: Gerald Ford and Ronald Reagan 90: John Adams and Herbert Hoover 78: Andrew Jackson and Dwight D. Eisenhower 71: John Tyler and Grover Cleveland 67: George Washington , Benjamin Harrison , and Woodrow Wilson 64: Franklin Pierce and Lyndon B. Johnson 63: Ulysses S. Grant and Franklin D. Roosevelt 60: Theodore Roosevelt and Calvin Coolidge 57: Chester A. Arthur and Warren G. Harding Died before multiple predecessors William Henry Harrison (L), Abraham Lincoln\n\n[DOC 2] aring-in ceremony Recorded August 9, 1974 Gerald Rudolph Ford Jr. (born Leslie Lynch King Jr. ; July 14, 1913 – December 26, 2006) was the 38th president of the United States , serving from 1974 to 1977. A member of the Republican Party , Ford assumed the presidency after the resignation of Richard Nixon , under whom he had served as the 40th vice president from 1973 to 1974 following the resignation of Spiro Agnew . Prior to that, he served as a member of the U. S. House of Representatives from 1949 to 1973. Ford was born in Omaha, Nebraska , and raised in Grand Rapids, Michigan . He attended the University of Michigan , where he played for the university football team , before eventually attending Yale Law School . Afterward, he served in the U. S. Naval R\n\n[DOC 3] ward Taft Yale University Member of the Yale Corporation 1901–1913 Hampton University Board of Trustees 1909–1930 Warren G. Harding American University Board of Trustees 1921–1923 Calvin Coolidge Amherst College Board of Trustees (life member) 1921–1933 Herbert Hoover Stanford University Board of Trustees 1923–1960 American University Board of Trustees 1945–1950 Franklin D. Roosevelt Harvard University Board of Overseers 1917–1923 Vassar College Board of Trustees 1923–1945 Dwight D. Eisenhower Eisenhower College Namesake, fundraiser 1965–1969 John F. Kennedy Harvard University Board of Overseers 1957–1958 Jimmy Carter Mercer University Board of Trustees 2012–2024 Ronald Reagan Eureka College Board of Trustees 1947–1953, 1967–1973, 1974–1980 See also List of\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 2 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents mention Gerald Ford, Yale affiliations, and various presidents, but none provide information linking a president who 'won Alaska', graduated from Yale, and had a son named Michael. Crucially, there is no mention of George H. W. Bush (the likely candidate) or his middle name at birth. Therefore the required facts are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 880, + "osl": 1135, + "total_tokens": 2015, + "latency_ms": 35771.7, + "tokens_per_second": 31.73 + }, + "context": { + "kept_docs_count": 3, + "iteration": 2, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "93506411-9750-4a1e-9db9-8e143d0333c8", + "component": "generate_search_queries", + "hop_count": 2, + "timestamp": "2026-05-16T03:34:56.010497Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of 2024, at the time of his birth, what was the middle name of the U.S. president who won Alaska, graduated from Yale University, and had a son named Michael?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Gerald Ford in 2006 Same calendar year 1826: Thomas Jefferson and John Adams , both on July 4 1862: John Tyler and Martin Van Buren , on January 18 and July 24, respectively 1901: Benjamin Harrison and William McKinley , on March 13 and September 14, respectively Same age (rounded down to nearest year) 93: Gerald Ford and Ronald Reagan 90: John Adams and Herbert Hoover 78: Andrew Jackson and Dwight D. Eisenhower 71: John Tyler and Grover Cleveland 67: George Washington , Benjamin Harrison , and Woodrow Wilson 64: Franklin Pierce and Lyndon B. Johnson 63: Ulysses S. Grant and Franklin D. Roosevelt 60: Theodore Roosevelt and Calvin Coolidge 57: Chester A. Arthur and Warren G. Harding Died before multiple predecessors William Henry Harrison (L), Abraham Lincoln\n\n[DOC 2] aring-in ceremony Recorded August 9, 1974 Gerald Rudolph Ford Jr. (born Leslie Lynch King Jr. ; July 14, 1913 – December 26, 2006) was the 38th president of the United States , serving from 1974 to 1977. A member of the Republican Party , Ford assumed the presidency after the resignation of Richard Nixon , under whom he had served as the 40th vice president from 1973 to 1974 following the resignation of Spiro Agnew . Prior to that, he served as a member of the U. S. House of Representatives from 1949 to 1973. Ford was born in Omaha, Nebraska , and raised in Grand Rapids, Michigan . He attended the University of Michigan , where he played for the university football team , before eventually attending Yale Law School . Afterward, he served in the U. S. Naval R\n\n[DOC 3] ward Taft Yale University Member of the Yale Corporation 1901–1913 Hampton University Board of Trustees 1909–1930 Warren G. Harding American University Board of Trustees 1921–1923 Calvin Coolidge Amherst College Board of Trustees (life member) 1921–1933 Herbert Hoover Stanford University Board of Trustees 1923–1960 American University Board of Trustees 1945–1950 Franklin D. Roosevelt Harvard University Board of Overseers 1917–1923 Vassar College Board of Trustees 1923–1945 Dwight D. Eisenhower Eisenhower College Namesake, fundraiser 1965–1969 John F. Kennedy Harvard University Board of Overseers 1957–1958 Jimmy Carter Mercer University Board of Trustees 2012–2024 Ronald Reagan Eureka College Board of Trustees 1947–1953, 1967–1973, 1974–1980 See also List of\n\n\nSEARCH HISTORY: - Query: 'Gerald Ford Yale' → 4 docs\n- Query: 'Gerald Ford son Michael' → 0 docs\n- Query: 'Gerald Ford birth name' → 0 docs\n\nFEEDBACK: Need to identify the president who fits the clues (won Alaska, Yale graduate, son Michael) and then find his middle name at birth.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Leslie Lynch King Jr.\",\n \"Gerald Ford middle name\",\n \"Gerald Ford son Michael\"\n ],\n \"feedback\": \"Need to confirm the president who fits clues (won Alaska's electoral votes in 1976, Yale Law graduate, son Michael) is Gerald Ford. Queries target his birth name (which reveals middle name), his middle name directly, and his son Michael to verify family clue.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1101, + "osl": 1781, + "total_tokens": 2882, + "latency_ms": 47121.86, + "tokens_per_second": 37.8 + }, + "context": { + "kept_docs_count": 3, + "iteration_state": "generating_queries", + "query_history_length": 3 + } + }, + { + "call_id": "5292ca71-09b2-4c0b-8ee6-3e7d1ff1c8fc", + "component": "check_sufficiency", + "hop_count": 3, + "timestamp": "2026-05-16T03:36:24.243080Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of 2024, at the time of his birth, what was the middle name of the U.S. president who won Alaska, graduated from Yale University, and had a son named Michael?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Gerald Ford in 2006 Same calendar year 1826: Thomas Jefferson and John Adams , both on July 4 1862: John Tyler and Martin Van Buren , on January 18 and July 24, respectively 1901: Benjamin Harrison and William McKinley , on March 13 and September 14, respectively Same age (rounded down to nearest year) 93: Gerald Ford and Ronald Reagan 90: John Adams and Herbert Hoover 78: Andrew Jackson and Dwight D. Eisenhower 71: John Tyler and Grover Cleveland 67: George Washington , Benjamin Harrison , and Woodrow Wilson 64: Franklin Pierce and Lyndon B. Johnson 63: Ulysses S. Grant and Franklin D. Roosevelt 60: Theodore Roosevelt and Calvin Coolidge 57: Chester A. Arthur and Warren G. Harding Died before multiple predecessors William Henry Harrison (L), Abraham Lincoln\n\n[DOC 2] aring-in ceremony Recorded August 9, 1974 Gerald Rudolph Ford Jr. (born Leslie Lynch King Jr. ; July 14, 1913 – December 26, 2006) was the 38th president of the United States , serving from 1974 to 1977. A member of the Republican Party , Ford assumed the presidency after the resignation of Richard Nixon , under whom he had served as the 40th vice president from 1973 to 1974 following the resignation of Spiro Agnew . Prior to that, he served as a member of the U. S. House of Representatives from 1949 to 1973. Ford was born in Omaha, Nebraska , and raised in Grand Rapids, Michigan . He attended the University of Michigan , where he played for the university football team , before eventually attending Yale Law School . Afterward, he served in the U. S. Naval R\n\n[DOC 3] ward Taft Yale University Member of the Yale Corporation 1901–1913 Hampton University Board of Trustees 1909–1930 Warren G. Harding American University Board of Trustees 1921–1923 Calvin Coolidge Amherst College Board of Trustees (life member) 1921–1933 Herbert Hoover Stanford University Board of Trustees 1923–1960 American University Board of Trustees 1945–1950 Franklin D. Roosevelt Harvard University Board of Overseers 1917–1923 Vassar College Board of Trustees 1923–1945 Dwight D. Eisenhower Eisenhower College Namesake, fundraiser 1965–1969 John F. Kennedy Harvard University Board of Overseers 1957–1958 Jimmy Carter Mercer University Board of Trustees 2012–2024 Ronald Reagan Eureka College Board of Trustees 1947–1953, 1967–1973, 1974–1980 See also List of\n\n[DOC 4] C Ira C. Kepford USN 16 NC (2), SS, DFC Leslie H. Kerr Jr. ~5 Robert R. Kidwell Jr. 5 DFC (4) Donald D. Kienholz USAAF 6 DFC KIFA 1973 John R. Kincaid USN 5 Robert A. Kincaid 5 NC, SS, DFC (2) Benjamin H. King USAAF 7 SS, DFC (2) Charles W. King 5 SS David L. King 5 DFC William B. King 5.5 SS William J. Kingston Jr. USN 6 DFC (3) Claiborne H. Kinnard Jr. USAAF 8 DSC James J. Kinsella USN 5 DFC (3) Claude R. Kinsey USAAF 7 DFC POW 4 April 1943, escaped captivity October 1943 Marion F. Kirby 5 SS George N. Kirk USN 7 DFC (2) Lenton F. Kirkland Jr. USAAF 5 SS MIA 25 December 1944 Floyd C. Kirkpatrick USMC 5.5 DFC (3) Phillip L. Kirkwood USN 12 NC, DFC John A. Kirla USAAF 11.5 SS George E. Kiser 9 DSC, SS Frank W. Klibbe 7 Robert H. Knapp 5 DFC (3) William M. Kn\n\n[DOC 5] Butler Carpenter de Condorcet Cornell Davis (Mike) Dewey Douglas (Kelly) Douglas (William) Du Bois Dyson Ellis Fredrickson Friedan Gilmore Hammonds Hasan Helms Hirschfeld hooks Jeffries Kahane Karenga Keller Kendi King McIntosh Merriam Meyers Mills Moore Oastler Ostrowski Painter Patterson Peled-Elhanan Perez Reich (Wilhelm) Roberts Rothstein Saini Sanger Seuss Shaw Stanley Stein Su TallBear Walia Welsing West Whedon Wilderson III Wong (Joshua) Zuboff Politicians Albanese Anderson Ardern Atatürk Attlee Bhutto Boric Brandt Butler Bryan Corbyn Cuza Đinđić Drees F. D. R. Hessel Hontiveros Hunter İmamoğlu La Follette Layton L. B. J. Lula Lyuh Manley Mandela Mottley Murayama Nehru Norton Obrador Petro Sánchez Sanders Tsai Warren Yamamoto Zapatero Organizations A\n\n[DOC 6] † N/A 1968 George Scott Bobby Knoop Brooks Robinson * † Luis Aparicio † Reggie Smith Mickey Stanley Carl Yastrzemski † Bill Freehan Jim Kaat † N/A 1969 Joe Pepitone Davey Johnson Brooks Robinson * † Mark Belanger Paul Blair Mickey Stanley Carl Yastrzemski † Bill Freehan Jim Kaat † N/A 1970 Jim Spencer Davey Johnson Brooks Robinson * † Luis Aparicio † Paul Blair Mickey Stanley Ken Berry Ray Fosse Jim Kaat † N/A 1971 George Scott Davey Johnson Brooks Robinson * † Mark Belanger Paul Blair Amos Otis Carl Yastrzemski † Ray Fosse Jim Kaat † N/A 1972 George Scott Doug Griffin Brooks Robinson * † Ed Brinkman Paul Blair Bobby Murcer Ken Berry Carlton Fisk † Jim Kaat † N/A 1973 George Scott Bobby Grich Brooks Robinson * † Mark Belanger Paul Blair Amos Otis Mickey Stan\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 3 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents provide Gerald Ford's birth name (Leslie Lynch King Jr.) confirming his middle name at birth (Lynch) and note his attendance at Yale Law School, fulfilling the Yale graduate clue. However, they do not contain information that he \\\"won Alaska\\\" in an election, nor do they mention that he had a son named Michael. Without these two required facts, the answer cannot be fully derived from the provided documents.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1614, + "osl": 1293, + "total_tokens": 2907, + "latency_ms": 9581.12, + "tokens_per_second": 134.95 + }, + "context": { + "kept_docs_count": 6, + "iteration": 3, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "eaa138af-e114-4f35-8f33-01ce69e0b725", + "component": "generate_search_queries", + "hop_count": 3, + "timestamp": "2026-05-16T03:36:30.814133Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of 2024, at the time of his birth, what was the middle name of the U.S. president who won Alaska, graduated from Yale University, and had a son named Michael?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Gerald Ford in 2006 Same calendar year 1826: Thomas Jefferson and John Adams , both on July 4 1862: John Tyler and Martin Van Buren , on January 18 and July 24, respectively 1901: Benjamin Harrison and William McKinley , on March 13 and September 14, respectively Same age (rounded down to nearest year) 93: Gerald Ford and Ronald Reagan 90: John Adams and Herbert Hoover 78: Andrew Jackson and Dwight D. Eisenhower 71: John Tyler and Grover Cleveland 67: George Washington , Benjamin Harrison , and Woodrow Wilson 64: Franklin Pierce and Lyndon B. Johnson 63: Ulysses S. Grant and Franklin D. Roosevelt 60: Theodore Roosevelt and Calvin Coolidge 57: Chester A. Arthur and Warren G. Harding Died before multiple predecessors William Henry Harrison (L), Abraham Lincoln\n\n[DOC 2] aring-in ceremony Recorded August 9, 1974 Gerald Rudolph Ford Jr. (born Leslie Lynch King Jr. ; July 14, 1913 – December 26, 2006) was the 38th president of the United States , serving from 1974 to 1977. A member of the Republican Party , Ford assumed the presidency after the resignation of Richard Nixon , under whom he had served as the 40th vice president from 1973 to 1974 following the resignation of Spiro Agnew . Prior to that, he served as a member of the U. S. House of Representatives from 1949 to 1973. Ford was born in Omaha, Nebraska , and raised in Grand Rapids, Michigan . He attended the University of Michigan , where he played for the university football team , before eventually attending Yale Law School . Afterward, he served in the U. S. Naval R\n\n[DOC 3] ward Taft Yale University Member of the Yale Corporation 1901–1913 Hampton University Board of Trustees 1909–1930 Warren G. Harding American University Board of Trustees 1921–1923 Calvin Coolidge Amherst College Board of Trustees (life member) 1921–1933 Herbert Hoover Stanford University Board of Trustees 1923–1960 American University Board of Trustees 1945–1950 Franklin D. Roosevelt Harvard University Board of Overseers 1917–1923 Vassar College Board of Trustees 1923–1945 Dwight D. Eisenhower Eisenhower College Namesake, fundraiser 1965–1969 John F. Kennedy Harvard University Board of Overseers 1957–1958 Jimmy Carter Mercer University Board of Trustees 2012–2024 Ronald Reagan Eureka College Board of Trustees 1947–1953, 1967–1973, 1974–1980 See also List of\n\n[DOC 4] C Ira C. Kepford USN 16 NC (2), SS, DFC Leslie H. Kerr Jr. ~5 Robert R. Kidwell Jr. 5 DFC (4) Donald D. Kienholz USAAF 6 DFC KIFA 1973 John R. Kincaid USN 5 Robert A. Kincaid 5 NC, SS, DFC (2) Benjamin H. King USAAF 7 SS, DFC (2) Charles W. King 5 SS David L. King 5 DFC William B. King 5.5 SS William J. Kingston Jr. USN 6 DFC (3) Claiborne H. Kinnard Jr. USAAF 8 DSC James J. Kinsella USN 5 DFC (3) Claude R. Kinsey USAAF 7 DFC POW 4 April 1943, escaped captivity October 1943 Marion F. Kirby 5 SS George N. Kirk USN 7 DFC (2) Lenton F. Kirkland Jr. USAAF 5 SS MIA 25 December 1944 Floyd C. Kirkpatrick USMC 5.5 DFC (3) Phillip L. Kirkwood USN 12 NC, DFC John A. Kirla USAAF 11.5 SS George E. Kiser 9 DSC, SS Frank W. Klibbe 7 Robert H. Knapp 5 DFC (3) William M. Kn\n\n[DOC 5] Butler Carpenter de Condorcet Cornell Davis (Mike) Dewey Douglas (Kelly) Douglas (William) Du Bois Dyson Ellis Fredrickson Friedan Gilmore Hammonds Hasan Helms Hirschfeld hooks Jeffries Kahane Karenga Keller Kendi King McIntosh Merriam Meyers Mills Moore Oastler Ostrowski Painter Patterson Peled-Elhanan Perez Reich (Wilhelm) Roberts Rothstein Saini Sanger Seuss Shaw Stanley Stein Su TallBear Walia Welsing West Whedon Wilderson III Wong (Joshua) Zuboff Politicians Albanese Anderson Ardern Atatürk Attlee Bhutto Boric Brandt Butler Bryan Corbyn Cuza Đinđić Drees F. D. R. Hessel Hontiveros Hunter İmamoğlu La Follette Layton L. B. J. Lula Lyuh Manley Mandela Mottley Murayama Nehru Norton Obrador Petro Sánchez Sanders Tsai Warren Yamamoto Zapatero Organizations A\n\n[DOC 6] † N/A 1968 George Scott Bobby Knoop Brooks Robinson * † Luis Aparicio † Reggie Smith Mickey Stanley Carl Yastrzemski † Bill Freehan Jim Kaat † N/A 1969 Joe Pepitone Davey Johnson Brooks Robinson * † Mark Belanger Paul Blair Mickey Stanley Carl Yastrzemski † Bill Freehan Jim Kaat † N/A 1970 Jim Spencer Davey Johnson Brooks Robinson * † Luis Aparicio † Paul Blair Mickey Stanley Ken Berry Ray Fosse Jim Kaat † N/A 1971 George Scott Davey Johnson Brooks Robinson * † Mark Belanger Paul Blair Amos Otis Carl Yastrzemski † Ray Fosse Jim Kaat † N/A 1972 George Scott Doug Griffin Brooks Robinson * † Ed Brinkman Paul Blair Bobby Murcer Ken Berry Carlton Fisk † Jim Kaat † N/A 1973 George Scott Bobby Grich Brooks Robinson * † Mark Belanger Paul Blair Amos Otis Mickey Stan\n\n\nSEARCH HISTORY: - Query: 'Gerald Ford Yale' → 4 docs\n- Query: 'Gerald Ford son Michael' → 0 docs\n- Query: 'Gerald Ford birth name' → 0 docs\n- Query: 'Leslie Lynch King Jr.' → 3 docs\n- Query: 'Gerald Ford middle name' → 0 docs\n- Query: 'Gerald Ford son Michael' → 0 docs\n\nFEEDBACK: Need to identify the president who fits the clues (won Alaska, Yale graduate, son Michael) and then find his middle name at birth.\nNeed to confirm the president who fits clues (won Alaska's electoral votes in 1976, Yale Law graduate, son Michael) is Gerald Ford. Queries target his birth name (which reveals middle name), his middle name directly, and his son Michael to verify family clue.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Leslie Lynch King Jr.\",\n \"Gerald Ford birth name\",\n \"Gerald Ford middle name Lynch\"\n ],\n \"feedback\": \"Need to locate the president's birth name to extract his middle name. Prior attempts on 'Gerald Ford middle name' and 'Gerald Ford birth name' returned no results, so broaden by using his full birth name and a variant that includes the middle name directly.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1913, + "osl": 93, + "total_tokens": 2006, + "latency_ms": 6570.32, + "tokens_per_second": 14.15 + }, + "context": { + "kept_docs_count": 6, + "iteration_state": "generating_queries", + "query_history_length": 6 + } + }, + { + "call_id": "adcb6e2a-d218-4118-9e00-5982dab9c76e", + "component": "check_sufficiency", + "hop_count": 4, + "timestamp": "2026-05-16T03:36:43.382207Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of 2024, at the time of his birth, what was the middle name of the U.S. president who won Alaska, graduated from Yale University, and had a son named Michael?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Gerald Ford in 2006 Same calendar year 1826: Thomas Jefferson and John Adams , both on July 4 1862: John Tyler and Martin Van Buren , on January 18 and July 24, respectively 1901: Benjamin Harrison and William McKinley , on March 13 and September 14, respectively Same age (rounded down to nearest year) 93: Gerald Ford and Ronald Reagan 90: John Adams and Herbert Hoover 78: Andrew Jackson and Dwight D. Eisenhower 71: John Tyler and Grover Cleveland 67: George Washington , Benjamin Harrison , and Woodrow Wilson 64: Franklin Pierce and Lyndon B. Johnson 63: Ulysses S. Grant and Franklin D. Roosevelt 60: Theodore Roosevelt and Calvin Coolidge 57: Chester A. Arthur and Warren G. Harding Died before multiple predecessors William Henry Harrison (L), Abraham Lincoln\n\n[DOC 2] aring-in ceremony Recorded August 9, 1974 Gerald Rudolph Ford Jr. (born Leslie Lynch King Jr. ; July 14, 1913 – December 26, 2006) was the 38th president of the United States , serving from 1974 to 1977. A member of the Republican Party , Ford assumed the presidency after the resignation of Richard Nixon , under whom he had served as the 40th vice president from 1973 to 1974 following the resignation of Spiro Agnew . Prior to that, he served as a member of the U. S. House of Representatives from 1949 to 1973. Ford was born in Omaha, Nebraska , and raised in Grand Rapids, Michigan . He attended the University of Michigan , where he played for the university football team , before eventually attending Yale Law School . Afterward, he served in the U. S. Naval R\n\n[DOC 3] ward Taft Yale University Member of the Yale Corporation 1901–1913 Hampton University Board of Trustees 1909–1930 Warren G. Harding American University Board of Trustees 1921–1923 Calvin Coolidge Amherst College Board of Trustees (life member) 1921–1933 Herbert Hoover Stanford University Board of Trustees 1923–1960 American University Board of Trustees 1945–1950 Franklin D. Roosevelt Harvard University Board of Overseers 1917–1923 Vassar College Board of Trustees 1923–1945 Dwight D. Eisenhower Eisenhower College Namesake, fundraiser 1965–1969 John F. Kennedy Harvard University Board of Overseers 1957–1958 Jimmy Carter Mercer University Board of Trustees 2012–2024 Ronald Reagan Eureka College Board of Trustees 1947–1953, 1967–1973, 1974–1980 See also List of\n\n[DOC 4] C Ira C. Kepford USN 16 NC (2), SS, DFC Leslie H. Kerr Jr. ~5 Robert R. Kidwell Jr. 5 DFC (4) Donald D. Kienholz USAAF 6 DFC KIFA 1973 John R. Kincaid USN 5 Robert A. Kincaid 5 NC, SS, DFC (2) Benjamin H. King USAAF 7 SS, DFC (2) Charles W. King 5 SS David L. King 5 DFC William B. King 5.5 SS William J. Kingston Jr. USN 6 DFC (3) Claiborne H. Kinnard Jr. USAAF 8 DSC James J. Kinsella USN 5 DFC (3) Claude R. Kinsey USAAF 7 DFC POW 4 April 1943, escaped captivity October 1943 Marion F. Kirby 5 SS George N. Kirk USN 7 DFC (2) Lenton F. Kirkland Jr. USAAF 5 SS MIA 25 December 1944 Floyd C. Kirkpatrick USMC 5.5 DFC (3) Phillip L. Kirkwood USN 12 NC, DFC John A. Kirla USAAF 11.5 SS George E. Kiser 9 DSC, SS Frank W. Klibbe 7 Robert H. Knapp 5 DFC (3) William M. Kn\n\n[DOC 5] Butler Carpenter de Condorcet Cornell Davis (Mike) Dewey Douglas (Kelly) Douglas (William) Du Bois Dyson Ellis Fredrickson Friedan Gilmore Hammonds Hasan Helms Hirschfeld hooks Jeffries Kahane Karenga Keller Kendi King McIntosh Merriam Meyers Mills Moore Oastler Ostrowski Painter Patterson Peled-Elhanan Perez Reich (Wilhelm) Roberts Rothstein Saini Sanger Seuss Shaw Stanley Stein Su TallBear Walia Welsing West Whedon Wilderson III Wong (Joshua) Zuboff Politicians Albanese Anderson Ardern Atatürk Attlee Bhutto Boric Brandt Butler Bryan Corbyn Cuza Đinđić Drees F. D. R. Hessel Hontiveros Hunter İmamoğlu La Follette Layton L. B. J. Lula Lyuh Manley Mandela Mottley Murayama Nehru Norton Obrador Petro Sánchez Sanders Tsai Warren Yamamoto Zapatero Organizations A\n\n[DOC 6] † N/A 1968 George Scott Bobby Knoop Brooks Robinson * † Luis Aparicio † Reggie Smith Mickey Stanley Carl Yastrzemski † Bill Freehan Jim Kaat † N/A 1969 Joe Pepitone Davey Johnson Brooks Robinson * † Mark Belanger Paul Blair Mickey Stanley Carl Yastrzemski † Bill Freehan Jim Kaat † N/A 1970 Jim Spencer Davey Johnson Brooks Robinson * † Luis Aparicio † Paul Blair Mickey Stanley Ken Berry Ray Fosse Jim Kaat † N/A 1971 George Scott Davey Johnson Brooks Robinson * † Mark Belanger Paul Blair Amos Otis Carl Yastrzemski † Ray Fosse Jim Kaat † N/A 1972 George Scott Doug Griffin Brooks Robinson * † Ed Brinkman Paul Blair Bobby Murcer Ken Berry Carlton Fisk † Jim Kaat † N/A 1973 George Scott Bobby Grich Brooks Robinson * † Mark Belanger Paul Blair Amos Otis Mickey Stan\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 4 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"Documents provide Gerald Ford's birth name (Leslie Lynch King Jr.) giving the middle name 'Lynch' and note his attendance at Yale Law School, satisfying the middle name and Yale graduation clues. However, none of the kept documents explicitly state that he won Alaska in an election nor that he had a son named Michael, so the required facts are missing.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1592, + "osl": 87, + "total_tokens": 1679, + "latency_ms": 11981.64, + "tokens_per_second": 7.26 + }, + "context": { + "kept_docs_count": 6, + "iteration": 4, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "f2edc91b-6931-4ad1-b75c-11956697c1ea", + "component": "generate_search_queries", + "hop_count": 4, + "timestamp": "2026-05-16T03:36:48.757447Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You generate search queries for complex multi-hop questions.\n\nQUESTION: As of 2024, at the time of his birth, what was the middle name of the U.S. president who won Alaska, graduated from Yale University, and had a son named Michael?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Gerald Ford in 2006 Same calendar year 1826: Thomas Jefferson and John Adams , both on July 4 1862: John Tyler and Martin Van Buren , on January 18 and July 24, respectively 1901: Benjamin Harrison and William McKinley , on March 13 and September 14, respectively Same age (rounded down to nearest year) 93: Gerald Ford and Ronald Reagan 90: John Adams and Herbert Hoover 78: Andrew Jackson and Dwight D. Eisenhower 71: John Tyler and Grover Cleveland 67: George Washington , Benjamin Harrison , and Woodrow Wilson 64: Franklin Pierce and Lyndon B. Johnson 63: Ulysses S. Grant and Franklin D. Roosevelt 60: Theodore Roosevelt and Calvin Coolidge 57: Chester A. Arthur and Warren G. Harding Died before multiple predecessors William Henry Harrison (L), Abraham Lincoln\n\n[DOC 2] aring-in ceremony Recorded August 9, 1974 Gerald Rudolph Ford Jr. (born Leslie Lynch King Jr. ; July 14, 1913 – December 26, 2006) was the 38th president of the United States , serving from 1974 to 1977. A member of the Republican Party , Ford assumed the presidency after the resignation of Richard Nixon , under whom he had served as the 40th vice president from 1973 to 1974 following the resignation of Spiro Agnew . Prior to that, he served as a member of the U. S. House of Representatives from 1949 to 1973. Ford was born in Omaha, Nebraska , and raised in Grand Rapids, Michigan . He attended the University of Michigan , where he played for the university football team , before eventually attending Yale Law School . Afterward, he served in the U. S. Naval R\n\n[DOC 3] ward Taft Yale University Member of the Yale Corporation 1901–1913 Hampton University Board of Trustees 1909–1930 Warren G. Harding American University Board of Trustees 1921–1923 Calvin Coolidge Amherst College Board of Trustees (life member) 1921–1933 Herbert Hoover Stanford University Board of Trustees 1923–1960 American University Board of Trustees 1945–1950 Franklin D. Roosevelt Harvard University Board of Overseers 1917–1923 Vassar College Board of Trustees 1923–1945 Dwight D. Eisenhower Eisenhower College Namesake, fundraiser 1965–1969 John F. Kennedy Harvard University Board of Overseers 1957–1958 Jimmy Carter Mercer University Board of Trustees 2012–2024 Ronald Reagan Eureka College Board of Trustees 1947–1953, 1967–1973, 1974–1980 See also List of\n\n[DOC 4] C Ira C. Kepford USN 16 NC (2), SS, DFC Leslie H. Kerr Jr. ~5 Robert R. Kidwell Jr. 5 DFC (4) Donald D. Kienholz USAAF 6 DFC KIFA 1973 John R. Kincaid USN 5 Robert A. Kincaid 5 NC, SS, DFC (2) Benjamin H. King USAAF 7 SS, DFC (2) Charles W. King 5 SS David L. King 5 DFC William B. King 5.5 SS William J. Kingston Jr. USN 6 DFC (3) Claiborne H. Kinnard Jr. USAAF 8 DSC James J. Kinsella USN 5 DFC (3) Claude R. Kinsey USAAF 7 DFC POW 4 April 1943, escaped captivity October 1943 Marion F. Kirby 5 SS George N. Kirk USN 7 DFC (2) Lenton F. Kirkland Jr. USAAF 5 SS MIA 25 December 1944 Floyd C. Kirkpatrick USMC 5.5 DFC (3) Phillip L. Kirkwood USN 12 NC, DFC John A. Kirla USAAF 11.5 SS George E. Kiser 9 DSC, SS Frank W. Klibbe 7 Robert H. Knapp 5 DFC (3) William M. Kn\n\n[DOC 5] Butler Carpenter de Condorcet Cornell Davis (Mike) Dewey Douglas (Kelly) Douglas (William) Du Bois Dyson Ellis Fredrickson Friedan Gilmore Hammonds Hasan Helms Hirschfeld hooks Jeffries Kahane Karenga Keller Kendi King McIntosh Merriam Meyers Mills Moore Oastler Ostrowski Painter Patterson Peled-Elhanan Perez Reich (Wilhelm) Roberts Rothstein Saini Sanger Seuss Shaw Stanley Stein Su TallBear Walia Welsing West Whedon Wilderson III Wong (Joshua) Zuboff Politicians Albanese Anderson Ardern Atatürk Attlee Bhutto Boric Brandt Butler Bryan Corbyn Cuza Đinđić Drees F. D. R. Hessel Hontiveros Hunter İmamoğlu La Follette Layton L. B. J. Lula Lyuh Manley Mandela Mottley Murayama Nehru Norton Obrador Petro Sánchez Sanders Tsai Warren Yamamoto Zapatero Organizations A\n\n[DOC 6] † N/A 1968 George Scott Bobby Knoop Brooks Robinson * † Luis Aparicio † Reggie Smith Mickey Stanley Carl Yastrzemski † Bill Freehan Jim Kaat † N/A 1969 Joe Pepitone Davey Johnson Brooks Robinson * † Mark Belanger Paul Blair Mickey Stanley Carl Yastrzemski † Bill Freehan Jim Kaat † N/A 1970 Jim Spencer Davey Johnson Brooks Robinson * † Luis Aparicio † Paul Blair Mickey Stanley Ken Berry Ray Fosse Jim Kaat † N/A 1971 George Scott Davey Johnson Brooks Robinson * † Mark Belanger Paul Blair Amos Otis Carl Yastrzemski † Ray Fosse Jim Kaat † N/A 1972 George Scott Doug Griffin Brooks Robinson * † Ed Brinkman Paul Blair Bobby Murcer Ken Berry Carlton Fisk † Jim Kaat † N/A 1973 George Scott Bobby Grich Brooks Robinson * † Mark Belanger Paul Blair Amos Otis Mickey Stan\n\n\nSEARCH HISTORY: - Query: 'Gerald Ford Yale' → 4 docs\n- Query: 'Gerald Ford son Michael' → 0 docs\n- Query: 'Gerald Ford birth name' → 0 docs\n- Query: 'Leslie Lynch King Jr.' → 3 docs\n- Query: 'Gerald Ford middle name' → 0 docs\n- Query: 'Gerald Ford son Michael' → 0 docs\n- Query: 'Leslie Lynch King Jr.' → 0 docs\n- Query: 'Gerald Ford birth name' → 0 docs\n- Query: 'Gerald Ford middle name Lynch' → 0 docs\n\nFEEDBACK: Need to identify the president who fits the clues (won Alaska, Yale graduate, son Michael) and then find his middle name at birth.\nNeed to confirm the president who fits clues (won Alaska's electoral votes in 1976, Yale Law graduate, son Michael) is Gerald Ford. Queries target his birth name (which reveals middle name), his middle name directly, and his son Michael to verify family clue.\nNeed to locate the president's birth name to extract his middle name. Prior attempts on 'Gerald Ford middle name' and 'Gerald Ford birth name' returned no results, so broaden by using his full birth name and a variant that includes the middle name directly.\n\nTASK: GENERATE QUERIES\nAnalyze what's MISSING, then generate up to 3 strategic search queries.\n\nCRITICAL ANALYSIS OF FAILURES:\nLook at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern):\n- STOP that approach immediately\n- ESCALATE to broader searches\n- Get main Wikipedia articles first\n- DO NOT repeat failed query patterns\n\nESCALATION STRATEGIES (when stuck):\n1. **Get full articles**: Search just entity name \"Harriet Lane\" (not \"Harriet Lane mother\")\n2. **Use list articles**: \"List of presidents\", \"List of first ladies\"\n3. **Search related entities**: If can't find X's mother, search X's family/siblings\n4. **Try alternate names**: Full name, nickname, maiden name\n\nQUERY PATTERNS:\n- **Biography**: Person's name alone → gets full Wikipedia with infobox family details\n- **Lists/Rankings**: \"List of X\", \"Timeline of Y\"\n- **Simple is better**: 2-4 words, entity names not descriptions\n\nRESPONSE FORMAT (JSON only):\n{\"queries\": [\"Harriet Lane\", \"James Garfield mother\"], \"feedback\": \"Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name.\"}\n\nRULES:\n- Make queries SHORT (2-4 words)\n- Use entity NAMES not descriptions\n- Never repeat failed query patterns\n- Escalate after 3 failed attempts on same info\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"queries\": [\n \"Leslie Lynch King Jr.\",\n \"Gerald Ford children\",\n \"Gerald Ford birth name Leslie\"\n ],\n \"feedback\": \"Need to locate the president's birth name (which includes his middle name) and verify the son named Michael. The first query targets his original name directly. The second query should retrieve his family section showing son Michael. The third query combines his common name with the birth name to capture any article that mentions the rename.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 2013, + "osl": 101, + "total_tokens": 2114, + "latency_ms": 5374.38, + "tokens_per_second": 18.79 + }, + "context": { + "kept_docs_count": 6, + "iteration_state": "generating_queries", + "query_history_length": 9 + } + }, + { + "call_id": "8da467e7-4bef-4786-a352-e0b03247f2db", + "component": "check_sufficiency", + "hop_count": 5, + "timestamp": "2026-05-16T03:37:02.969163Z", + "input": { + "messages": [ + { + "role": "user", + "content": "You check if the retrieved documents are sufficient to answer a question.\n\nQUESTION: As of 2024, at the time of his birth, what was the middle name of the U.S. president who won Alaska, graduated from Yale University, and had a son named Michael?\n\nKEPT DOCUMENTS (already marked relevant):\n\n[DOC 1] Gerald Ford in 2006 Same calendar year 1826: Thomas Jefferson and John Adams , both on July 4 1862: John Tyler and Martin Van Buren , on January 18 and July 24, respectively 1901: Benjamin Harrison and William McKinley , on March 13 and September 14, respectively Same age (rounded down to nearest year) 93: Gerald Ford and Ronald Reagan 90: John Adams and Herbert Hoover 78: Andrew Jackson and Dwight D. Eisenhower 71: John Tyler and Grover Cleveland 67: George Washington , Benjamin Harrison , and Woodrow Wilson 64: Franklin Pierce and Lyndon B. Johnson 63: Ulysses S. Grant and Franklin D. Roosevelt 60: Theodore Roosevelt and Calvin Coolidge 57: Chester A. Arthur and Warren G. Harding Died before multiple predecessors William Henry Harrison (L), Abraham Lincoln\n\n[DOC 2] aring-in ceremony Recorded August 9, 1974 Gerald Rudolph Ford Jr. (born Leslie Lynch King Jr. ; July 14, 1913 – December 26, 2006) was the 38th president of the United States , serving from 1974 to 1977. A member of the Republican Party , Ford assumed the presidency after the resignation of Richard Nixon , under whom he had served as the 40th vice president from 1973 to 1974 following the resignation of Spiro Agnew . Prior to that, he served as a member of the U. S. House of Representatives from 1949 to 1973. Ford was born in Omaha, Nebraska , and raised in Grand Rapids, Michigan . He attended the University of Michigan , where he played for the university football team , before eventually attending Yale Law School . Afterward, he served in the U. S. Naval R\n\n[DOC 3] ward Taft Yale University Member of the Yale Corporation 1901–1913 Hampton University Board of Trustees 1909–1930 Warren G. Harding American University Board of Trustees 1921–1923 Calvin Coolidge Amherst College Board of Trustees (life member) 1921–1933 Herbert Hoover Stanford University Board of Trustees 1923–1960 American University Board of Trustees 1945–1950 Franklin D. Roosevelt Harvard University Board of Overseers 1917–1923 Vassar College Board of Trustees 1923–1945 Dwight D. Eisenhower Eisenhower College Namesake, fundraiser 1965–1969 John F. Kennedy Harvard University Board of Overseers 1957–1958 Jimmy Carter Mercer University Board of Trustees 2012–2024 Ronald Reagan Eureka College Board of Trustees 1947–1953, 1967–1973, 1974–1980 See also List of\n\n[DOC 4] C Ira C. Kepford USN 16 NC (2), SS, DFC Leslie H. Kerr Jr. ~5 Robert R. Kidwell Jr. 5 DFC (4) Donald D. Kienholz USAAF 6 DFC KIFA 1973 John R. Kincaid USN 5 Robert A. Kincaid 5 NC, SS, DFC (2) Benjamin H. King USAAF 7 SS, DFC (2) Charles W. King 5 SS David L. King 5 DFC William B. King 5.5 SS William J. Kingston Jr. USN 6 DFC (3) Claiborne H. Kinnard Jr. USAAF 8 DSC James J. Kinsella USN 5 DFC (3) Claude R. Kinsey USAAF 7 DFC POW 4 April 1943, escaped captivity October 1943 Marion F. Kirby 5 SS George N. Kirk USN 7 DFC (2) Lenton F. Kirkland Jr. USAAF 5 SS MIA 25 December 1944 Floyd C. Kirkpatrick USMC 5.5 DFC (3) Phillip L. Kirkwood USN 12 NC, DFC John A. Kirla USAAF 11.5 SS George E. Kiser 9 DSC, SS Frank W. Klibbe 7 Robert H. Knapp 5 DFC (3) William M. Kn\n\n[DOC 5] Butler Carpenter de Condorcet Cornell Davis (Mike) Dewey Douglas (Kelly) Douglas (William) Du Bois Dyson Ellis Fredrickson Friedan Gilmore Hammonds Hasan Helms Hirschfeld hooks Jeffries Kahane Karenga Keller Kendi King McIntosh Merriam Meyers Mills Moore Oastler Ostrowski Painter Patterson Peled-Elhanan Perez Reich (Wilhelm) Roberts Rothstein Saini Sanger Seuss Shaw Stanley Stein Su TallBear Walia Welsing West Whedon Wilderson III Wong (Joshua) Zuboff Politicians Albanese Anderson Ardern Atatürk Attlee Bhutto Boric Brandt Butler Bryan Corbyn Cuza Đinđić Drees F. D. R. Hessel Hontiveros Hunter İmamoğlu La Follette Layton L. B. J. Lula Lyuh Manley Mandela Mottley Murayama Nehru Norton Obrador Petro Sánchez Sanders Tsai Warren Yamamoto Zapatero Organizations A\n\n[DOC 6] † N/A 1968 George Scott Bobby Knoop Brooks Robinson * † Luis Aparicio † Reggie Smith Mickey Stanley Carl Yastrzemski † Bill Freehan Jim Kaat † N/A 1969 Joe Pepitone Davey Johnson Brooks Robinson * † Mark Belanger Paul Blair Mickey Stanley Carl Yastrzemski † Bill Freehan Jim Kaat † N/A 1970 Jim Spencer Davey Johnson Brooks Robinson * † Luis Aparicio † Paul Blair Mickey Stanley Ken Berry Ray Fosse Jim Kaat † N/A 1971 George Scott Davey Johnson Brooks Robinson * † Mark Belanger Paul Blair Amos Otis Carl Yastrzemski † Ray Fosse Jim Kaat † N/A 1972 George Scott Doug Griffin Brooks Robinson * † Ed Brinkman Paul Blair Bobby Murcer Ken Berry Carlton Fisk † Jim Kaat † N/A 1973 George Scott Bobby Grich Brooks Robinson * † Mark Belanger Paul Blair Amos Otis Mickey Stan\n\n\nTASK: Review ALL kept documents and determine if they contain enough information to answer the question.\n\nTry to connect information across documents:\n- Match entity names across documents\n- Chain relationships (A → B → C)\n- Cross-reference dates, positions, attributes\n- Build complete answer chains\n\nITERATION: 5 of 5\n\nIf iteration is 5 (the last iteration), you MUST provide a final answer with available information or return \"Unknown\" if truly insufficient.\n\nOUTPUT FORMAT (JSON only):\nIf sufficient: {\"sufficient\": true, \"reasoning\": \"Found all required facts across documents X, Y, Z...\"}\nIf not sufficient: {\"sufficient\": false, \"reasoning\": \"Missing: specific fact or detail needed...\"}\n\nRULES:\n- Be precise about what information is present vs missing\n- If on final iteration (5), force sufficient=true to generate final answer\n" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "{\n \"sufficient\": false,\n \"reasoning\": \"The kept documents provide Gerald Ford's birth name (Leslie Lynch King Jr.) giving his middle name at birth, and mention his attendance at Yale Law School. However, they do not contain information confirming that the president won Alaska in an election or that he had a son named Michael. Without those facts, we cannot definitively identify the president based solely on the provided documents, so the answer cannot be derived from the available information.\"\n}", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1592, + "osl": 101, + "total_tokens": 1693, + "latency_ms": 13626.85, + "tokens_per_second": 7.41 + }, + "context": { + "kept_docs_count": 6, + "iteration": 5, + "max_iterations": 5, + "iteration_state": "checking_sufficiency" + } + }, + { + "call_id": "0e7f794f-f909-460f-bacb-4c3d58087f1f", + "component": "answer_generator", + "hop_count": 5, + "timestamp": "2026-05-16T03:37:12.701235Z", + "input": { + "messages": [ + { + "role": "user", + "content": "Answer the question based ONLY on the provided documents.\n\nQUESTION: As of 2024, at the time of his birth, what was the middle name of the U.S. president who won Alaska, graduated from Yale University, and had a son named Michael?\n\nDOCUMENTS:\n\n[DOC 1] Gerald Ford in 2006 Same calendar year 1826: Thomas Jefferson and John Adams , both on July 4 1862: John Tyler and Martin Van Buren , on January 18 and July 24, respectively 1901: Benjamin Harrison and William McKinley , on March 13 and September 14, respectively Same age (rounded down to nearest year) 93: Gerald Ford and Ronald Reagan 90: John Adams and Herbert Hoover 78: Andrew Jackson and Dwight D. Eisenhower 71: John Tyler and Grover Cleveland 67: George Washington , Benjamin Harrison , and Woodrow Wilson 64: Franklin Pierce and Lyndon B. Johnson 63: Ulysses S. Grant and Franklin D. Roosevelt 60: Theodore Roosevelt and Calvin Coolidge 57: Chester A. Arthur and Warren G. Harding Died before multiple predecessors William Henry Harrison (L), Abraham Lincoln\n\n[DOC 2] aring-in ceremony Recorded August 9, 1974 Gerald Rudolph Ford Jr. (born Leslie Lynch King Jr. ; July 14, 1913 – December 26, 2006) was the 38th president of the United States , serving from 1974 to 1977. A member of the Republican Party , Ford assumed the presidency after the resignation of Richard Nixon , under whom he had served as the 40th vice president from 1973 to 1974 following the resignation of Spiro Agnew . Prior to that, he served as a member of the U. S. House of Representatives from 1949 to 1973. Ford was born in Omaha, Nebraska , and raised in Grand Rapids, Michigan . He attended the University of Michigan , where he played for the university football team , before eventually attending Yale Law School . Afterward, he served in the U. S. Naval R\n\n[DOC 3] ward Taft Yale University Member of the Yale Corporation 1901–1913 Hampton University Board of Trustees 1909–1930 Warren G. Harding American University Board of Trustees 1921–1923 Calvin Coolidge Amherst College Board of Trustees (life member) 1921–1933 Herbert Hoover Stanford University Board of Trustees 1923–1960 American University Board of Trustees 1945–1950 Franklin D. Roosevelt Harvard University Board of Overseers 1917–1923 Vassar College Board of Trustees 1923–1945 Dwight D. Eisenhower Eisenhower College Namesake, fundraiser 1965–1969 John F. Kennedy Harvard University Board of Overseers 1957–1958 Jimmy Carter Mercer University Board of Trustees 2012–2024 Ronald Reagan Eureka College Board of Trustees 1947–1953, 1967–1973, 1974–1980 See also List of\n\n[DOC 4] C Ira C. Kepford USN 16 NC (2), SS, DFC Leslie H. Kerr Jr. ~5 Robert R. Kidwell Jr. 5 DFC (4) Donald D. Kienholz USAAF 6 DFC KIFA 1973 John R. Kincaid USN 5 Robert A. Kincaid 5 NC, SS, DFC (2) Benjamin H. King USAAF 7 SS, DFC (2) Charles W. King 5 SS David L. King 5 DFC William B. King 5.5 SS William J. Kingston Jr. USN 6 DFC (3) Claiborne H. Kinnard Jr. USAAF 8 DSC James J. Kinsella USN 5 DFC (3) Claude R. Kinsey USAAF 7 DFC POW 4 April 1943, escaped captivity October 1943 Marion F. Kirby 5 SS George N. Kirk USN 7 DFC (2) Lenton F. Kirkland Jr. USAAF 5 SS MIA 25 December 1944 Floyd C. Kirkpatrick USMC 5.5 DFC (3) Phillip L. Kirkwood USN 12 NC, DFC John A. Kirla USAAF 11.5 SS George E. Kiser 9 DSC, SS Frank W. Klibbe 7 Robert H. Knapp 5 DFC (3) William M. Kn\n\n[DOC 5] Butler Carpenter de Condorcet Cornell Davis (Mike) Dewey Douglas (Kelly) Douglas (William) Du Bois Dyson Ellis Fredrickson Friedan Gilmore Hammonds Hasan Helms Hirschfeld hooks Jeffries Kahane Karenga Keller Kendi King McIntosh Merriam Meyers Mills Moore Oastler Ostrowski Painter Patterson Peled-Elhanan Perez Reich (Wilhelm) Roberts Rothstein Saini Sanger Seuss Shaw Stanley Stein Su TallBear Walia Welsing West Whedon Wilderson III Wong (Joshua) Zuboff Politicians Albanese Anderson Ardern Atatürk Attlee Bhutto Boric Brandt Butler Bryan Corbyn Cuza Đinđić Drees F. D. R. Hessel Hontiveros Hunter İmamoğlu La Follette Layton L. B. J. Lula Lyuh Manley Mandela Mottley Murayama Nehru Norton Obrador Petro Sánchez Sanders Tsai Warren Yamamoto Zapatero Organizations A\n\n[DOC 6] † N/A 1968 George Scott Bobby Knoop Brooks Robinson * † Luis Aparicio † Reggie Smith Mickey Stanley Carl Yastrzemski † Bill Freehan Jim Kaat † N/A 1969 Joe Pepitone Davey Johnson Brooks Robinson * † Mark Belanger Paul Blair Mickey Stanley Carl Yastrzemski † Bill Freehan Jim Kaat † N/A 1970 Jim Spencer Davey Johnson Brooks Robinson * † Luis Aparicio † Paul Blair Mickey Stanley Ken Berry Ray Fosse Jim Kaat † N/A 1971 George Scott Davey Johnson Brooks Robinson * † Mark Belanger Paul Blair Amos Otis Carl Yastrzemski † Ray Fosse Jim Kaat † N/A 1972 George Scott Doug Griffin Brooks Robinson * † Ed Brinkman Paul Blair Bobby Murcer Ken Berry Carlton Fisk † Jim Kaat † N/A 1973 George Scott Bobby Grich Brooks Robinson * † Mark Belanger Paul Blair Amos Otis Mickey Stan\n\n\nINSTRUCTIONS:\n- Provide a specific, concise answer\n- Base your answer ONLY on facts from the documents\n- If documents don't contain enough information, answer \"Unknown\"\n- Do NOT guess or make assumptions\n\nAnswer:" + } + ], + "model": "openai/gpt-oss-120b", + "temperature": 1, + "max_tokens": 10240, + "top_p": 1.0, + "top_k": -1, + "reasoning_effort": "medium", + "other_params": {} + }, + "output": { + "response": "Lynch", + "finish_reason": "stop" + }, + "metrics": { + "isl": 1447, + "osl": 2, + "total_tokens": 1449, + "latency_ms": 9731.23, + "tokens_per_second": 0.21 + }, + "context": { + "num_documents_used": 6, + "iteration_state": "generating_final_answer", + "retrieval_complete": true + } + } + ], + "timestamp_end": "2026-05-16T03:37:12.701990Z", + "summary": { + "total_llm_calls": 10, + "total_hops": 5, + "total_input_tokens": 13612, + "total_output_tokens": 6811, + "total_tokens": 20423, + "total_latency_ms": 205234.01, + "average_tokens_per_second": 29.88, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ] + }, + "retrieval_results": { + "retrieved_urls": [], + "correct_urls": [ + "https://en.wikipedia.org/wiki/United_States_presidential_elections_in_Alaska", + "https://en.wikipedia.org/wiki/Gerald_Ford" + ], + "precision": 0, + "recall": 0, + "f1": 0 + }, + "answer_results": { + "llm_answer": "Lynch", + "ground_truth_answer": "Lynch" + } + } + ], + "experiment_summary": { + "total_queries": 50, + "total_llm_calls": 481, + "total_input_tokens": 492583, + "total_output_tokens": 257313, + "total_tokens": 749896, + "total_latency_ms": 3881933.57, + "average_tokens_per_second": 98.87, + "average_hops_per_query": 4.2, + "components_used": [ + "check_sufficiency", + "evaluate_document_relevance", + "answer_generator", + "generate_search_queries" + ], + "retrieval_metrics": { + "average_precision": 0.0, + "average_recall": 0.0, + "average_f1": 0.0 + }, + "answer_metrics": { + "average_judge_score": 0.0, + "queries_correct": 0, + "queries_incorrect": 50, + "accuracy": 0.0 + } + } +} \ No newline at end of file diff --git a/e2e/multi_shot_retrieval.py b/e2e/multi_shot_retrieval.py new file mode 100644 index 0000000000..b87d56375f --- /dev/null +++ b/e2e/multi_shot_retrieval.py @@ -0,0 +1,1831 @@ +""" +Multi-shot Retrieval System + +This module implements multi-shot retrieval with query decomposition: +1. Takes a complex query +2. Uses LLM to rewrite/decompose into multiple sub-queries (max k=3) +3. Retrieves documents for each sub-query +4. Optionally reranks combined results +5. Evaluates performance + +Architecture: + Prompt → Query Rewriter (LLM) → k Sub-queries → Retrieval → Reranking → Evaluation +""" + +import argparse +import json +import re +import time +import os +from concurrent.futures import ThreadPoolExecutor, as_completed +from datetime import datetime +from typing import List, Dict, Any, Optional +import pandas as pd + +# Set no_proxy to bypass proxy for localhost/127.0.0.1 +original_no_proxy = os.environ.get('no_proxy', '') +os.environ['no_proxy'] = '127.0.0.1,localhost,' + original_no_proxy +os.environ['NO_PROXY'] = '127.0.0.1,localhost,' + original_no_proxy + +# Get OpenRouter API key from environment +OPENROUTER_API_KEY = os.environ.get('OPENROUTER_API_KEY', '') + +from retrieve import VectorDB, BM25DB +from evaluation import evaluate_retrieval_query, run_evaluation +from utils import (set_deterministic_seeds, filter_dataset_by_difficulty, + setup_llm_config, get_device_config) +from params import add_all_args +from llm_logger import LLMLogger +import requests + +# Prompts + + +RELEVANCE_CHECK_PROMPT = """\ +You are a document relevance classifier. Evaluate if documents are relevant to answering a question. + +QUESTION: {question} + +NEW DOCUMENTS TO EVALUATE: +{new_docs} + +KEPT DOCUMENTS (context only): +{kept_docs} + +TASK: For each NEW document, mark 1 if it contains ANY information relevant to answering the question, 0 if completely irrelevant. + +Be GENEROUS in marking relevance: +- Mark 1 if document mentions any entity/person/place/event in the question +- Mark 1 if document has related information that could help answer +- Mark 1 if document has facts that could be connected with other docs +- Only mark 0 if truly unrelated content + +OUTPUT FORMAT (JSON only): +{{"relevance": [1, 0, 1, 0, 1]}} + +The array MUST have exactly {num_docs} elements (one per NEW document). +""" + +SUFFICIENCY_CHECK_PROMPT = """\ +You check if the retrieved documents are sufficient to answer a question. + +QUESTION: {question} + +KEPT DOCUMENTS (already marked relevant): +{kept_docs} + +TASK: Review ALL kept documents and determine if they contain enough information to answer the question. + +Try to connect information across documents: +- Match entity names across documents +- Chain relationships (A → B → C) +- Cross-reference dates, positions, attributes +- Build complete answer chains + +ITERATION: {iteration} of {max_iterations} + +If iteration is {max_iterations} (the last iteration), you MUST provide a final answer with available information or return "Unknown" if truly insufficient. + +OUTPUT FORMAT (JSON only): +If sufficient: {{"sufficient": true, "reasoning": "Found all required facts across documents X, Y, Z..."}} +If not sufficient: {{"sufficient": false, "reasoning": "Missing: specific fact or detail needed..."}} + +RULES: +- Be precise about what information is present vs missing +- If on final iteration ({max_iterations}), force sufficient=true to generate final answer +""" + +QUERY_GENERATION_PROMPT = """\ +You generate search queries for complex multi-hop questions. + +QUESTION: {question} + +KEPT DOCUMENTS (already marked relevant): +{kept_docs} + +SEARCH HISTORY: {history} +FEEDBACK: {feedback_history} + +TASK: GENERATE QUERIES +Analyze what's MISSING, then generate up to {max_queries} strategic search queries. + +CRITICAL ANALYSIS OF FAILURES: +Look at SEARCH HISTORY. If queries repeatedly failed (0 docs or 3+ attempts with same pattern): +- STOP that approach immediately +- ESCALATE to broader searches +- Get main Wikipedia articles first +- DO NOT repeat failed query patterns + +ESCALATION STRATEGIES (when stuck): +1. **Get full articles**: Search just entity name "Harriet Lane" (not "Harriet Lane mother") +2. **Use list articles**: "List of presidents", "List of first ladies" +3. **Search related entities**: If can't find X's mother, search X's family/siblings +4. **Try alternate names**: Full name, nickname, maiden name + +QUERY PATTERNS: +- **Biography**: Person's name alone → gets full Wikipedia with infobox family details +- **Lists/Rankings**: "List of X", "Timeline of Y" +- **Simple is better**: 2-4 words, entity names not descriptions + +RESPONSE FORMAT (JSON only): +{{"queries": ["Harriet Lane", "James Garfield mother"], "feedback": "Found: 15th first lady is Harriet Lane. Missing: Her mother's name, 2nd assassinated president's mother's maiden name."}} + +RULES: +- Make queries SHORT (2-4 words) +- Use entity NAMES not descriptions +- Never repeat failed query patterns +- Escalate after 3 failed attempts on same info +""" + +# Legacy monolithic prompt (still used by old query_rewriter function) +QUERY_REWRITER_PROMPT = """\ +You evaluate documents and generate search queries for complex multi-hop questions. + +QUESTION: {question} +DOCUMENTS: {context} +SEARCH HISTORY: {history} +FEEDBACK: {feedback_history} + +TASK 1: EVALUATE AND SUMMARIZE NEW DOCUMENTS +**ONLY if there are NEW documents to evaluate:** +For each NEW document: +- Mark: 1 if relevant to any part of question, 0 if irrelevant +- MANDATORY: If marked as relevant (1), you MUST provide a detailed summary preserving ALL key facts, names, dates, numbers, and relationships mentioned in the document that could be useful for answering the question +- If marked as irrelevant (0), provide empty string "" + +**If there are NO NEW documents to evaluate, skip this task and go to TASK 3** + +SUMMARY REQUIREMENTS: +- Extract and preserve specific details +- only facts from the document + +TASK 2: CHECK IF SUFFICIENT AND CONNECT INFORMATION +Review ALL KEPT documents and summaries. Actively connect facts across documents: +- Identify entities by matching names across summaries +- Chain relationships (A → B → C) +- Cross-reference dates/events +- Build complete chains: Person → Family member → Attribute, or Event → Year → Cross-reference +If you can construct a complete answer chain with specific names/facts from kept documents, provide final answer. + +TASK 3: GENERATE QUERIES (if not sufficient) +Analyze what's MISSING to complete the answer, then generate at most {k} strategic queries. + +CRITICAL ANALYSIS OF FAILURES: +Look at SEARCH HISTORY. If a query returned 0 documents OR if same query failed 2+ times: +- **STOP IMMEDIATELY** - This approach is not working +- **ESCALATE TO BROADER SEARCH** - Get the main Wikipedia article first +- **DO NOT REUSE FAILED PATTERNS** - Varying descriptions of the same thing won't help + +ESCALATION STRATEGIES (use when stuck 3+ iterations): + +**When repeated queries fail:** +1. **Get full article first**: Search just the entity name to get their complete Wikipedia page +2. **Search related entities**: If can't find X's mother, search for X's siblings, X's biography, X's family tree +3. **Use list articles**: "List of presidents", "List of first ladies", "Timeline of X" +4. **Try alternate names**: Full formal name, nickname, maiden name, married name + +STRATEGIC QUERY PATTERNS: + +**For People/Biography:** +- Full article: Just the person's name "Harriet Lane" +- Family: "Person X family", "Person X parents" +- Specific relative: "Person X" then extract family, don't search "Person X mother" repeatedly + +**For Events/Dates:** +- Specific year: "X 2012 winner" +- Timeline: "X dates", "List of X events" +- Cross-reference: "List tallest buildings X City" + +**For Numerical/Classification:** +- List/table: "Billboard 200 Nuclear Blast records", "Dewey Decimal X" + +**Progressive refinement strategy:** +1. **Get main articles FIRST**: Search entity names to get full Wikipedia pages +2. **Extract connections**: From articles, identify related entities and search those +3. **Get details**: Once you have related entities, search for their specific attributes +4. **Cross-verify**: Use list articles to confirm order/position/relationships + +QUERY GENERATION RULES (FOLLOW EXACTLY): +1. **GET FULL ARTICLES FIRST**: To find family/biographical info about Person X, search "Person X" alone to get their complete Wikipedia article with infobox and family details +2. **USE ENTITY NAMES, NOT DESCRIPTIONS**: Once you identify an entity name, always use the name in queries, never use descriptions +3. **ONE CONCEPT PER QUERY**: Each query should target one piece of information +4. **NEVER REPEAT FAILURES**: Check SEARCH HISTORY - if a query pattern failed (0 docs or 3+ attempts), use a completely different approach +5. **ESCALATE WHEN STUCK**: After a failed attempt on same information, escalate to broader queries: + - Search the main entity name alone (get full Wikipedia article) + - Search "List of X" for ordinal/positional questions + - Search related entities instead + +RESPONSE FORMAT: +**If there are NEW documents:** +If sufficient: {{"relevance": [1,0,1], "summaries": ["Person served as X from dates, family details include mother named Y with maiden name Z", "", "Person was Nth X, mother was Y Z with maiden name Ballou"], "answer": "Y Z"}} +If not: {{"relevance": [1,0,1], "summaries": ["Person A served from dates, mentioned as related to B", "", "Person C was Nth X, mother mentioned as Y"], "queries": ["Person A", "Person C family"], "feedback": "Found: Entity names and roles. Missing: Complete family details. Next: Get full biographical articles."}} + +**If there are NO NEW documents:** +{{"relevance": [], "summaries": [], "queries": ["Nth position holder name", "specific event list"], "feedback": "Starting with direct entity/list searches."}} + +CRITICAL REQUIREMENTS: +- If NO NEW documents: return empty arrays: "relevance": [], "summaries": [] +- If {len_new_docs} NEW documents: return exactly {len_new_docs} relevance scores and {len_new_docs} summaries +- For relevant docs (relevance=1): summary MUST extract specific facts (names, dates, relationships, family details from infobox/text) +- For irrelevant docs (relevance=0): summary MUST be empty string "" +- **ESCALATION TRIGGER**: If same query type failed 3+ times, MUST escalate to full article or list search +- Make queries SHORT and SIMPLE (2-4 words best) - Wikipedia article titles are short +- Search entity names alone to get complete biographical articles with family sections +- Wikipedia infoboxes contain: parents, born, died, spouse, children - search person's name to get this + +FORMAT VALIDATION: Array lengths MUST match document count - double check before responding! +SEARCH STRATEGY: Simple entity names work better than complex descriptive queries! +Respond only in JSON format""" + + + + + +def get_chat_completions_headers(service_url: str): + """Headers for an OpenAI-compatible /v1/chat/completions request. + + Adds Bearer auth + OpenRouter-specific headers when the URL is OpenRouter + and OPENROUTER_API_KEY is set. Local endpoints (vLLM, etc.) get an empty + header dict. + """ + if "openrouter.ai" in service_url and OPENROUTER_API_KEY: + return { + "Authorization": f"Bearer {OPENROUTER_API_KEY}", + "HTTP-Referer": "https://github.com/anthropics/e2e-docgrader", + "X-Title": "E2E DocGrader Multi-Shot Retrieval" + } + return {} + +def call_chat_completions(service_url: str, model_name: str, messages: List[Dict], + temperature: float = 1.0, max_tokens: int = 4096, + top_p: float = 1.0, + top_k: int = -1, + reasoning_effort: str = "medium", + frequency_penalty: float = 0.0, + presence_penalty: float = 0.0, + repetition_penalty: float = 1.0, + max_retries: int = 5, + logger: Optional[LLMLogger] = None, + component: str = "unknown", + hop_count: Optional[int] = None, + context: Dict[str, Any] = None) -> str: + """Call OpenRouter API with proper authentication and logging. + + Default sampling parameters: + - top_p=1.0: No nucleus sampling (consider full distribution) + - top_k=-1: No top-k filtering (unlimited vocabulary) + - reasoning_effort="medium": Balanced reasoning vs speed + """ + payload = { + "model": model_name, + "messages": messages, + "temperature": temperature, + "top_p": top_p, + "top_k": top_k, + "max_tokens": max_tokens + } + + # Add reasoning_effort if not default + if reasoning_effort != "medium": + payload["reasoning_effort"] = reasoning_effort + else: + payload["reasoning_effort"] = reasoning_effort # Always include for logging + + # Add optional sampling parameters + if frequency_penalty != 0.0: + payload["frequency_penalty"] = frequency_penalty + if presence_penalty != 0.0: + payload["presence_penalty"] = presence_penalty + if repetition_penalty != 1.0: + payload["repetition_penalty"] = repetition_penalty + + headers = get_chat_completions_headers(service_url) + + for attempt in range(max_retries): + start_time = time.time() + try: + response = requests.post(service_url, json=payload, headers=headers, timeout=120) + + # Retry on rate limit + if response.status_code == 429: + retry_after = int(response.headers.get('Retry-After', 2 ** attempt)) + print(f" Rate limited (429). Retrying in {retry_after}s (attempt {attempt+1}/{max_retries})") + time.sleep(retry_after) + continue + + response.raise_for_status() + result = response.json() + latency_ms = (time.time() - start_time) * 1000 + + message = result['choices'][0]['message'] + llm_output = (message.get('content') or '').strip() + + # Fallback: thinking models put output in reasoning_content + if not llm_output: + reasoning_content = message.get('reasoning_content') or '' + if reasoning_content: + json_match = re.search(r'\{.*\}', reasoning_content, re.DOTALL) + if json_match: + llm_output = json_match.group(0) + + if not llm_output: + print(f" WARNING [{component}]: LLM returned empty content. Raw response: {json.dumps(result)[:500]}") + + # Log this call + if logger: + logger.log_llm_call( + component=component, + hop_count=hop_count, + payload=payload, + response=result, + latency_ms=latency_ms, + context=context or {} + ) + + return llm_output + + except requests.exceptions.HTTPError as e: + status = e.response.status_code if e.response is not None else None + if status in (502, 503, 504) and attempt < max_retries - 1: + wait = 2 ** attempt + print(f" Server error ({status}). Retrying in {wait}s (attempt {attempt+1}/{max_retries})") + time.sleep(wait) + continue + print(f" ERROR [{component}]: HTTP {status}: {e}") + raise + except requests.exceptions.Timeout: + if attempt < max_retries - 1: + wait = 2 ** attempt + print(f" Timeout. Retrying in {wait}s (attempt {attempt+1}/{max_retries})") + time.sleep(wait) + continue + print(f" ERROR [{component}]: Request timed out after {max_retries} attempts") + raise + except Exception as e: + print(f" ERROR [{component}]: {e}") + raise + + print(f" ERROR [{component}]: Max retries ({max_retries}) exceeded") + raise RuntimeError(f"LLM call failed after {max_retries} retries") + +def evaluate_document_relevance(question: str, + new_documents: List[tuple], + kept_documents: List[tuple], + llm_config: Optional[Dict[str, Any]] = None, + logger: Optional[LLMLogger] = None, + hop_count: int = 1) -> Dict[str, Any]: + """Simple binary relevance classification using the grader model.""" + if not new_documents: + return {"relevance": []} + + service_url = llm_config['grader_service_url'] + model_name = llm_config['grader_model_name'] + max_tokens = 4096 + + # Format NEW documents + new_docs_text = "" + for i, (url, content) in enumerate(new_documents): + new_docs_text += f"\n[NEW {i+1}] {content}\n" + + # Format KEPT documents + kept_docs_text = "" + if kept_documents: + kept_docs_text = f"[{len(kept_documents)} documents already kept as relevant]\n" + for i, doc in enumerate(kept_documents[:5]): + content = doc[1] if len(doc) >= 2 else "" + snippet = content[:300] if len(content) > 300 else content + kept_docs_text += f"[KEPT {i+1}] {snippet}...\n" + + prompt = RELEVANCE_CHECK_PROMPT.format( + question=question, + new_docs=new_docs_text, + kept_docs=kept_docs_text if kept_docs_text else "None", + num_docs=len(new_documents) + ) + + messages = [{"role": "user", "content": prompt}] + + try: + llm_output = call_chat_completions( + service_url=service_url, + model_name=model_name, + messages=messages, + temperature=llm_config.get('temperature', 1.0), + max_tokens=max_tokens, + top_p=1.0, + top_k=-1, + reasoning_effort="medium", + max_retries=llm_config.get('max_retries', 5), + logger=logger, + component="evaluate_document_relevance", + hop_count=hop_count, + context={ + "num_documents_evaluated": len(new_documents), + "iteration_state": "evaluating_relevance", + "kept_docs_before": len(kept_documents) + } + ) + + print(f" [DEBUG] Relevance LLM raw output: {llm_output[:200]}...") + + if not llm_output: + print(f" Warning: Relevance check returned empty, marking all as relevant") + return {"relevance": [1] * len(new_documents)} + + if llm_output.startswith("```"): + llm_output = llm_output.split("```")[1] + if llm_output.startswith("json"): + llm_output = llm_output[4:] + llm_output = llm_output.strip() + + relevance_result = json.loads(llm_output) + relevance = relevance_result.get("relevance", []) + + if len(relevance) != len(new_documents): + print(f" Warning: Relevance mismatch. Expected {len(new_documents)}, got {len(relevance)}") + return {"relevance": [1] * len(new_documents)} + + return {"relevance": relevance} + + except Exception as e: + print(f" Error in relevance check: {e}") + return {"relevance": [1] * len(new_documents)} + + +def check_sufficiency(question: str, + kept_documents: List[tuple], + iteration: int, + max_iterations: int, + llm_config: Optional[Dict[str, Any]] = None, + logger: Optional[LLMLogger] = None, + hop_count: int = 1) -> Dict[str, Any]: + """ + Check if kept documents are sufficient to answer the question. + Uses gpt-oss-120b model via OpenRouter. + + Args: + question: The user's original question + kept_documents: List of documents marked as relevant + iteration: Current iteration number + max_iterations: Maximum iterations allowed + llm_config: LLM configuration + + Returns: + Dict with: + - 'sufficient' (bool): Whether documents are sufficient + - 'reasoning' (str): Explanation of decision + """ + service_url = llm_config['sufficiency_service_url'] + model_name = llm_config['sufficiency_model_name'] + max_tokens = 10240 + + # Format KEPT documents + kept_docs_text = "" + if kept_documents: + for i, doc in enumerate(kept_documents): + content = doc[1] if len(doc) >= 2 else "" + kept_docs_text += f"\n[DOC {i+1}] {content}\n" + else: + kept_docs_text = "None" + + prompt = SUFFICIENCY_CHECK_PROMPT.format( + question=question, + kept_docs=kept_docs_text, + iteration=iteration, + max_iterations=max_iterations + ) + + messages = [{"role": "user", "content": prompt}] + + try: + llm_output = call_chat_completions( + service_url=service_url, + model_name=model_name, + messages=messages, + temperature=llm_config.get('temperature', 1.0), + max_tokens=max_tokens, + top_p=1.0, + top_k=-1, + reasoning_effort="medium", + max_retries=llm_config.get('max_retries', 5), + logger=logger, + component="check_sufficiency", + hop_count=hop_count, + context={ + "kept_docs_count": len(kept_documents), + "iteration": iteration, + "max_iterations": max_iterations, + "iteration_state": "checking_sufficiency" + } + ) + + print(f" [DEBUG] Sufficiency check raw output: {llm_output[:200]}...") + + if not llm_output: + print(f" Warning: Sufficiency check returned empty") + # On final iteration, force sufficient + if iteration >= max_iterations: + return {"sufficient": True, "reasoning": "Max iterations reached"} + return {"sufficient": False, "reasoning": "LLM returned empty"} + + if llm_output.startswith("```"): + llm_output = llm_output.split("```")[1] + if llm_output.startswith("json"): + llm_output = llm_output[4:] + llm_output = llm_output.strip() + + # Try to extract JSON + json_match = re.search(r'\{.*\}', llm_output, re.DOTALL) + if json_match: + llm_output = json_match.group(0) + + result = json.loads(llm_output) + sufficient = result.get("sufficient", False) + reasoning = result.get("reasoning", "") + + # Force sufficient on final iteration + if iteration >= max_iterations and not sufficient: + print(f" [OVERRIDE] Final iteration - forcing sufficient=True") + sufficient = True + reasoning = f"Max iterations reached. {reasoning}" + + return {"sufficient": sufficient, "reasoning": reasoning} + + except Exception as e: + print(f" Error in sufficiency check: {e}") + # On final iteration, force sufficient + if iteration >= max_iterations: + return {"sufficient": True, "reasoning": f"Max iterations reached (error: {str(e)})"} + return {"sufficient": False, "reasoning": f"Error: {str(e)}"} + + +def generate_answer(question: str, + kept_documents: List[tuple], + llm_config: Optional[Dict[str, Any]] = None, + logger: Optional[LLMLogger] = None, + hop_count: Optional[int] = None) -> str: + """ + Generate final answer from kept documents using gpt-oss-120b. + + Args: + question: The user's original question + kept_documents: List of documents to base answer on + llm_config: LLM configuration + + Returns: + Final answer string + """ + service_url = llm_config['sufficiency_service_url'] + model_name = llm_config['sufficiency_model_name'] + max_tokens = llm_config.get('max_tokens', 10240) + + # Format KEPT documents + kept_docs_text = "" + if kept_documents: + for i, doc in enumerate(kept_documents): + content = doc[1] if len(doc) >= 2 else "" + kept_docs_text += f"\n[DOC {i+1}] {content}\n" + else: + return "Unknown" + + prompt = f"""Answer the question based ONLY on the provided documents. + +QUESTION: {question} + +DOCUMENTS: +{kept_docs_text} + +INSTRUCTIONS: +- Provide a specific, concise answer +- Base your answer ONLY on facts from the documents +- If documents don't contain enough information, answer "Unknown" +- Do NOT guess or make assumptions + +Answer:""" + + messages = [{"role": "user", "content": prompt}] + + try: + llm_output = call_chat_completions( + service_url=service_url, + model_name=model_name, + messages=messages, + temperature=llm_config.get('temperature', 1.0), + max_tokens=max_tokens, + top_p=1.0, + top_k=-1, + reasoning_effort="medium", + max_retries=llm_config.get('max_retries', 5), + logger=logger, + component="answer_generator", + hop_count=hop_count, + context={ + "num_documents_used": len(kept_documents), + "iteration_state": "generating_final_answer", + "retrieval_complete": True + } + ) + + if not llm_output or not llm_output.strip(): + return "Unknown" + + return llm_output.strip() + + except Exception as e: + print(f" Error generating answer: {e}") + return "Unknown" + + +def generate_search_queries(question: str, + kept_documents: List[tuple], + max_queries: int = 3, + query_history: Optional[List[str]] = None, + query_results: Optional[List[int]] = None, + feedback_history: Optional[List[str]] = None, + llm_config: Optional[Dict[str, Any]] = None, + logger: Optional[LLMLogger] = None, + hop_count: int = 1) -> Dict[str, Any]: + """ + Generate search queries using gpt-oss-120b via OpenRouter. + """ + service_url = llm_config['query_service_url'] + model_name = llm_config['query_model_name'] + max_tokens = llm_config.get('max_tokens', 10240) + + # Format KEPT documents + kept_docs_text = "" + if kept_documents: + for i, doc in enumerate(kept_documents): + content = doc[1] if len(doc) >= 2 else "" + kept_docs_text += f"\n[DOC {i+1}] {content}\n" + else: + kept_docs_text = "None" + + # Format history + history_text = "" + if query_history and query_results: + for q, count in zip(query_history, query_results): + history_text += f"- Query: '{q}' → {count} docs\n" + if not history_text: + history_text = "No queries yet" + + # Format feedback + feedback_text = "\n".join(feedback_history) if feedback_history else "Iteration 1 - Initial search" + + prompt = QUERY_GENERATION_PROMPT.format( + question=question, + kept_docs=kept_docs_text, + history=history_text, + feedback_history=feedback_text, + max_queries=max_queries + ) + + messages = [{"role": "user", "content": prompt}] + + try: + llm_output = call_chat_completions( + service_url=service_url, + model_name=model_name, + messages=messages, + temperature=llm_config.get('temperature', 1.0), + max_tokens=max_tokens, + top_p=1.0, + top_k=-1, + reasoning_effort="medium", + max_retries=llm_config.get('max_retries', 5), + logger=logger, + component="generate_search_queries", + hop_count=hop_count, + context={ + "kept_docs_count": len(kept_documents), + "iteration_state": "generating_queries", + "query_history_length": len(query_history) if query_history else 0 + } + ) + + print(f" [DEBUG] Query gen LLM raw output: {llm_output[:200]}...") + + if not llm_output: + print(f" Warning: Query generation returned empty") + return {"queries": [question], "feedback": "LLM returned empty"} + + if llm_output.startswith("```"): + llm_output = llm_output.split("```")[1] + if llm_output.startswith("json"): + llm_output = llm_output[4:] + llm_output = llm_output.strip() + + # Try to extract JSON object even from mixed text/markdown responses + json_match = re.search(r'\{.*\}', llm_output, re.DOTALL) + if json_match: + llm_output = json_match.group(0) + + query_result = json.loads(llm_output) + return { + "queries": query_result.get("queries", [question]), + "feedback": query_result.get("feedback", "Generating queries") + } + + except Exception as e: + print(f" Error in query generation: {e}") + return {"queries": [question], "feedback": f"Error: {str(e)}"} + + +def query_rewriter(question: str, new_documents: List[tuple], + kept_documents: List[tuple], + max_queries: int = 3, + reasoning_effort: str = "medium", + query_history: Optional[List[str]] = None, + query_results: Optional[List[int]] = None, + previous_feedback: str = "", + feedback_history: Optional[List[str]] = None, + llm_config: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: + """ + Evaluates documents AND generates new queries in one LLM call. + + Args: + question: The user's original question + new_documents: List of NEW document texts to evaluate + kept_documents: List of KEPT document texts (already marked relevant) + max_queries: Maximum number of new queries to generate + reasoning_effort: LLM reasoning level + query_history: List of previous search queries + query_results: List of number of documents found for each query (parallel to query_history) + previous_feedback: Feedback from previous iteration about what's missing + + Returns: + Dict with: + - 'relevance' (list of 0/1 for ONLY new_documents) + - 'queries' (list of new search queries, empty if answer provided) + - 'feedback' (what's missing, empty if answer provided) + - 'answer' (final answer if sufficient, empty otherwise) + """ + # Format KEPT documents with summaries + kept_context = "" + if kept_documents: + for i, doc in enumerate(kept_documents, 1): + kept_context += f"\n[KEPT {i}] {doc[2]}\n" + else: + kept_context = "None" + + # Format NEW documents + new_context = "" + if new_documents: + for i, doc in enumerate(new_documents, 1): + new_context += f"\n[NEW {i}] {doc[1]}\n" + else: + new_context = "None" + + # Combine for context + context = f"KEPT DOCUMENTS (already relevant):\n{kept_context}\n\nNEW DOCUMENTS (evaluate these):\n{new_context}" + + # Format query history with results - focus on failures for learning + if query_history: + failed_queries = [] + successful_queries = [] + if query_results and len(query_results) == len(query_history): + for q, num_docs in zip(query_history, query_results): + if num_docs == 0: + failed_queries.append(q) + else: + successful_queries.append(f"{q} ({num_docs} docs)") + + history_parts = [] + if failed_queries: + history_parts.append(f"FAILED: {', '.join(failed_queries)}") # Last 3 failures + if successful_queries: + history_parts.append(f"SUCCESS: {', '.join(successful_queries)}") # Last 2 successes + + history_text = "; ".join(history_parts) if history_parts else "No queries yet" + else: + history_text = "No queries yet" + + # Build feedback history - show progression of what was tried and learned + if feedback_history and len(feedback_history) > 0: + unique_feedback = [] + for fb in reversed(feedback_history): + if fb and fb not in unique_feedback: + unique_feedback.append(fb) + + if unique_feedback: + feedback_text = "PREVIOUS ATTEMPTS: " + " → ".join(reversed(unique_feedback)) + else: + feedback_text = f"Iteration {len(query_history) + 1 if query_history else 1}" + else: + feedback_text = f"Iteration {len(query_history) + 1 if query_history else 1} - Initial search" + + print(f"Context: {context}") + print(f"History: {history_text}") + print(f"Feedback: {feedback_text}") + + prompt = QUERY_REWRITER_PROMPT.format( + question=question, + context=context, + history=history_text, + feedback_history=feedback_text, + k=max_queries, + len_new_docs=len(new_documents) + ) + + system_message = f"""You are an expert at multi-hop reasoning and strategic search. + CRITICAL: Never repeat failed queries. + Always try completely different approaches when queries return 0 docs. + Focus on atomic facts and progressive strategies.""" + + # Use LLM config if provided, otherwise use defaults + if llm_config: + model_name = llm_config["model_name"] + service_url = llm_config["service_url"] + max_tokens = llm_config["max_tokens"] + else: + model_name = "/mnt/weka/data/pytorch/llama3.3/Meta-Llama-3.3-70B-Instruct/" + service_url = "http://127.0.0.1:8123/v1/chat/completions" + max_tokens = 10240 + + seed = llm_config.get('seed') if llm_config else None + payload = { + "model": model_name, + "messages": [ + {"role": "system", "content": system_message}, + {"role": "user", "content": prompt} + ], + "temperature": llm_config.get('temperature', 1.0) if llm_config else 1.0, + "top_p": 1, + "top_k": -1, + "max_tokens": max_tokens + } + if seed is not None: + payload["seed"] = seed + + try: + response = requests.post(service_url, json=payload, timeout=300) + response.raise_for_status() + result = response.json() + + message = result['choices'][0]['message'] + llm_output = message.get('content') + + # Fallback: use reasoning_content if content is empty (thinking models) + reasoning_content = message.get('reasoning_content', '') + if reasoning_content and not llm_output: + print(f" DEBUG: reasoning_content exists but content is empty, extracting JSON from reasoning_content") + # Try to extract JSON from reasoning_content + json_match = re.search(r'\{.*\}', reasoning_content, re.DOTALL) + if json_match: + llm_output = json_match.group(0) + print(f" DEBUG: Extracted JSON from reasoning_content ({len(llm_output)} chars)") + else: + print(f" DEBUG: No JSON found in reasoning_content snippet: {reasoning_content[:200]}") + + if llm_output is None or not llm_output.strip(): + print(f" Warning: LLM returned empty content, using original query as fallback") + # Always fall back to original query - never return empty queries + return { + "relevance": [0] * len(new_documents), + "summaries": [""] * len(new_documents), + "queries": [question], + "feedback": "LLM returned empty response", + "answer": "" + } + + llm_output = llm_output.strip() + + # Parse JSON output - handle markdown code blocks + if llm_output.startswith("```"): + llm_output = llm_output.split("```")[1] + if llm_output.startswith("json"): + llm_output = llm_output[4:] + llm_output = llm_output.strip() + + result_data = json.loads(llm_output) + + # Validate format + required_fields = ["relevance"] + for field in required_fields: + if field not in result_data: + print(f"Warning: Missing required field '{field}' in response") + result_data[field] = [0] * len(new_documents) + + # Ensure we have either "answer" OR "queries"+"feedback" + if "answer" not in result_data: + result_data["answer"] = "" + if "queries" not in result_data: + result_data["queries"] = [] + if "feedback" not in result_data: + result_data["feedback"] = "" + if "summaries" not in result_data: + result_data["summaries"] = [""] * len(new_documents) + + # Ensure relevance array matches NEW document count - fix mismatches by padding/truncating + if len(result_data["relevance"]) != len(new_documents): + print(f"Warning: Relevance array length mismatch. Expected {len(new_documents)}, got {len(result_data['relevance'])}. Auto-fixing.") + relevance = result_data["relevance"][:len(new_documents)] # Truncate if too long + while len(relevance) < len(new_documents): # Pad with 0s if too short + relevance.append(0) + result_data["relevance"] = relevance + print(f"Fixed relevance array: {relevance}") + + # Ensure summaries array matches NEW document count - fix mismatches by padding/truncating + if len(result_data["summaries"]) != len(new_documents): + print(f"Warning: Summaries array length mismatch. Expected {len(new_documents)}, got {len(result_data['summaries'])}. Auto-fixing.") + summaries = result_data["summaries"][:len(new_documents)] # Truncate if too long + while len(summaries) < len(new_documents): # Pad with empty strings if too short + summaries.append("") + result_data["summaries"] = summaries + print(f"Fixed summaries array length: {len(summaries)}") + + # Validate that relevant documents have non-empty summaries + for i, (rel, summary) in enumerate(zip(result_data["relevance"], result_data["summaries"])): + if rel == 1 and not summary.strip(): + print(f"Warning: Document {i+1} marked relevant but has empty summary. This defeats the summarization purpose.") + # Don't auto-fix here - let it be empty to debug the issue + + # Ensure queries is a list + if not isinstance(result_data["queries"], list): + result_data["queries"] = [] + + return result_data + + except requests.exceptions.RequestException as e: + print(f"Error calling combined LLM: {e}") + return { + "relevance": [0] * len(new_documents), + "summaries": [""] * len(new_documents), + "queries": [question] if not query_history else [], + "feedback": f"API error: {str(e)}", + "answer": "" + } + except json.JSONDecodeError as e: + print(f"Error parsing LLM output: {e}") + print(f"LLM output: {llm_output[:200]}") + return { + "relevance": [0] * len(new_documents), + "summaries": [""] * len(new_documents), + "queries": [question] if not query_history else [], + "feedback": f"JSON parse error: {str(e)}", + "answer": "" + } + except Exception as e: + print(f"Unexpected error: {e}") + import traceback + traceback.print_exc() + return { + "relevance": [0] * len(new_documents), + "summaries": [""] * len(new_documents), + "queries": [question] if not query_history else [], + "feedback": f"Unexpected error: {str(e)}", + "answer": "" + } + + +def multi_shot_retrieval(rag_db, original_query: str, expected_urls: List[str], + expected_answer: str = "", + max_sub_queries: int = 3, + top_k_retriever: int = 10, + top_k_reranking: int = 10, + max_iterations: int = 10, + no_rerank: bool = False, + retrieval_strategy: str = "fixed_k", + verbose: bool = True, + reasoning_effort: str = "medium", + llm_config: Optional[Dict[str, Any]] = None, + logger: Optional[LLMLogger] = None, + **strategy_params) -> Dict[str, Any]: + """ + Multi-shot retrieval with iterative query refinement and document evaluation. + + Algorithm: + 1. Generate initial search queries based on the original question + 2. Retrieve documents for each query + 3. Evaluate documents and check if sufficient to answer + 4. If not sufficient: generate new queries based on what's missing, go to step 2 + 5. Repeat until sufficient or max_iterations reached + + Args: + rag_db: RAG database instance + original_query: Original user question + expected_urls: Expected ground truth URLs for evaluation + max_sub_queries: Maximum number of sub-queries per iteration + top_k_retriever: Number of documents to retrieve per sub-query + top_k_reranking: Final number of documents to return + max_iterations: Maximum number of retrieval iterations (default: 10) + no_rerank: Skip reranking step + retrieval_strategy: Strategy for retrieval + verbose: Print detailed information + reasoning_effort: LLM reasoning level + **strategy_params: Additional parameters for retrieval strategy + + Returns: + Dictionary containing evaluation metrics and iteration statistics + """ + + start_time = time.perf_counter() + + # Track iteration history + query_history = [] + query_results = [] # Track how many docs each query found + kept_docs = [] # List of (url, content, summary) tuples that were marked relevant + new_docs = [] # List of (url, content) tuples just retrieved this iteration + all_retrieved_urls = set() + iteration_times = [] + previous_feedback = "" # Feedback from previous iteration + feedback_history = [] # Track all feedback to show progression + + sufficient = False + iteration = 0 + final_answer = "" + + if verbose: + print(f"\n{'='*80}") + print(f"MULTI-SHOT RETRIEVAL") + print(f"{'='*80}") + print(f"Original Query: {original_query}") + print(f"Max iterations: {max_iterations}") + print(f"Max sub-queries per iteration: {max_sub_queries}") + print(f"{'='*80}\n") + + while not sufficient and iteration < max_iterations: + iteration += 1 + iteration_start = time.perf_counter() + + if verbose: + print(f"\n{'─'*80}") + print(f"ITERATION {iteration}/{max_iterations}") + print(f"{'─'*80}") + + # Step 1: Use combined function to grade NEW docs AND generate new queries + if verbose: + print(f"\n Evaluating documents and generating queries...") + + # Aggressive summarization: use summaries after iteration 2 to improve information connection + total_content_length = sum(len(doc[1]) for doc in kept_docs) + + # Special handling for iteration 1: decompose original query first + if iteration == 1 and not new_docs and not kept_docs: + if verbose: + print(f" [ITERATION 1] Decomposing original query into sub-queries via generate_search_queries...") + + # Use generate_search_queries for initial decomposition (uses query_model_name / gpt-oss-120b) + query_result = generate_search_queries( + question=original_query, + kept_documents=[], + max_queries=max_sub_queries, + query_history=None, + query_results=None, + feedback_history=None, + llm_config=llm_config, + logger=logger, + hop_count=iteration + ) + + sub_queries = query_result.get("queries", [original_query]) + if not sub_queries: + sub_queries = [original_query] + + if verbose: + print(f" Generated {len(sub_queries)} initial sub-queries") + for i, q in enumerate(sub_queries, 1): + print(f" {i}. {q}") + + sufficient = False + final_answer = "" + current_feedback = query_result.get("feedback", "Initial query decomposition") + relevance = [] + summaries = [] + reasoning_steps = "" + + else: + # CALL 1: Evaluate document relevance (if we have new docs) + relevance = [] + if new_docs: + if verbose: + print(f" [CALL1] Evaluating {len(new_docs)} new documents with gpt-oss-20b...") + + relevance_result = evaluate_document_relevance( + question=original_query, + new_documents=new_docs, + kept_documents=kept_docs, + llm_config=llm_config, + logger=logger, + hop_count=iteration + ) + relevance = relevance_result.get("relevance", [1] * len(new_docs)) + + # Add relevant docs to kept_docs IMMEDIATELY + for i, (url, content) in enumerate(new_docs): + if i < len(relevance) and relevance[i] == 1: + kept_docs.append((url, content, content[:1000])) + + if verbose: + print(f" Marked {sum(relevance)} of {len(new_docs)} docs as relevant") + print(f" Relevance array: {relevance}") + print(f" Total kept docs now: {len(kept_docs)}") + + # CALL 2: Check sufficiency - uses gpt-oss-120b + if kept_docs: + if verbose: + print(f" [CALL2] Checking sufficiency with gpt-oss-120b (iteration {iteration}/{max_iterations})...") + + sufficiency_result = check_sufficiency( + question=original_query, + kept_documents=kept_docs, + iteration=iteration, + max_iterations=max_iterations, + llm_config=llm_config, + logger=logger, + hop_count=iteration + ) + + sufficient = sufficiency_result.get("sufficient", False) + sufficiency_reasoning = sufficiency_result.get("reasoning", "") + + if verbose: + print(f" Sufficient: {sufficient}") + print(f" Reasoning: {sufficiency_reasoning}") + + else: + sufficient = False + sufficiency_reasoning = "No relevant documents kept yet" + + # CALL 3a or 3b: Either generate answer (if sufficient) or generate queries (if not) + if sufficient: + # CALL 3a: Generate final answer - uses gpt-oss-120b + if verbose: + print(f" [CALL3a] Generating final answer with gpt-oss-120b...") + + final_answer = generate_answer( + question=original_query, + kept_documents=kept_docs, + llm_config=llm_config, + logger=logger, + hop_count=iteration + ) + + if verbose: + print(f" Generated answer: {final_answer[:200]}...") + + sub_queries = [] + current_feedback = sufficiency_reasoning + else: + # CALL 3b: Generate search queries - uses gpt-oss-120b + if verbose: + print(f" [CALL3b] Generating search queries with gpt-oss-120b...") + + # Cap kept_docs sent to avoid context overflow + MAX_DOCS_FOR_QUERY_GEN = 12 + docs_for_query_gen = kept_docs[-MAX_DOCS_FOR_QUERY_GEN:] if len(kept_docs) > MAX_DOCS_FOR_QUERY_GEN else kept_docs + + query_result = generate_search_queries( + question=original_query, + kept_documents=docs_for_query_gen, + max_queries=max_sub_queries, + query_history=query_history, + query_results=query_results, + feedback_history=feedback_history, + llm_config=llm_config, + logger=logger, + hop_count=iteration + ) + + sub_queries = query_result.get("queries", []) + current_feedback = query_result.get("feedback", "") + final_answer = "" + + if verbose: + print(f" Generated {len(sub_queries)} queries") + + summaries = [] + reasoning_steps = "" + + + # Add to feedback history if it's new and meaningful + if current_feedback and current_feedback.strip() and current_feedback != previous_feedback: + feedback_history.append(current_feedback.strip()) + + previous_feedback = current_feedback + + if verbose: + print(f" Sufficient: {'yes' if sufficient else 'no'}") + print(f" Kept docs: {len(kept_docs)}") + if new_docs: + print(f" New docs evaluated: {len(new_docs)}") + print(f" Relevant new docs: {sum(relevance)}/{len(relevance)}") + print(f" Relevance array: {relevance}") + # Show summary quality + if summaries: + non_empty_summaries = [s for s in summaries if s.strip()] + print(f" Generated summaries: {len(non_empty_summaries)}/{len(summaries)} non-empty") + for i, summary in enumerate(summaries): + if summary.strip() and relevance[i] == 1: + print(f" Summary {i+1}: {summary}...") + if reasoning_steps: + print(f" Reasoning: {reasoning_steps[:300]}...") + if not sufficient: + print(f" Feedback: {previous_feedback}") + print(f" Generated {len(sub_queries)} new queries") + + # Clear new_docs for next iteration (already added to kept_docs in CALL1 block above) + new_docs = [] + + # If sufficient, we're done + if sufficient: + if verbose: + print(f"\n ✓ Sufficient information found!") + if final_answer: + print(f" Answer: {final_answer[:200]}...") + iteration_times.append(time.perf_counter() - iteration_start) + break + + # If no queries generated, fall back to original query rather than stopping + if not sub_queries: + if verbose: + print(f"\n ⚠ No new queries generated, falling back to original query") + sub_queries = [original_query] + + if verbose: + print(f"\n New queries:") + for i, q in enumerate(sub_queries, 1): + print(f" {i}. {q}") + + # Step 2: Retrieve for each sub-query and track results + num_sub_queries = len(sub_queries) + #docs_per_subquery = max(1, top_k_retriever // num_sub_queries) + docs_per_subquery = max(1, top_k_retriever) + + iteration_results = [] + per_query_counts = [] # Track new docs found by each query + + # Calculate target docs per subquery after reranking + target_docs_per_subquery = max(3, top_k_retriever // num_sub_queries) + + for i, sub_query in enumerate(sub_queries, 1): + if verbose: + print(f"\n Retrieving for query {i}: {sub_query[:60]}...") + + query_start_count = len(new_docs) # Track docs before this query + + # Retrieve + if retrieval_strategy == "fixed_k": + results = rag_db.lookup(sub_query, k=docs_per_subquery) + else: + from retrieve.filter import filter + original_max_results = strategy_params.get("max_results", 20) + #adjusted_max_results = max(1, original_max_results // num_sub_queries) + adjusted_max_results = max(1, original_max_results) + strategy_params_copy = strategy_params.copy() + strategy_params_copy["max_results"] = adjusted_max_results + results = filter(rag_db, sub_query, method=retrieval_strategy, **strategy_params_copy) + + # Apply per-subquery reranking if enabled + if not no_rerank and len(results) > target_docs_per_subquery: + if verbose: + print(f" Reranking {len(results)} docs for this subquery to top {target_docs_per_subquery}...") + + # Extract contents for reranking + contents = [r.page_content for r in results] + scored_passages = rag_db.rerank(sub_query, contents) + + # Reorder results by reranking scores and take top-k + reranked_indices = [i for i, _ in sorted(enumerate(scored_passages), + key=lambda x: x[1][1], reverse=True)] + results = [results[idx] for idx in reranked_indices[:target_docs_per_subquery]] + + if verbose: + print(f" After reranking: keeping top {len(results)} docs") + elif len(results) > target_docs_per_subquery: + # No reranking, just limit to target + results = results[:target_docs_per_subquery] + + # Add to new_docs for evaluation (avoid duplicates) + for result in results: + if 'original_url' in result.metadata and result.metadata['original_url']: + url = result.metadata['original_url'] + if url not in all_retrieved_urls: + all_retrieved_urls.add(url) + new_docs.append((url, result.page_content)) + iteration_results.append(result) + + # Track how many NEW docs this query found + docs_found_by_query = len(new_docs) - query_start_count + per_query_counts.append(docs_found_by_query) + + if verbose: + print(f" Retrieved {len(results)} docs, {docs_found_by_query} new unique docs from this query") + for j, result in enumerate(results, 1): + url = result.metadata.get('original_url', 'N/A') + passage = result.page_content[:300].replace('\n', ' ') + print(f" [{j}] {url}\n {passage}...") + + # Add queries and their results to history + for sub_query, count in zip(sub_queries, per_query_counts): + query_history.append(sub_query) + query_results.append(count) + + if verbose: + print(f" Total kept docs: {len(kept_docs)}, new docs to evaluate: {len(new_docs)}") + + iteration_time = time.perf_counter() - iteration_start + iteration_times.append(iteration_time) + + if iteration >= max_iterations: + if verbose: + print(f"\n ⚠ Maximum iterations reached") + break + + # Final processing + total_time = time.perf_counter() - start_time + + # Extract URLs from kept_docs + retrieved_urls = [] + for doc in kept_docs: + if len(doc) >= 3: + retrieved_urls.append(doc[0]) # url is first element + elif len(doc) == 2: # Handle old format for backward compatibility + retrieved_urls.append(doc[0]) # url is first element + + # Limit to top_k_reranking (reranking already done per-subquery) + retrieved_urls = retrieved_urls[:top_k_reranking] + + # Calculate metrics + from evaluation import calculate_retrieval_metrics + expected_set = set(url for url in expected_urls if url and url.strip()) + metrics = calculate_retrieval_metrics(list(expected_set), retrieved_urls) + + # Add iteration statistics + metrics.update({ + 'total_time': total_time, + 'num_iterations': iteration, + 'total_queries': len(query_history), + 'final_docs_count': len(retrieved_urls), + 'sufficient': sufficient, + 'avg_iteration_time': sum(iteration_times) / len(iteration_times) if iteration_times else 0, + 'llm_answer': final_answer, + }) + + # Print final results + if verbose: + print(f"\n{'='*80}") + print(f"MULTI-SHOT RETRIEVAL RESULTS") + print(f"{'='*80}") + print(f"Original Query: {original_query[:100]}...") + print(f"Iterations: {iteration}") + print(f"Total queries issued: {len(query_history)}") + print(f"Sufficient: {'Yes' if sufficient else 'No'}") + if final_answer: + print(f"LLM Answer: {final_answer}") + if expected_answer: + print(f"Expected Answer: {expected_answer}") + print(f"Expected ({len(expected_set)}): {sorted(list(expected_set)[:3])}{'...' if len(expected_set) > 3 else ''}") + print(f"Retrieved ({len(retrieved_urls)} unique docs): {retrieved_urls[:3]}{'...' if len(retrieved_urls) > 3 else ''}") + matches = len(expected_set.intersection(set(retrieved_urls))) + print(f"Matches: {matches}") + print(f"\nMetrics:") + print(f" P@N: {metrics.get('precision@N', 0.0):.3f}") + print(f" R@N: {metrics.get('recall@N', 0.0):.3f}") + print(f" F1@N: {metrics.get('f1@N', 0.0):.3f}") + print(f" MAP: {metrics.get('average_precision', 0.0):.3f}") + print(f"\nTiming:") + print(f" Avg per iteration: {metrics['avg_iteration_time']*1000:.1f}ms") + print(f" Total: {total_time*1000:.1f}ms") + print(f"{'='*80}\n") + + return metrics + + +def run_multi_shot_evaluation(rag_db, dataset_path: str, + max_sub_queries: int = 3, + top_k_retriever: int = 10, + top_k_reranking: int = 10, + max_queries: Optional[int] = None, + no_rerank: bool = False, + retrieval_strategy: str = "fixed_k", + reasoning_effort: str = "medium", + detailed_analysis: bool = False, + difficulty: int = 0, + max_iterations: int = 10, + llm_config: Optional[Dict[str, Any]] = None, + logger: Optional[LLMLogger] = None, + num_workers: int = 1, + **strategy_params) -> Dict[str, float]: + """ + Run multi-shot evaluation on a dataset. + + Args: + rag_db: RAG database instance + dataset_path: Path to dataset TSV file + max_sub_queries: Maximum number of sub-queries per query + top_k_retriever: Number of documents to retrieve per sub-query + top_k_reranking: Number of documents after final reranking + max_queries: Maximum number of queries to evaluate + no_rerank: Skip reranking step + retrieval_strategy: Strategy for retrieval + reasoning_effort: LLM reasoning level + detailed_analysis: Enable detailed complexity-based analysis + difficulty: Minimum number of answer links required (0 = no filtering) + max_iterations: Maximum iterations for iterative retrieval (default: 10) + **strategy_params: Additional parameters for retrieval strategy + + Returns: + Dictionary of averaged metrics + """ + + df = pd.read_csv(dataset_path, sep='\t') + + # Filter by difficulty if specified + df = filter_dataset_by_difficulty(df, difficulty) + + if isinstance(max_queries, int) and max_queries > 0: + df = df.head(max_queries) + else: + max_queries = len(df) + + print(f"\n{'='*80}") + print(f"MULTI-SHOT EVALUATION") + print(f"{'='*80}") + print(f"Dataset: {dataset_path}") + print(f"Queries: {max_queries}") + print(f"Max sub-queries: {max_sub_queries}") + print(f"Retrieval strategy: {retrieval_strategy}") + print(f"LLM reasoning effort: {reasoning_effort}") + print(f"Detailed analysis: {detailed_analysis}") + print(f"Max iterations: {max_iterations}") + if difficulty > 0: + print(f"Difficulty filter: >= {difficulty} answer links") + print(f"{'='*80}\n") + + total_metrics = {} + valid_queries = 0 + all_query_metrics = [] # For detailed analysis + all_results = [] # For per-query results (used by evaluate.py) + + # Prepare work items + work_items = [] + for idx, row in df.iterrows(): + expected_urls = [] + for col in df.columns: + if col.startswith('wikipedia_link_') and pd.notna(row[col]): + expected_urls.append(row[col].strip()) + expected_answer = row.get('Answer', '').strip() if 'Answer' in row and pd.notna(row.get('Answer')) else "" + if expected_urls: + work_items.append((idx, row['Prompt'], expected_urls, expected_answer)) + + def process_single_query(item): + idx, prompt, expected_urls, expected_answer = item + print(f"\n[Query {idx+1}/{max_queries}]") + + if logger: + logger.start_query(str(idx), prompt) + + metrics = multi_shot_retrieval( + rag_db, prompt, expected_urls, + expected_answer=expected_answer, + max_sub_queries=max_sub_queries, + top_k_retriever=top_k_retriever, + top_k_reranking=top_k_reranking, + max_iterations=max_iterations, + no_rerank=no_rerank, + retrieval_strategy=retrieval_strategy, + verbose=True, + reasoning_effort=reasoning_effort, + llm_config=llm_config, + logger=logger, + **strategy_params + ) + + if logger: + logger.end_query( + retrieval_results={ + "retrieved_urls": metrics.get('retrieved_urls', []), + "correct_urls": expected_urls, + "precision": metrics.get('precision', 0), + "recall": metrics.get('recall', 0), + "f1": metrics.get('f1', 0) + }, + answer_results={ + "llm_answer": metrics.get('llm_answer', ''), + "ground_truth_answer": expected_answer + } + ) + + result = { + "prompt": prompt, + "llm_answer": metrics.get('llm_answer', ''), + "ground_truth": expected_answer, + } + return idx, metrics, result + + if num_workers <= 1: + # Sequential execution (original behavior) + for item in work_items: + idx, metrics, result = process_single_query(item) + for metric_name, value in metrics.items(): + if metric_name not in total_metrics: + total_metrics[metric_name] = 0.0 + if isinstance(value, (int, float)): + total_metrics[metric_name] += value + valid_queries += 1 + all_results.append(result) + if detailed_analysis: + all_query_metrics.append(metrics.copy()) + else: + # Parallel execution with thread pool + print(f"\n Using {num_workers} parallel workers") + with ThreadPoolExecutor(max_workers=num_workers) as executor: + futures = {executor.submit(process_single_query, item): item for item in work_items} + for future in as_completed(futures): + try: + idx, metrics, result = future.result() + for metric_name, value in metrics.items(): + if metric_name not in total_metrics: + total_metrics[metric_name] = 0.0 + if isinstance(value, (int, float)): + total_metrics[metric_name] += value + valid_queries += 1 + all_results.append(result) + if detailed_analysis: + all_query_metrics.append(metrics.copy()) + except Exception as e: + print(f" Error processing query: {e}") + import traceback + traceback.print_exc() + + if valid_queries > 0: + # Calculate averages + avg_metrics = {name: total / valid_queries for name, total in total_metrics.items()} + + # Print summary + print(f"\n{'='*80}") + print(f"MULTI-SHOT EVALUATION SUMMARY ({valid_queries} queries)") + print(f"{'='*80}") + print(f"\nPRECISION METRICS:") + print(f" Precision@N: {avg_metrics.get('precision@N', 0.0):.3f}") + print(f"\nRECALL METRICS:") + print(f" Recall@N: {avg_metrics.get('recall@N', 0.0):.3f}") + print(f"\nF1 METRICS:") + print(f" F1@N: {avg_metrics.get('f1@N', 0.0):.3f}") + print(f"\nRANKING METRICS:") + print(f" Mean Average Precision: {avg_metrics.get('average_precision', 0.0):.3f}") + print(f"\nRETRIEVAL STATISTICS:") + print(f" Avg Sub-queries: {avg_metrics.get('num_sub_queries', 0.0):.1f}") + print(f" Avg Passages Retrieved: {avg_metrics.get('retrieved_passages_count', 0.0):.1f}") + print(f" Avg Unique Docs (N): {avg_metrics.get('retrieved_docs_count', 0.0):.1f}") + print(f"\nTIMING:") + print(f" Avg Decomposition Time: {avg_metrics.get('decomposition_time', 0.0)*1000:.1f}ms") + print(f" Avg Retrieval Time: {avg_metrics.get('retrieval_time', 0.0)*1000:.1f}ms") + if avg_metrics.get('reranking_time', 0.0) > 0: + print(f" Avg Reranking Time: {avg_metrics.get('reranking_time', 0.0)*1000:.1f}ms") + print(f" Avg Total Time: {avg_metrics.get('total_time', 0.0)*1000:.1f}ms") + print(f"{'='*80}\n") + + # Print detailed analysis if requested + if detailed_analysis and all_query_metrics: + from evaluation import _print_detailed_analysis + _print_detailed_analysis(df, all_query_metrics, valid_queries) + + avg_metrics['_per_query_results'] = all_results + return avg_metrics + else: + print("No valid queries found!") + return {} + + +if __name__ == "__main__": + args = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter, + description="Multi-shot retrieval with query decomposition") + + # Add all standard parameters + add_all_args(args) + + # Add multi-shot specific parameters + args.add_argument('--max-sub-queries', type=int, default=3, + help='Maximum number of sub-queries to generate (default: 3)') + args.add_argument('--reasoning', type=str, default='medium', + choices=['low', 'medium', 'high'], + help='LLM reasoning level for query decomposition (default: medium)') + args.add_argument('--max-iterations', type=int, default=10, + help='Maximum number of retrieval iterations (default: 10)') + args.add_argument('--num-workers', type=int, default=1, + help='Number of parallel query workers (default: 1, sequential)') + args.add_argument('--temperature', type=float, default=1.0, + help='LLM sampling temperature (default: 1.0)') + args.add_argument('--max-retries', type=int, default=5, + help='Max retries for LLM calls on rate limit/server errors (default: 5)') + args.add_argument('--output-dir', type=str, default='.', + help='Directory for output files (default: current directory)') + + # Special handling for --eval argument + for action in args._actions: + if '--eval' in action.option_strings: + action.type = lambda x: int(x) if x.isdigit() else True + action.const = True + break + + args = args.parse_args() + + # Set deterministic seeds + set_deterministic_seeds(args.seed) + + # Setup LLM configuration with auto-detection + llm_config = setup_llm_config(args) + llm_config['temperature'] = args.temperature + llm_config['max_retries'] = args.max_retries + print(f"LLM Config: {llm_config}") + + # Setup device-specific environment + device_config = get_device_config() + print(f"Device Config: {device_config}") + + # Initialize database + if args.retrieval_method == "bm25": + db_class = BM25DB + else: + db_class = VectorDB + + if args.database is None: + args.database = db_class.get_default_db_name() + + db_file_path = args.database if args.database.endswith('.db') else f"{args.database}.db" + db_base_name = args.database.replace('.db', '') if args.database.endswith('.db') else args.database + + rag_db = db_class( + retriever_model=args.retriever_model, + reranker_model=args.reranker_model, + device=args.device, + k1=args.bm25_k1, b=args.bm25_b, method=args.bm25_method, + database=db_base_name, + delta=args.bm25_delta, backend=args.bm25_backend, + stopwords=args.bm25_stopwords, + show_progress=args.bm25_show_progress, stemmer=args.bm25_stemmer, + vector_index_method=args.vector_index_method, + ivf_nprobe=args.ivf_nprobe, + load_embeddings=args.load_embeddings, + num_embedding_devices=args.num_embedding_devices, + embedding_device=args.embedding_device, + reranker_device=args.reranker_device, + benchmark=args.benchmark + ) + + # Load database + if os.path.exists(db_file_path): + print(f"Loading existing database from {db_file_path}") + rag_db.from_serialized(db_file_path) + else: + raise ValueError(f"Database not found: {db_file_path}. Please create it first using single_shot_retrieval.py") + + # Build strategy parameters + strategy_params = {"max_results": args.max_results} + if args.retrieval_strategy == "top_p": + strategy_params["p"] = args.top_p + elif args.retrieval_strategy == "relative": + strategy_params["ratio"] = args.relative_ratio + + # Initialize LLM logger with incremental writing + experiment_start_time = datetime.now() + os.makedirs(args.output_dir, exist_ok=True) + log_filename = os.path.join(args.output_dir, f"llm_logs_multi_shot_{experiment_start_time.strftime('%Y%m%d_%H%M%S')}.json") + + # Determine chunk size from database name + chunk_size = 768 # default + if 'len' in db_base_name: + import re as re_module + match = re_module.search(r'len(\d+)', db_base_name) + if match: + chunk_size = int(match.group(1)) + + # Create logger with file and metadata for incremental writing + llm_logger = LLMLogger( + output_file=log_filename, + experiment_metadata={ + "experiment_name": f"multi_shot_{db_base_name}_n{{queries}}", # Will be updated + "timestamp_start": experiment_start_time.isoformat(), + "timestamp_end": "in_progress", + "retrieval_method": args.retrieval_method, + "retrieval_mode": "multi_shot", + "max_iterations": args.max_iterations, + "max_sub_queries": args.max_sub_queries, + "top_k_retriever": args.top_k_retriever, + "rerank_per_subquery": f"top_k_retriever / num_sub_queries (= {args.top_k_retriever} / 3 = {args.top_k_retriever // 3})", + "chunk_size": chunk_size, + "device": args.device, + "grader_model": llm_config.get('grader_model_name', 'unknown'), + "sufficiency_checker_model": llm_config.get('sufficiency_model_name', 'unknown'), + "query_model": llm_config.get('query_model_name', 'unknown'), + "answer_generator_model": llm_config.get('sufficiency_model_name', 'unknown'), + "total_queries": "in_progress" + } + ) + print(f"LLM logs will be written incrementally to: {log_filename}") + + + # Setup threading infrastructure if parallel workers requested + if args.num_workers > 1: + print(f"Enabling parallel execution with {args.num_workers} workers") + rag_db.enable_threading() + + # Run evaluation or single query + if args.eval: + max_queries = args.eval if isinstance(args.eval, int) and not isinstance(args.eval, bool) and args.eval > 0 else None + + metrics = run_multi_shot_evaluation( + rag_db, args.dataset, + max_sub_queries=args.max_sub_queries, + top_k_retriever=args.top_k_retriever, + top_k_reranking=args.top_k_reranking, + max_queries=max_queries, + no_rerank=args.no_rerank, + retrieval_strategy=args.retrieval_strategy, + reasoning_effort=args.reasoning, + detailed_analysis=True, # Enable detailed complexity analysis + difficulty=args.difficulty, + max_iterations=args.max_iterations, + llm_config=llm_config, + logger=llm_logger, + num_workers=args.num_workers, + **strategy_params + ) + + # Save results + per_query_results = metrics.pop('_per_query_results', []) + results_data = { + "multi_shot": True, + "max_sub_queries": args.max_sub_queries, + "reasoning_effort": args.reasoning, + "metrics": metrics, + "results": per_query_results, + } + + result_path = os.path.join(args.output_dir, "result_multi_shot.json") + with open(result_path, "w") as f: + json.dump(results_data, f, indent=2) + + print(f"Results saved to {result_path}") + + # Finalize LLM logs (update metadata with final values) + experiment_end_time = datetime.now() + llm_logger.experiment_metadata.update({ + "experiment_name": f"multi_shot_{db_base_name}_n{max_queries or len(per_query_results)}", + "timestamp_end": experiment_end_time.isoformat(), + "total_queries": max_queries or len(per_query_results) + }) + + # Write final version with updated metadata + llm_logger.save() + print(f"LLM logs finalized: {log_filename}") + + else: + # Single query multi-shot retrieval + if not args.query: + args.query = "Who won the French Open Mens Singles tournament the year that New York City FC won their first MLS Cup title?" + + print(f"\nRunning multi-shot retrieval for single query...") + + # Start logging for single query + llm_logger.start_query("0", args.query) + + result = multi_shot_retrieval( + rag_db, args.query, expected_urls=[], + max_sub_queries=args.max_sub_queries, + top_k_retriever=args.top_k_retriever, + top_k_reranking=args.top_k_reranking, + no_rerank=args.no_rerank, + retrieval_strategy=args.retrieval_strategy, + verbose=True, + reasoning_effort=args.reasoning, + llm_config=llm_config, + logger=llm_logger, + **strategy_params + ) + + # End logging for single query + llm_logger.end_query( + retrieval_results={ + "retrieved_urls": result.get('retrieved_urls', []), + "correct_urls": [], + "precision": result.get('precision', 0), + "recall": result.get('recall', 0), + "f1": result.get('f1', 0) + }, + answer_results={ + "llm_answer": result.get('llm_answer', ''), + "ground_truth_answer": "" + } + ) + + # Finalize LLM logs (update metadata with final values) + experiment_end_time = datetime.now() + llm_logger.experiment_metadata.update({ + "experiment_name": f"single_query_{db_base_name}", + "timestamp_end": experiment_end_time.isoformat(), + "total_queries": 1 + }) + + # Write final version + llm_logger.save() + print(f"LLM logs finalized: {log_filename}") + + # Cleanup reranker child process + rq = rag_db._reranker_queue + if rq is not None: + avg_ms = rq.total_latency_ms / rq.total_requests if rq.total_requests else 0 + print(f"Reranker stats: {rq.total_requests} requests, {rq.total_documents} docs, {rq.total_latency_ms:.0f}ms total, {avg_ms:.1f}ms/request avg") + rag_db.shutdown_reranker() diff --git a/e2e/oracle_single_shot.py b/e2e/oracle_single_shot.py new file mode 100644 index 0000000000..ccb6e2ce67 --- /dev/null +++ b/e2e/oracle_single_shot.py @@ -0,0 +1,530 @@ +#!/usr/bin/env python3 +""" +Oracle Single Shot - Generate LLM answers using ground truth Wikipedia articles. + +This script reads queries from frames_dataset.tsv, loads the corresponding +Wikipedia articles from wiki_articles folder, and generates answers using +an LLM service with the oracle documents as context. + +Features: +- Batch processing of LLM requests (default batch size: 16) +- Checkpointing: saves progress after each batch to pickle file +- Resume capability: skips already processed queries +- Retry failed requests when all queries are processed +- Handles missing documents gracefully +""" + +import argparse +import ast +import json +import os +import pickle +import re +import time +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path +from typing import Dict, List, Optional, Tuple +import pandas as pd +import requests + + +DEFAULT_CHECKPOINT_FILE = "oracle_checkpoint.pkl" +DEFAULT_SERVICE_URL = "http://localhost:8123/v1/chat/completions" +#DEFAULT_MODEL_NAME = "/mnt/weka/data/pytorch/llama3.3/Meta-Llama-3.3-70B-Instruct" +#DEFAULT_MODEL_NAME = "/mnt/weka/data/pytorch/llama3.1/Meta-Llama-3.1-405B-Instruct-v2" +DEFAULT_MODEL_NAME = "/model/gpt-oss-120b-mxfp4" +DEFAULT_BATCH_SIZE = 1 +DEFAULT_TIMEOUT = 2400 +# For reasoning model, it should be large enough +DEFAULT_MAX_TOKENS = 10*1024 +MAX_TOTAL_CHARS = 400000 # total char budget split across all docs per query (~100K tokens @ 4 chars/token) + +# Global cache for URL to filename mapping +_url_to_file_cache: Optional[Dict[str, Path]] = None + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Oracle Single Shot: Generate LLM answers using ground truth documents", + formatter_class=argparse.RawDescriptionHelpFormatter + ) + parser.add_argument( + "--dataset", + type=str, + default="data/frames_dataset.tsv", + help="Path to frames_dataset.tsv (default: data/frames_dataset.tsv)" + ) + parser.add_argument( + "--wiki-articles-dir", + type=str, + default="wiki_articles", + help="Path to wiki_articles directory (default: wiki_articles)" + ) + parser.add_argument( + "--checkpoint-file", + type=str, + default=DEFAULT_CHECKPOINT_FILE, + help=f"Path to checkpoint pickle file (default: {DEFAULT_CHECKPOINT_FILE})" + ) + parser.add_argument( + "--service-url", + type=str, + default=DEFAULT_SERVICE_URL, + help=f"LLM service URL (default: {DEFAULT_SERVICE_URL})" + ) + parser.add_argument( + "--model-name", + type=str, + default=DEFAULT_MODEL_NAME, + help=f"Model name (default: {DEFAULT_MODEL_NAME})" + ) + parser.add_argument( + "--batch-size", + type=int, + default=DEFAULT_BATCH_SIZE, + help=f"Batch size for LLM requests (default: {DEFAULT_BATCH_SIZE})" + ) + parser.add_argument( + "--max-tokens", + type=int, + default=DEFAULT_MAX_TOKENS, + help=f"Maximum tokens for LLM response (default: {DEFAULT_MAX_TOKENS})" + ) + parser.add_argument( + "--timeout", + type=int, + default=DEFAULT_TIMEOUT, + help=f"Timeout in seconds for LLM generation requests (default: {DEFAULT_TIMEOUT})" + ) + parser.add_argument( + "--enable-thinking", + action="store_true", + default=False, + help="Enable thinking/reasoning in the model via chat_template_kwargs (default: False)" + ) + parser.add_argument( + "--reasoning-effort", + type=str, + default=None, + choices=["low", "medium", "high"], + help="Reasoning effort level to pass to the model (default: not set)" + ) + parser.add_argument( + "--retry-failed", + action="store_true", + help="Retry failed requests (automatically enabled when all queries processed)" + ) + parser.add_argument( + "--max-queries", + type=int, + default=None, + help="Maximum number of queries to process (default: all)" + ) + return parser.parse_args() + + +def build_url_to_file_cache(wiki_dir: Path) -> Dict[str, Path]: + """ + Build a cache mapping Wikipedia URLs to their corresponding .txt files. + Uses JSON metadata files to get accurate URL mapping. + """ + cache = {} + + print(f"Building URL cache from {wiki_dir}...") + json_files = list(wiki_dir.glob("*.json")) + + for json_file in json_files: + try: + with open(json_file, 'r') as f: + data = json.load(f) + + # Get URL without fragment + url = data.get('url', '') + source_url = data.get('source_url', '') + + # Remove fragment from source_url if present + if '#' in source_url: + source_url = source_url.split('#')[0] + + # Get corresponding .txt file + txt_file = json_file.with_suffix('.txt') + if txt_file.exists(): + # Map both url and source_url (without fragment) to the file + if url: + cache[url] = txt_file + if source_url and source_url != url: + cache[source_url] = txt_file + except Exception as e: + # Skip files with errors + continue + + print(f"Cached {len(cache)} URL mappings from {len(json_files)} JSON files") + return cache + + +def find_wiki_article(url: str, wiki_dir: Path) -> Optional[Path]: + """ + Find the .txt file in wiki_articles that matches the Wikipedia URL. + Uses JSON metadata for accurate matching. + """ + global _url_to_file_cache + + if not url or "wikipedia.org/wiki/" not in url: + return None + + # Build cache on first call + if _url_to_file_cache is None: + _url_to_file_cache = build_url_to_file_cache(wiki_dir) + + # Remove fragment from URL if present + url_no_fragment = url.split('#')[0] + + # Look up in cache + if url in _url_to_file_cache: + return _url_to_file_cache[url] + elif url_no_fragment in _url_to_file_cache: + return _url_to_file_cache[url_no_fragment] + + return None + + +def load_wiki_articles(wiki_urls: List[str], wiki_dir: Path, max_chars: int = MAX_TOTAL_CHARS) -> Tuple[List[str], List[str], List[int]]: + """ + Load Wikipedia articles from wiki_articles folder. + max_chars is the TOTAL character budget shared across all docs for this query. + + Returns: + Tuple of (documents, file_paths, doc_lengths) + """ + documents = [] + file_paths = [] + doc_lengths = [] + + valid_urls = [u for u in wiki_urls if u] + n = max(1, len(valid_urls)) + per_doc_limit = max(500, max_chars // n) # distribute budget evenly + for url in wiki_urls: + # Find the actual file using URL + file_path = find_wiki_article(url, wiki_dir) + + if file_path and file_path.exists(): + try: + content = file_path.read_text(encoding="utf-8", errors="ignore") + truncated = content[:per_doc_limit] + documents.append(truncated) + file_paths.append(str(file_path)) + doc_lengths.append(len(truncated)) + except Exception as e: + print(f"Warning: Failed to read {file_path}: {e}") + documents.append("") + file_paths.append("") + doc_lengths.append(0) + else: + documents.append("") + file_paths.append("") + doc_lengths.append(0) + + return documents, file_paths, doc_lengths + + +def generate_llm_answer(query: str, documents: List[str], urls: List[str], llm_config: Dict) -> str: + """ + Generate LLM answer using the provided documents as context. + + This function is adapted from single_shot_retrieval.py _generate_llm_answer + """ + context_parts = [] + for idx, (doc, url) in enumerate(zip(documents, urls), 1): + if doc.strip(): + source = url or "Unknown source" + snippet = doc.strip() + context_parts.append(f"[{idx}] Source: {source}\n{snippet}") + + evidence_block = "\n\n".join(context_parts) if context_parts else "No supporting documents were retrieved." + + user_prompt = ( + "Answer the question using only the provided evidence." + " Respond with a single word or short phrase, or 'Unknown' if the evidence is insufficient.\n\n" + f"Question:\n{query}\n\nEvidence:\n{evidence_block}" + ) + + payload = { + "model": llm_config["model_name"], + "messages": [ + { + "role": "system", + "content": "You are a concise retrieval QA assistant who trusts the supplied context." + }, + {"role": "user", "content": user_prompt} + ], + "temperature": 0.0, + "max_tokens": llm_config["max_tokens"], + } + + if llm_config.get("enable_thinking"): + payload["chat_template_kwargs"] = {"enable_thinking": True} + + if llm_config.get("reasoning_effort"): + payload["reasoning_effort"] = llm_config["reasoning_effort"] + + response = requests.post(llm_config["service_url"], json=payload, timeout=llm_config["timeout"]) + response.raise_for_status() + data = response.json() + #from pprint import pprint + #pprint(data, indent=4) + return data["choices"][0]["message"]["content"].strip() + + +def load_checkpoint(checkpoint_file: Path) -> pd.DataFrame: + """Load checkpoint from pickle file.""" + if checkpoint_file.exists(): + with open(checkpoint_file, "rb") as f: + df = pickle.load(f) + # Ensure it's a DataFrame + if isinstance(df, dict): + # Handle old format for backwards compatibility + df = pd.DataFrame(df.get("results", [])) + return df + return pd.DataFrame() + + +def save_checkpoint(checkpoint_file: Path, df: pd.DataFrame): + """Save checkpoint DataFrame to pickle file.""" + with open(checkpoint_file, "wb") as f: + pickle.dump(df, f) + + +def parse_wiki_links(wiki_links_str: str) -> List[str]: + """Parse the wiki_links column which is stored as a string representation of a list.""" + try: + # Use ast.literal_eval to safely parse the string as a Python literal + return ast.literal_eval(wiki_links_str) + except: + return [] + + +def process_single( + idx: int, + query: str, + ground_truth: str, + wiki_urls: List[str], + wiki_dir: Path, + llm_config: Dict +) -> Dict: + """Process a single query and return the result dict.""" + result = { + "index": idx, + "query": query, + "ground_truth": ground_truth, + "wiki_urls": str(wiki_urls), + "wiki_file_paths": "", + "doc_lengths": "", + "total_doc_length": 0, + "num_docs": len(wiki_urls), + "num_missing_docs": 0, + "llm_answer": "", + "success": False, + "failure_reason": "" + } + + try: + documents, file_paths, doc_lengths = load_wiki_articles(wiki_urls, wiki_dir) + result["wiki_file_paths"] = str(file_paths) + result["doc_lengths"] = str(doc_lengths) + result["total_doc_length"] = sum(doc_lengths) + + valid_docs = [d for d in documents if d.strip()] + missing_count = len(wiki_urls) - len(valid_docs) + result["num_missing_docs"] = missing_count + + if missing_count > 0: + print(f" Query {idx}: {missing_count}/{len(wiki_urls)} documents missing") + + llm_answer = generate_llm_answer(query, documents, wiki_urls, llm_config) + result["llm_answer"] = llm_answer + result["success"] = True + + except Exception as e: + result["failure_reason"] = str(e) + print(f" Query {idx}: Failed - {e}") + + return result + + +def process_batch( + batch_data: List[Tuple[int, str, str, List[str]]], + wiki_dir: Path, + llm_config: Dict +) -> List[Dict]: + """ + Process a batch of queries in parallel. + + Args: + batch_data: List of (index, query, answer, wiki_urls) + wiki_dir: Path to wiki_articles directory + llm_config: LLM configuration dict + + Returns: + List of result dictionaries sorted by original order + """ + futures_map = {} + with ThreadPoolExecutor(max_workers=len(batch_data)) as executor: + for idx, query, ground_truth, wiki_urls in batch_data: + future = executor.submit(process_single, idx, query, ground_truth, wiki_urls, wiki_dir, llm_config) + futures_map[future] = idx + + results_map = {} + for future in as_completed(futures_map): + result = future.result() + results_map[result["index"]] = result + + # Return in original batch order + return [results_map[idx] for idx, _, _, _ in batch_data] + + +def main(): + args = parse_args() + + # Setup paths + dataset_path = Path(args.dataset) + wiki_dir = Path(args.wiki_articles_dir) + checkpoint_file = Path(args.checkpoint_file) + + # Validate paths + if not dataset_path.exists(): + raise FileNotFoundError(f"Dataset not found: {dataset_path}") + if not wiki_dir.exists(): + raise FileNotFoundError(f"Wiki articles directory not found: {wiki_dir}") + + # Pre-build URL cache once before threads start + global _url_to_file_cache + _url_to_file_cache = build_url_to_file_cache(wiki_dir) + + # Load dataset + print(f"Loading dataset from {dataset_path}...") + df = pd.read_csv(dataset_path, sep="\t") + + # Apply max_queries limit if specified + if args.max_queries: + df = df.head(args.max_queries) + + print(f"Total queries in dataset: {len(df)}") + + # Load checkpoint + checkpoint_df = load_checkpoint(checkpoint_file) + processed_indices = set(checkpoint_df["index"].tolist()) if not checkpoint_df.empty else set() + + print(f"Already processed: {len(processed_indices)} queries") + + # LLM configuration + llm_config = { + "service_url": args.service_url, + "model_name": args.model_name, + "max_tokens": args.max_tokens, + "timeout": args.timeout, + "enable_thinking": args.enable_thinking, + "reasoning_effort": args.reasoning_effort, + } + + # Determine which queries to process + if args.retry_failed: + # Retry only failed queries + if not checkpoint_df.empty: + failed_df = checkpoint_df[checkpoint_df["success"] == False] + queries_to_process = [(int(row["index"]), df.iloc[int(row["index"])]["Prompt"], + df.iloc[int(row["index"])]["Answer"], + parse_wiki_links(df.iloc[int(row["index"])]["wiki_links"])) + for _, row in failed_df.iterrows()] + print(f"Retrying {len(queries_to_process)} failed queries...") + else: + queries_to_process = [] + else: + # Process unprocessed queries + queries_to_process = [] + for idx, row in df.iterrows(): + if idx not in processed_indices: + wiki_urls = parse_wiki_links(row["wiki_links"]) + queries_to_process.append((idx, row["Prompt"], row["Answer"], wiki_urls)) + + print(f"Processing {len(queries_to_process)} new queries...") + + if not queries_to_process: + # If no new queries and all done, check for failed ones + if not args.retry_failed: + failed_count = len(checkpoint_df[checkpoint_df["success"] == False]) if not checkpoint_df.empty else 0 + if failed_count > 0: + print(f"\nAll new queries processed. {failed_count} queries failed.") + print("Run with --retry-failed to retry failed queries.") + else: + print("\nAll queries successfully processed!") + else: + print("No failed queries to retry!") + return + + # Process in batches + batch_size = args.batch_size + total_batches = (len(queries_to_process) + batch_size - 1) // batch_size + + print(f"Batch size: {batch_size}") + print(f"Total batches: {total_batches}") + print(f"Service URL: {llm_config['service_url']}") + print(f"Model: {llm_config['model_name']}\n") + + for batch_idx in range(total_batches): + start_idx = batch_idx * batch_size + end_idx = min(start_idx + batch_size, len(queries_to_process)) + batch = queries_to_process[start_idx:end_idx] + + print(f"Processing batch {batch_idx + 1}/{total_batches} " + f"(queries {start_idx + 1}-{end_idx})...") + + # Process batch + batch_results = process_batch(batch, wiki_dir, llm_config) + + # Convert batch results to DataFrame + batch_df = pd.DataFrame(batch_results) + + # Update checkpoint + if args.retry_failed: + # For retry, update existing results + # Remove old entries for these indices + indices_to_update = batch_df["index"].tolist() + checkpoint_df = checkpoint_df[~checkpoint_df["index"].isin(indices_to_update)] + # Append new results + checkpoint_df = pd.concat([checkpoint_df, batch_df], ignore_index=True) + else: + # For new queries, append results + checkpoint_df = pd.concat([checkpoint_df, batch_df], ignore_index=True) + + # Sort by index for consistency + checkpoint_df = checkpoint_df.sort_values("index").reset_index(drop=True) + + # Save checkpoint after each batch + save_checkpoint(checkpoint_file, checkpoint_df) + print(f" Checkpoint saved to {checkpoint_file}") + + # Show batch statistics + success_count = batch_df["success"].sum() + print(f" Batch success rate: {success_count}/{len(batch_results)}\n") + + # Final statistics + print("\n" + "="*60) + print("Processing complete!") + print("="*60) + + total_processed = len(checkpoint_df) + total_success = checkpoint_df["success"].sum() + total_failed = total_processed - total_success + + print(f"Total queries processed: {total_processed}") + print(f"Successful: {total_success}") + print(f"Failed: {total_failed}") + + if total_failed > 0: + print(f"\nRun with --retry-failed to retry {total_failed} failed queries.") + + print(f"\nResults saved to: {checkpoint_file}") + + +if __name__ == "__main__": + main() diff --git a/e2e/params.py b/e2e/params.py new file mode 100644 index 0000000000..13f85d17aa --- /dev/null +++ b/e2e/params.py @@ -0,0 +1,870 @@ +""" +Centralized Parameter Definitions + +This module provides a single source of truth for all parameters +used across single_shot_retrieval.py, optimize_retrieval.py, and other scripts. + +Usage: + from params import add_all_args, add_common_args, add_retrieval_args + + parser = argparse.ArgumentParser() + add_all_args(parser) # Add all parameters + # OR + add_common_args(parser) # Add common args (ingest, database, query, etc.) + add_retrieval_args(parser) # Add retrieval-specific args + args = parser.parse_args() +""" + +from typing import Dict, Any, List, Optional +import argparse + +# ============================================================================ +# Parameter Definitions +# ============================================================================ +class ParamDef: + """Parameter definition with metadata.""" + def __init__(self, + name: str, + arg_names: List[str], + type: type, + default: Any, + help: str, + choices: Optional[List[Any]] = None, + nargs: Optional[str] = None, + action: Optional[str] = None, + category: str = "general", + applies_to: List[str] = None, + optuna_suggest: Optional[Dict[str, Any]] = None): + """ + Args: + name: Internal parameter name + arg_names: List of CLI argument names (e.g., ['--top-p', '--top_p']) + type: Parameter type (int, float, str, bool) + default: Default value + help: Help text + choices: Valid choices (for categorical parameters) + nargs: argparse nargs value + action: argparse action (e.g., 'store_true', 'store_false') + category: Parameter category (general, bm25, vector, strategy, reranking) + applies_to: List of methods this applies to (['bm25', 'vector', 'both']) + optuna_suggest: Optuna suggestion config (type, min, max, step, etc.) + """ + self.name = name + self.arg_names = arg_names + self.type = type + self.default = default + self.help = help + self.choices = choices + self.nargs = nargs + self.action = action + self.category = category + self.applies_to = applies_to or ["both"] + self.optuna_suggest = optuna_suggest + + def add_to_parser(self, parser: argparse.ArgumentParser): + """Add this parameter to an argument parser.""" + kwargs = { + 'help': self.help, + 'default': self.default, + } + + if self.action: + kwargs['action'] = self.action + else: + kwargs['type'] = self.type + + if self.choices: + kwargs['choices'] = self.choices + + if self.nargs: + kwargs['nargs'] = self.nargs + + parser.add_argument(*self.arg_names, **kwargs) + + def suggest_value(self, trial): + """Suggest a value for Optuna trial.""" + if not self.optuna_suggest: + raise ValueError(f"No optuna_suggest config for parameter {self.name}") + + config = self.optuna_suggest + suggest_type = config['type'] + + if suggest_type == 'float': + return trial.suggest_float( + self.name, + config['min'], + config['max'], + step=config.get('step') + ) + elif suggest_type == 'int': + return trial.suggest_int( + self.name, + config['min'], + config['max'], + step=config.get('step', 1) + ) + elif suggest_type == 'categorical': + return trial.suggest_categorical(self.name, config['choices']) + else: + raise ValueError(f"Unknown suggest type: {suggest_type}") + + +# ============================================================================ +# Common Script Parameters +# ============================================================================ + +COMMON_PARAMS = [ + ParamDef( + name="ingest", + arg_names=["--ingest"], + type=str, + default=None, + help="Path to ingest data from:\n - JSON array file: 'passage' will be the passage text, all other keys will be metadata\n Example: [{'index': int, 'pdf_filename': str, 'passage': str}]\n - Folder: For BM25, ingests all .txt files in the folder\nIgnored if --database is provided", + category="common", + applies_to=["both"] + ), + ParamDef( + name="database", + arg_names=["--database", "--db"], + type=str, + default="vector.db", + help="Path to the database file\nIf provided, --ingest will be ignored\nDefault: 'bm25.db' for BM25, 'vector.db' for vector", + category="common", + applies_to=["both"] + ), + ParamDef( + name="query", + arg_names=["--query"], + type=str, + default="Who won the French Open Mens Singles tournament the year that New York City FC won their first MLS Cup title?", + help="Query to search for", + category="common", + applies_to=["both"] + ), + ParamDef( + name="dataset", + arg_names=["--dataset"], + type=str, + default="data/frames_dataset.tsv", + help="Path to evaluation dataset file", + category="common", + applies_to=["both"] + ), + ParamDef( + name="eval", + arg_names=["--eval"], + type=str, # Special handling needed for this one + default=None, + help="Run evaluation on dataset. Optionally specify number of queries to evaluate (e.g., --eval 100)", + nargs="?", + category="common", + applies_to=["both"] + ), + ParamDef( + name="difficulty", + arg_names=["--difficulty"], + type=int, + default=0, + help="Minimum number of answer links (difficulty level). Filters out queries with fewer answer links. Default is 0 (no filtering)", + category="common", + applies_to=["both"] + ), + ParamDef( + name="retriever_model", + arg_names=["--retriever_model"], + type=str, + default="intfloat/e5-base-v2", + help="Model to use for embedding-based retrieval", + category="common", + applies_to=["vector"] + ), + ParamDef( + name="reranker_model", + arg_names=["--reranker_model"], + type=str, + default="colbert-ir/colbertv2.0", + help="Model to use for reranking (currently unused)", + category="common", + applies_to=["both"] + ), + ParamDef( + name="retrieval_method", + arg_names=["--retrieval_method"], + type=str, + default="vector", + help="Retrieval method: 'bm25' for BM25 lexical search, 'vector' for dense vector search", + choices=["bm25", "vector"], + category="common", + applies_to=["both"] + ), + ParamDef( + name="load_embeddings", + arg_names=["--load-embeddings"], + type=bool, + default=False, + help="Load embeddings from .emb.pkl cache if available (default: False)", + action="store_true", + category="common", + applies_to=["vector"] + ), + ParamDef( + name="no_save", + arg_names=["--no-save"], + type=bool, + default=False, + help="Skip saving database to disk (useful for optimization trials)", + action="store_true", + category="common", + applies_to=["both"] + ), + ParamDef( + name="no_rerank", + arg_names=["--no-rerank"], + type=bool, + default=False, + help="Skip reranking step for fair comparison between retrieval methods", + action="store_true", + category="common", + applies_to=["both"] + ), + ParamDef( + name="max_results", + arg_names=["--max_results"], + type=int, + default=20, + help="Maximum results to consider for adaptive methods", + category="common", + applies_to=["both"] + ), + ParamDef( + name="seed", + arg_names=["--seed"], + type=int, + default=42, + help="Random seed for reproducible results (default: 42)", + category="common", + applies_to=["both"] + ), + ParamDef( + name="benchmark", + arg_names=["--benchmark"], + type=bool, + default=False, + help="Run ingestion performance benchmarking", + action="store_true", + category="common", + applies_to=["both"] + ), +] + +# ============================================================================ +# General Parameters +# ============================================================================ + +GENERAL_PARAMS = [ + ParamDef( + name="device", + arg_names=["--device"], + type=str, + default="auto", + help="Device to use (auto/cuda/rocm/xpu/hpu/cpu)", + choices=["auto", "cuda", "rocm", "xpu", "hpu", "cpu"], + category="general", + applies_to=["both"] + ), + ParamDef( + name="embedding_device", + arg_names=["--embedding-device"], + type=str, + default=None, + help="Override device for the embedding model (default: inherit --device)", + choices=["auto", "cuda", "rocm", "xpu", "hpu", "cpu"], + category="general", + applies_to=["vector"] + ), + ParamDef( + name="reranker_device", + arg_names=["--reranker-device"], + type=str, + default=None, + help="Override device for the reranker model (default: inherit --device)", + choices=["auto", "cuda", "rocm", "xpu", "hpu", "cpu"], + category="general", + applies_to=["both"] + ), + ParamDef( + name="threads", + arg_names=["--threads"], + type=int, + default=None, + help="Number of threads for BM25 retrieval (default: CPU count)", + category="general", + applies_to=["bm25"] + ), + ParamDef( + name="num_embedding_devices", + arg_names=["--num_embedding_devices"], + type=int, + default=1, + help="Number of devices to use for parallel embedding generation (supports XPU, CUDA, CPU)", + category="general", + applies_to=["vector"] + ), + ParamDef( + name="llm_service_url", + arg_names=["--llm_service_url"], + type=str, + default="http://127.0.0.1:8123/v1/chat/completions", + help="URL for the LLM service endpoint", + category="general", + applies_to=["both"] + ), + ParamDef( + name="llm_model", + arg_names=["--llm_model"], + type=str, + default="auto", + help="LLM model name/path (auto to detect from service). Used by document grader (evaluate_document_relevance).", + category="general", + applies_to=["both"] + ), + ParamDef( + name="query_model", + arg_names=["--query_model"], + type=str, + default=None, + help="LLM model name/path for query generation (generate_search_queries). Defaults to --llm_model if not set. Example: /model/gpt-oss-120b", + category="general", + applies_to=["both"] + ), + # Per-component endpoint overrides. Each defaults to --llm_service_url when + # not set; same for the model. Lets you split components across separate + # vLLM servers (e.g. 20B grader on :8124, 120B query/sufficiency on :8123). + ParamDef( + name="grader_service_url", + arg_names=["--grader-service-url"], + type=str, + default=None, + help="LLM service URL for the document grader (default: --llm_service_url).", + category="general", + applies_to=["both"] + ), + ParamDef( + name="grader_model", + arg_names=["--grader-model"], + type=str, + default=None, + help="Model name for the document grader (default: --llm_model).", + category="general", + applies_to=["both"] + ), + ParamDef( + name="query_service_url", + arg_names=["--query-service-url"], + type=str, + default=None, + help="LLM service URL for query generation (default: --llm_service_url).", + category="general", + applies_to=["both"] + ), + ParamDef( + name="sufficiency_service_url", + arg_names=["--sufficiency-service-url"], + type=str, + default=None, + help="LLM service URL for sufficiency check + answer generation (default: --llm_service_url).", + category="general", + applies_to=["both"] + ), + ParamDef( + name="sufficiency_model", + arg_names=["--sufficiency-model"], + type=str, + default=None, + help="Model name for sufficiency check + answer generation (default: --query_model, then --llm_model).", + category="general", + applies_to=["both"] + ), + ParamDef( + name="max_tokens", + arg_names=["--max_tokens"], + type=str, + default="auto", + help="Maximum tokens for LLM response (auto to detect from service, or specify number)", + category="general", + applies_to=["both"] + ), + ParamDef( + name="base_doc_dir", + arg_names=["--base-doc-dir"], + type=str, + default="doc_html", + help="Directory containing full documents to use when --full-doc-context is enabled", + category="general", + applies_to=["both"] + ), + ParamDef( + name="save_results", + arg_names=["--save-results"], + type=bool, + default=True, + help="Save experiment outputs to result_single_shot.json (use --no-save-results to disable)", + action=argparse.BooleanOptionalAction, + category="general", + applies_to=["both"] + ), + ParamDef( + name="full_doc_context", + arg_names=["--full-doc-context"], + type=bool, + default=False, + help="Load full source documents for LLM context instead of retrieved passages", + action="store_true", + category="general", + applies_to=["both"] + ), + ParamDef( + name="generate_answer", + arg_names=["--generate-answer"], + type=bool, + default=False, + help="Generate LLM answer outputs and save them alongside retrieval results", + action="store_true", + category="general", + applies_to=["both"] + ), +] + +# ============================================================================ +# BM25 Parameters +# ============================================================================ + +BM25_PARAMS = [ + ParamDef( + name="bm25_k1", + arg_names=["--bm25_k1"], + type=float, + default=1.2, + help="BM25 k1 parameter (term frequency saturation)", + category="bm25", + applies_to=["bm25"], + optuna_suggest={'type': 'float', 'min': 0.5, 'max': 3.0, 'step': 0.1} + ), + ParamDef( + name="bm25_b", + arg_names=["--bm25_b"], + type=float, + default=0.75, + help="BM25 b parameter (document length normalization)", + category="bm25", + applies_to=["bm25"], + optuna_suggest={'type': 'float', 'min': 0.0, 'max': 1.0, 'step': 0.1} + ), + ParamDef( + name="bm25_method", + arg_names=["--bm25_method"], + type=str, + default="lucene", + help="BM25 variant to use", + choices=["lucene", "robertson", "bm25+"], + category="bm25", + applies_to=["bm25"], + optuna_suggest={'type': 'categorical', 'choices': ["lucene", "robertson", "bm25+"]} + ), + ParamDef( + name="bm25_delta", + arg_names=["--bm25_delta"], + type=float, + default=0.5, + help="BM25+ delta parameter (lower bound for term weights)", + category="bm25", + applies_to=["bm25"], + optuna_suggest={'type': 'float', 'min': 0.0, 'max': 2.0, 'step': 0.1} + ), + ParamDef( + name="bm25_stemmer", + arg_names=["--bm25_stemmer"], + type=str, + default=None, + help="Stemmer to use for BM25 (None, porter, snowball, lancaster, pystemmer)", + choices=[None, "porter", "snowball", "lancaster", "pystemmer"], + category="bm25", + applies_to=["bm25"], + optuna_suggest={'type': 'categorical', 'choices': [None, "porter", "snowball", "lancaster", "pystemmer"]} + ), + ParamDef( + name="bm25_stopwords", + arg_names=["--bm25_stopwords"], + type=str, + default="en", + help="Stopwords language (en, None for no stopwords)", + category="bm25", + applies_to=["bm25"] + ), + ParamDef( + name="no_stopwords", + arg_names=["--no-stopwords"], + type=bool, + default=False, + help="Disable stopwords filtering", + action="store_true", + category="bm25", + applies_to=["bm25"] + ), + ParamDef( + name="bm25_backend", + arg_names=["--bm25_backend"], + type=str, + default="numba", + help="BM25 computation backend (numpy, numba)", + choices=["numpy", "numba"], + category="bm25", + applies_to=["bm25"], + optuna_suggest={'type': 'categorical', 'choices': ["numpy", "numba"]} + ), + ParamDef( + name="bm25_show_progress", + arg_names=["--bm25_show_progress"], + type=bool, + default=False, + help="Show progress bars during BM25 indexing", + action="store_true", + category="bm25", + applies_to=["bm25"] + ), +] + +# ============================================================================ +# Vector Parameters +# ============================================================================ + +VECTOR_PARAMS = [ + ParamDef( + name="vector_index_method", + arg_names=["--vector_index_method"], + type=str, + default="flat", + help="FAISS index method (flat, hnsw, ivf)", + choices=["flat", "hnsw", "ivf"], + category="vector", + applies_to=["vector"], + optuna_suggest={'type': 'categorical', 'choices': ["flat", "hnsw", "ivf"]} + ), + ParamDef( + name="ivf_nprobe", + arg_names=["--ivf_nprobe"], + type=int, + default=10, + help="Number of clusters to probe for IVF index", + category="vector", + applies_to=["vector"], + optuna_suggest={'type': 'int', 'min': 1, 'max': 100, 'step': 1} + ), + ParamDef( + name="hierarchical", + arg_names=["--hierarchical"], + type=bool, + default=False, + action="store_true", + help="Enable hierarchical retrieval (search small chunks, return large parents)", + category="vector", + applies_to=["vector"] + ), +] + +# ============================================================================ +# Retrieval Strategy Parameters +# ============================================================================ + +STRATEGY_PARAMS = [ + ParamDef( + name="retrieval_strategy", + arg_names=["--retrieval_strategy"], + type=str, + default="fixed_k", + help="Strategy for selecting number of retrieved documents", + choices=["fixed_k", "top_p", "relative"], + category="strategy", + applies_to=["both"], + optuna_suggest={'type': 'categorical', 'choices': ["fixed_k", "top_p", "relative"]} + ), + ParamDef( + name="top_k_retriever", + arg_names=["--top_k_retriever"], + type=int, + default=10, + help="Number of documents to retrieve (for fixed_k strategy or max for dynamic strategies)", + category="strategy", + applies_to=["both"], + optuna_suggest={'type': 'int', 'min': 5, 'max': 100, 'step': 5} + ), + ParamDef( + name="top_p", + arg_names=["--top_p"], + type=float, + default=0.9, + help="Cumulative probability threshold for top_p strategy (0.0-1.0)", + category="strategy", + applies_to=["both"], + optuna_suggest={'type': 'float', 'min': 0.5, 'max': 0.99, 'step': 0.01} + ), + ParamDef( + name="relative_ratio", + arg_names=["--relative_ratio"], + type=float, + default=0.8, + help="Score ratio threshold for relative strategy (0.0-1.0)", + category="strategy", + applies_to=["both"], + optuna_suggest={'type': 'float', 'min': 0.5, 'max': 0.99, 'step': 0.01} + ), +] + +# ============================================================================ +# Reranking Parameters +# ============================================================================ + +RERANKING_PARAMS = [ + ParamDef( + name="top_k_reranking", + arg_names=["--top_k_reranking"], + type=int, + default=10, + help="Number of documents to return after reranking", + category="reranking", + applies_to=["both"], + optuna_suggest={'type': 'int', 'min': 1, 'max': 50, 'step': 1} + ), +] + +# ============================================================================ +# All Parameters +# ============================================================================ + +ALL_PARAMS = ( + COMMON_PARAMS + + GENERAL_PARAMS + + BM25_PARAMS + + VECTOR_PARAMS + + STRATEGY_PARAMS + + RERANKING_PARAMS +) + +# Create lookup dictionaries +PARAM_BY_NAME = {p.name: p for p in ALL_PARAMS} +PARAM_BY_CLI_NAME = {} +for p in ALL_PARAMS: + for arg_name in p.arg_names: + PARAM_BY_CLI_NAME[arg_name] = p + +# Category groupings +PARAMS_BY_CATEGORY = { + "common": COMMON_PARAMS, + "general": GENERAL_PARAMS, + "bm25": BM25_PARAMS, + "vector": VECTOR_PARAMS, + "strategy": STRATEGY_PARAMS, + "reranking": RERANKING_PARAMS, +} + +# Method-specific parameters +BM25_METHOD_PARAMS = [p for p in ALL_PARAMS if "bm25" in p.applies_to or "both" in p.applies_to] +VECTOR_METHOD_PARAMS = [p for p in ALL_PARAMS if "vector" in p.applies_to or "both" in p.applies_to] + +# Optimizable parameters (those with optuna_suggest defined) +OPTIMIZABLE_PARAMS = [p for p in ALL_PARAMS if p.optuna_suggest is not None] +OPTIMIZABLE_PARAM_NAMES = [p.name for p in OPTIMIZABLE_PARAMS] + +# ============================================================================ +# Helper Functions +# ============================================================================ + +def add_common_args(parser: argparse.ArgumentParser): + """ + Add common script parameters (ingest, database, query, etc.) + + Args: + parser: ArgumentParser to add arguments to + """ + for param in COMMON_PARAMS: + param.add_to_parser(parser) + +def add_retrieval_args(parser: argparse.ArgumentParser, + method: Optional[str] = None, + categories: Optional[List[str]] = None): + """ + Add retrieval parameters to an argument parser. + Includes: General, BM25, Vector, Strategy, and Reranking parameters. + Does NOT include common script parameters (use add_common_args for those). + + Args: + parser: ArgumentParser to add arguments to + method: Filter by method ('bm25', 'vector', or None for all) + categories: Filter by categories (e.g., ['general', 'strategy']) + """ + # Get non-common params + params_to_add = [p for p in ALL_PARAMS if p.category != "common"] + + # Filter by method + if method: + params_to_add = [p for p in params_to_add + if method in p.applies_to or "both" in p.applies_to] + + # Filter by category + if categories: + params_to_add = [p for p in params_to_add if p.category in categories] + + # Add to parser + for param in params_to_add: + param.add_to_parser(parser) + +def add_all_args(parser: argparse.ArgumentParser, method: Optional[str] = None): + """ + Add all parameters (common + retrieval) to an argument parser. + + Args: + parser: ArgumentParser to add arguments to + method: Filter by method ('bm25', 'vector', or None for all) + """ + add_common_args(parser) + add_retrieval_args(parser, method=method) + +def get_optimizable_params(method: Optional[str] = None) -> List[ParamDef]: + """ + Get list of parameters that can be optimized with Optuna. + + Args: + method: Filter by method ('bm25', 'vector', or None for all) + + Returns: + List of ParamDef objects + """ + params = OPTIMIZABLE_PARAMS + + if method: + params = [p for p in params + if method in p.applies_to or "both" in p.applies_to] + + return params + +def suggest_param(trial, param_name: str) -> Any: + """ + Suggest a parameter value for Optuna trial. + + Args: + trial: Optuna trial object + param_name: Parameter name + + Returns: + Suggested value + """ + if param_name not in PARAM_BY_NAME: + raise ValueError(f"Unknown parameter: {param_name}") + + param = PARAM_BY_NAME[param_name] + return param.suggest_value(trial) + +def get_default_params(method: str) -> Dict[str, Any]: + """ + Get default parameter values for a method. + + Args: + method: 'bm25' or 'vector' + + Returns: + Dictionary of parameter name -> default value + """ + if method == "bm25": + params = BM25_METHOD_PARAMS + elif method == "vector": + params = VECTOR_METHOD_PARAMS + else: + params = ALL_PARAMS + + return {p.name: p.default for p in params} + +def format_params_for_cli(params: Dict[str, Any], skip_defaults: bool = True) -> List[str]: + """ + Format parameter dictionary as CLI arguments. + + Args: + params: Dictionary of parameter name -> value + skip_defaults: If True, skip parameters that match their default values + + Returns: + List of CLI argument strings + """ + args = [] + + for name, value in params.items(): + if name not in PARAM_BY_NAME: + continue + + param_def = PARAM_BY_NAME[name] + + # Skip if value matches default (when skip_defaults=True) + if skip_defaults and value == param_def.default: + continue + + cli_arg = param_def.arg_names[0] + + if param_def.action == "store_true": + if value: + args.append(cli_arg) + elif param_def.action == "store_false": + if not value: + args.append(cli_arg) + elif value is not None: + args.extend([cli_arg, str(value)]) + + return args + +def print_param_info(method: Optional[str] = None): + """Print parameter information grouped by category.""" + + if method == "bm25": + params = BM25_METHOD_PARAMS + elif method == "vector": + params = VECTOR_METHOD_PARAMS + else: + params = ALL_PARAMS + + # Group by category + by_category = {} + for param in params: + if param.category not in by_category: + by_category[param.category] = [] + by_category[param.category].append(param) + + # Print + for category in ["common", "general", "bm25", "vector", "strategy", "reranking"]: + if category not in by_category: + continue + + print(f"\n{category.upper()} Parameters:") + print("=" * 60) + + for param in by_category[category]: + opt_marker = " [optimizable]" if param.optuna_suggest else "" + print(f" {param.name}{opt_marker}") + print(f" CLI: {', '.join(param.arg_names)}") + print(f" Default: {param.default}") + print(f" Help: {param.help[:80]}..." if len(param.help) > 80 else f" Help: {param.help}") + if param.choices: + print(f" Choices: {param.choices}") + +# ============================================================================ +# Main (for testing/documentation) +# ============================================================================ + +if __name__ == "__main__": + import sys + + if len(sys.argv) > 1 and sys.argv[1] == "list": + method = sys.argv[2] if len(sys.argv) > 2 else None + print_param_info(method) + else: + print("Usage: python retrieval_params.py list [bm25|vector]") + print("\nOptimizable parameters:") + for param in OPTIMIZABLE_PARAMS: + print(f" - {param.name} ({param.category})") diff --git a/e2e/read_docs.py b/e2e/read_docs.py new file mode 100644 index 0000000000..a879f7331e --- /dev/null +++ b/e2e/read_docs.py @@ -0,0 +1,627 @@ +#!/usr/bin/env python3 +""" +Unified Document Text Extraction Pipeline + +Extract text from PDF and HTML files using appropriate extractors based on file extensions. +Automatically detects file types and can process mixed directories. +Uses shared text splitting functionality for consistency. +""" + +import fitz +import os +import json +import argparse +import re +from pathlib import Path +from typing import List, Dict, Optional, Tuple +from tqdm import tqdm +from abc import ABC, abstractmethod +from multiprocessing import Pool + +try: + from bs4 import BeautifulSoup, NavigableString, Tag +except ImportError as e: + print(f"Warning: BeautifulSoup not available. HTML processing will be disabled.") + print(f"Install with: pip install beautifulsoup4") + BeautifulSoup = None + +from text_splitter import split_into_passages, split_into_fixed_passages, create_passage_metadata +from utils import load_url_mapping, get_base_filename + + +class BaseDocumentExtractor(ABC): + """Base class for document text extractors.""" + + @abstractmethod + def extract_text(self, file_path: str) -> Optional[str]: + """Extract text from a document file.""" + pass + + @abstractmethod + def get_supported_extensions(self) -> List[str]: + """Return list of supported file extensions.""" + pass + + +class PDFExtractor(BaseDocumentExtractor): + """Extract text from PDF files using PyMuPDF (fitz).""" + + def extract_text(self, file_path: str) -> Optional[str]: + """Extract text from a single PDF file.""" + try: + doc = fitz.open(file_path) + extracted_text = [] + + for page in doc: + text = page.get_text() + extracted_text.append(text) + + doc.close() + return "\n".join(extracted_text) + + except Exception as e: + print(f"Error processing PDF {file_path}: {e}") + return None + + def get_supported_extensions(self) -> List[str]: + return ['.pdf'] + + +class HTMLExtractor(BaseDocumentExtractor): + """Extract text from HTML files using BeautifulSoup with focus on retrieval quality.""" + + def __init__(self, preserve_tables: bool = True, preserve_lists: bool = True, + text_boundary: str = "sentence"): + """ + Initialize HTML extractor with configurable options. + + Args: + preserve_tables: Whether to preserve table structure + preserve_lists: Whether to preserve list structure + text_boundary: Text boundary optimization - "sentence" (default), "word", or "none" + """ + if BeautifulSoup is None: + raise ImportError("BeautifulSoup is required for HTML processing. Install with: pip install beautifulsoup4") + + self.preserve_tables = preserve_tables + self.preserve_lists = preserve_lists + self.text_boundary = text_boundary + + if text_boundary not in ["sentence", "word", "none"]: + raise ValueError("text_boundary must be 'sentence', 'word', or 'none'") + + def extract_text(self, file_path: str) -> Optional[str]: + """Extract text from a single HTML file.""" + try: + with open(file_path, 'r', encoding='utf-8') as f: + html_content = f.read() + + return self.extract_text_from_html(html_content) + + except Exception as e: + print(f"Error processing HTML {file_path}: {e}") + return None + + def extract_text_from_html(self, html_content: str) -> str: + """Extract clean text optimized for retrieval systems.""" + # Use lxml parser for speed (fallback to html.parser if not available) + try: + soup = BeautifulSoup(html_content, 'lxml') + except: + soup = BeautifulSoup(html_content, 'html.parser') + + # Remove noise elements completely + for element in soup(['script', 'style', 'nav', 'header', 'footer']): + element.decompose() + + # Remove Wikipedia-specific metadata and navigation + self._remove_wikipedia_metadata(soup) + + # Extract main content using priority order + main_content = self._find_main_content(soup) + + # Get plain text with sentence separation + if main_content: + text = main_content.get_text(separator=' ', strip=True) + else: + text = soup.get_text(separator=' ', strip=True) + + # Clean and normalize the text + return self._clean_text(text) + + def _extract_from_element(self, element) -> List[str]: + """Extract text from an HTML element, preserving structure.""" + text_parts = [] + + if isinstance(element, NavigableString): + text = str(element).strip() + if text: + text_parts.append(text) + elif isinstance(element, Tag): + # Handle different tag types + if element.name in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6']: + # Headers get extra spacing + text = element.get_text().strip() + if text: + text_parts.append(f"\n{text}\n") + elif element.name == 'p': + # Paragraphs + text = element.get_text().strip() + if text: + text_parts.append(f"{text}\n") + elif element.name == 'table' and self.preserve_tables: + # Tables - extract as structured text + table_text = self._extract_table_text(element) + if table_text: + text_parts.append(f"\n{table_text}\n") + elif element.name in ['ul', 'ol'] and self.preserve_lists: + # Lists + list_text = self._extract_list_text(element) + if list_text: + text_parts.append(f"\n{list_text}\n") + elif element.name in ['br']: + text_parts.append('\n') + elif element.name in ['div', 'span', 'section', 'article']: + # Container elements - process children + for child in element.children: + text_parts.extend(self._extract_from_element(child)) + else: + # For other elements, just get the text + text = element.get_text().strip() + if text: + text_parts.append(text) + + return text_parts + + def _extract_table_text(self, table) -> str: + """Extract text from a table element.""" + rows = [] + for tr in table.find_all('tr'): + cells = [] + for cell in tr.find_all(['td', 'th']): + cell_text = cell.get_text().strip() + cells.append(cell_text) + if cells: + rows.append(' | '.join(cells)) + return '\n'.join(rows) + + def _extract_list_text(self, list_elem) -> str: + """Extract text from a list element.""" + items = [] + for li in list_elem.find_all('li', recursive=False): + item_text = li.get_text().strip() + if item_text: + prefix = '- ' if list_elem.name == 'ul' else f"{len(items) + 1}. " + items.append(f"{prefix}{item_text}") + return '\n'.join(items) + + def _clean_text(self, text: str) -> str: + """Clean and normalize extracted text with configurable boundary optimization.""" + # Basic normalization + import unicodedata + text = unicodedata.normalize('NFKC', text) + + # Replace various whitespace characters with standard space + text = re.sub(r'[\u00A0\u2000-\u200B\u2028\u2029]', ' ', text) + + # Clean up whitespace + text = text.strip().replace('\r', '\n') + text = re.sub(r' +', ' ', text) # Multiple spaces -> single space + text = re.sub(r'\n+', '\n', text) # Multiple newlines -> single newline + + # Remove empty lines and extra spacing + lines = [line.strip() for line in text.split('\n') if line.strip()] + text = '\n'.join(lines) + + # Apply boundary optimization based on setting + if self.text_boundary == "sentence": + text = self._optimize_sentence_boundaries(text) + elif self.text_boundary == "word": + text = self._optimize_word_boundaries(text) + # "none" - no boundary optimization + + return text + + def _optimize_sentence_boundaries(self, text: str) -> str: + """Optimize text for sentence-level splitting and retrieval.""" + # Add space after sentence endings if missing + text = re.sub(r'([.!?])([A-Z])', r'\1 \2', text) + + # Handle common abbreviations that shouldn't split sentences + # (e.g., "Mr.", "Dr.", "etc.", "U.S.") + abbrev_pattern = r'\b(Mr|Mrs|Dr|Prof|etc|vs|Inc|Ltd|Corp|U\.S|U\.K|E\.g|I\.e)\.(\s+)([a-z])' + text = re.sub(abbrev_pattern, r'\1.\2\3', text, flags=re.IGNORECASE) + + return text + + def _optimize_word_boundaries(self, text: str) -> str: + """Optimize text for word-level processing and retrieval.""" + # Ensure proper spacing around punctuation for better tokenization + text = re.sub(r'([.!?,:;])([A-Za-z])', r'\1 \2', text) + + # Handle hyphenated words - keep them as single tokens + text = re.sub(r'(\w+)-\s+(\w+)', r'\1-\2', text) + + # Normalize quotation marks and other punctuation + text = text.replace('"', '"').replace('"', '"') # Smart quotes to regular quotes + text = text.replace(''', "'").replace(''', "'") # Smart apostrophes to regular apostrophes + + # Ensure consistent spacing + text = re.sub(r'\s+', ' ', text) + + return text + + def _remove_wikipedia_metadata(self, soup): + """Remove Wikipedia-specific metadata and navigation elements.""" + # Wikipedia-specific noise removal + selectors_to_remove = [ + # Navigation and interface elements + '#mw-navigation', '.navbox', '.navigation-box', + '.ambox', '.tmbox', + # Edit links and metadata + '.mw-editsection', '.edit-section', '.editlink', + # References and citations (keep text but remove citation numbers) + 'sup.reference', '.reference', '.citation', + # Disambiguation and hatnotes + '.hatnote', '.dablink', '.rellink', + # Categories and external links boxes + '#catlinks', '.catlinks', '.external-links', + # Table of contents (often not needed for retrieval) + '#toc', '.toc', + # Image captions and metadata (keep main text) + '.thumbcaption .metadata', '.image-metadata' + ] + + for selector in selectors_to_remove: + for element in soup.select(selector): + element.decompose() + + def _find_main_content(self, soup): + """Find the main content area with fallback strategy.""" + # Priority order for content detection + content_selectors = [ + '#mw-content-text .mw-parser-output', # Wikipedia main content + '#mw-content-text', # Wikipedia content wrapper + 'main', # HTML5 main element + 'article', # HTML5 article element + '.content', # Generic content class + '#content', # Generic content ID + 'body' # Last resort + ] + + for selector in content_selectors: + content = soup.select_one(selector) + if content: + return content + + # Final fallback + return soup + + def get_supported_extensions(self) -> List[str]: + return ['.html', '.htm'] + + +class DocumentProcessor: + """Unified document processor that handles both PDF and HTML files.""" + + def __init__(self, preserve_tables: bool = True, preserve_lists: bool = True, + text_boundary: str = "sentence", benchmark: bool = False, + processes: int = 4): + """ + Initialize document processor. + + Args: + preserve_tables: Whether to preserve table structure (HTML only) + preserve_lists: Whether to preserve list structure (HTML only) + text_boundary: Text boundary optimization - "sentence" (default), "word", or "none" + benchmark: Enable performance monitoring + processes: Number of parallel processes for document processing + """ + self.processes = processes + self.extractors = { + '.pdf': PDFExtractor(), + } + + # Only add HTML extractor if BeautifulSoup is available + if BeautifulSoup is not None: + self.extractors.update({ + '.html': HTMLExtractor(preserve_tables, preserve_lists, text_boundary), + '.htm': HTMLExtractor(preserve_tables, preserve_lists, text_boundary), + }) + + self.url_mapping = {} + self.benchmark = benchmark + self.monitor = None + + # Store config for worker processes + self.preserve_tables = preserve_tables + self.preserve_lists = preserve_lists + self.text_boundary = text_boundary + + # Initialize monitoring if benchmark mode enabled + if self.benchmark: + from ingestion_monitor import IngestionMonitor + self.monitor = IngestionMonitor() + + def get_supported_extensions(self) -> List[str]: + """Get all supported file extensions.""" + extensions = [] + for extractor in self.extractors.values(): + extensions.extend(extractor.get_supported_extensions()) + return list(set(extensions)) + + @staticmethod + def process_single_file(args_tuple: Tuple) -> Optional[Tuple[str, str, List[str], str]]: + """ + Process a single document file (worker function for multiprocessing). + + Args: + args_tuple: (doc_file_path, output_dir, url_mapping, preserve_tables, + preserve_lists, text_boundary, fixed_length, fixed_overlap, + max_passage_length, passage_overlap) + + Returns: + Tuple of (output_filename, text, passages, original_url) or None if processing failed + """ + (doc_file_path, output_dir, url_mapping, preserve_tables, preserve_lists, + text_boundary, fixed_length, fixed_overlap, max_passage_length, passage_overlap) = args_tuple + + doc_file = Path(doc_file_path) + file_extension = doc_file.suffix.lower() + + # Create appropriate extractor + if file_extension == '.pdf': + extractor = PDFExtractor() + elif file_extension in ['.html', '.htm'] and BeautifulSoup is not None: + extractor = HTMLExtractor(preserve_tables, preserve_lists, text_boundary) + else: + return None + + # Extract text + try: + text = extractor.extract_text(str(doc_file)) + if text is None: + return None + except Exception as e: + print(f"Error extracting text from {doc_file}: {e}") + return None + + # Split text into passages + try: + if fixed_length: + passages = split_into_fixed_passages(text, fixed_length, fixed_overlap or 32) + else: + passages = split_into_passages(text, max_passage_length, passage_overlap) + except Exception as e: + print(f"Error splitting text for {doc_file}: {e}") + return None + + # Get original URL + base_filename = get_base_filename(doc_file.name) + original_url = url_mapping.get(base_filename, "") + + # Generate output filename + output_filename = doc_file.stem + ".txt" + + return (output_filename, text, passages, original_url, doc_file.name) + + def process_documents(self, input_dir: str, output_dir: str, json_file: Optional[str] = None, + max_passage_length: int = 512, passage_overlap: int = 50, + fixed_length: Optional[int] = None, fixed_overlap: Optional[int] = None, + max_files: Optional[int] = None): + """ + Process documents in a directory, extracting text and splitting into passages. + + Args: + input_dir: Directory containing document files + output_dir: Directory to save extracted text files + json_file: Optional JSON file to save passage metadata + max_passage_length: Maximum passage length for variable-length splitting + passage_overlap: Overlap between passages for variable-length splitting + fixed_length: Use fixed-length passages instead of variable-length + fixed_overlap: Overlap for fixed-length passages + max_files: Maximum number of files to process (for testing) + """ + input_path = Path(input_dir) + output_path = Path(output_dir) + output_path.mkdir(parents=True, exist_ok=True) + + self.url_mapping = load_url_mapping(input_dir) + + # Find all supported document files + supported_extensions = self.get_supported_extensions() + document_files = [] + + for ext in supported_extensions: + pattern = f"*{ext}" + document_files.extend(input_path.glob(pattern)) + + # Sort for consistent processing order + document_files = sorted(document_files) + + if not document_files: + return + + if max_files: + document_files = document_files[:max_files] + + print(f"Processing {len(document_files)} documents with {self.processes} parallel processes...") + + all_passages = [] + passage_id = 0 + + # Initialize monitoring if enabled + if self.benchmark and self.monitor: + self.monitor.start_ingestion() + + # Prepare arguments for multiprocessing + process_args = [ + (str(doc_file), str(output_path), self.url_mapping, + self.preserve_tables, self.preserve_lists, self.text_boundary, + fixed_length, fixed_overlap, max_passage_length, passage_overlap) + for doc_file in document_files + ] + + # Process documents in parallel with progress bar + with Pool(processes=self.processes) as pool: + with tqdm(total=len(document_files), desc="Processing documents") as pbar: + for result in pool.imap(self.process_single_file, process_args): + if result is None: + pbar.update(1) + continue + + output_filename, text, passages, original_url, doc_filename = result + + # Save text file + output_file_path = output_path / output_filename + try: + with open(output_file_path, 'w', encoding='utf-8') as f: + f.write(text) + except Exception as e: + print(f"Error writing {output_file_path}: {e}") + pbar.update(1) + continue + + # Add passages to collection if JSON output requested + if json_file: + for passage in passages: + passage_metadata = create_passage_metadata( + doc_filename, passage_id, original_url=original_url) + + all_passages.append({ + **passage_metadata, + 'passage': passage, + }) + passage_id += 1 + + pbar.update(1) + + # Finalize monitoring and report + self._report_processing_performance() + + # Save JSON file if requested + if json_file and all_passages: + json_path = Path(json_file) + json_path.parent.mkdir(parents=True, exist_ok=True) + + payload = { + "base_dir": str(output_path.resolve()), + "passages": all_passages + } + with open(json_path, 'w', encoding='utf-8') as f: + json.dump(payload, f, indent=2, ensure_ascii=False) + + print(f"Saved {len(all_passages)} passages to {json_file}") + + def _process_document(self, doc_file: Path, file_extension: str) -> Optional[str]: + """Process a single document with optional monitoring.""" + extractor = self.extractors[file_extension] + + if self.benchmark and self.monitor: + component_name = "html_parsing" if file_extension in ['.html', '.htm'] else "pdf_parsing" + file_size = doc_file.stat().st_size + with self.monitor.track_component(component_name, input_size_bytes=file_size, + items_count=1, is_pipeline_input=True): + return extractor.extract_text(str(doc_file)) + else: + return extractor.extract_text(str(doc_file)) + + def _process_text_chunking(self, text: str, fixed_length: Optional[int], fixed_overlap: Optional[int], + max_passage_length: int, passage_overlap: int) -> List[str]: + """Process text chunking with optional monitoring.""" + def chunk_func(): + if fixed_length: + return split_into_fixed_passages(text, fixed_length, fixed_overlap or 32) + else: + return split_into_passages(text, max_passage_length, passage_overlap) + + if self.benchmark and self.monitor: + text_size = len(text.encode('utf-8')) + with self.monitor.track_component("text_chunking", input_size_bytes=text_size, + items_count=1, text_only=True) as ctx: + passages = chunk_func() + ctx.add_text_bytes(text_size) + return passages + else: + return chunk_func() + + def _add_passages_to_collection(self, doc_file: Path, passages: List[str], + all_passages: List[Dict], passage_id: int) -> int: + """Add passages to collection and return updated passage_id.""" + base_filename = get_base_filename(doc_file.name) + original_url = self.url_mapping.get(base_filename, "") + + for passage in passages: + passage_metadata = create_passage_metadata( + doc_file.name, passage_id, original_url=original_url) + + all_passages.append({ + **passage_metadata, + 'passage': passage, + }) + passage_id += 1 + + return passage_id + + def _report_processing_performance(self): + """Report processing performance if monitoring is enabled.""" + if self.benchmark and self.monitor: + print("\n=== Document Processing Performance ===") + self.monitor.print_summary() + + +def main(): + parser = argparse.ArgumentParser( + description="Extract text from PDF and HTML files and split into passages", + formatter_class=argparse.RawTextHelpFormatter + ) + + parser.add_argument("input_dir", help="Directory containing document files (PDF/HTML)") + parser.add_argument("output_dir", help="Directory to save text files") + parser.add_argument("--json", help="JSON file to save passage data") + parser.add_argument("--max-length", type=int, default=512, + help="Maximum passage length in characters (default: 512)") + parser.add_argument("--overlap", type=int, default=50, + help="Overlap between passages in characters (default: 50)") + parser.add_argument("--fixed-length", type=int, + help="Use fixed-length passages instead of variable-length") + parser.add_argument("--fixed-overlap", type=int, default=32, + help="Overlap for fixed-length passages (default: 32)") + parser.add_argument("--max-files", type=int, + help="Maximum number of files to process (for testing)") + parser.add_argument("--no-tables", action="store_true", + help="Don't preserve table structure (HTML only)") + parser.add_argument("--no-lists", action="store_true", + help="Don't preserve list structure (HTML only)") + parser.add_argument("--text-boundary", choices=["sentence", "word", "none"], + default="sentence", + help="Text boundary optimization: 'sentence' (default), 'word', or 'none'") + parser.add_argument("--processes", type=int, default=4, + help="Number of parallel processes for document processing (default: 4)") + parser.add_argument("--benchmark", action="store_true", + help="Enable performance monitoring and detailed component analysis") + + args = parser.parse_args() + + processor = DocumentProcessor( + preserve_tables=not args.no_tables, + preserve_lists=not args.no_lists, + text_boundary=args.text_boundary, + benchmark=args.benchmark, + processes=args.processes + ) + + processor.process_documents( + input_dir=args.input_dir, + output_dir=args.output_dir, + json_file=args.json, + max_passage_length=args.max_length, + passage_overlap=args.overlap, + fixed_length=args.fixed_length, + fixed_overlap=args.fixed_overlap, + max_files=args.max_files + ) + + +if __name__ == "__main__": + main() diff --git a/e2e/requirements.txt b/e2e/requirements.txt new file mode 100644 index 0000000000..b660f9fc22 --- /dev/null +++ b/e2e/requirements.txt @@ -0,0 +1,12 @@ +pandas>=2.0.0 +datasets>=4.0.0 +tqdm>=4.66.0 +PyMuPDF>=1.23.0 +sentence-transformers~=5.1.0 +faiss-cpu +langchain +langchain-community +langchain-huggingface +bm25s +beautifulsoup4>=4.12.0 +requests>=2.31.0 diff --git a/e2e/reranker_worker.py b/e2e/reranker_worker.py new file mode 100644 index 0000000000..6cfa85bb2c --- /dev/null +++ b/e2e/reranker_worker.py @@ -0,0 +1,228 @@ +"""Out-of-process reranker worker (ColBERT late-interaction MaxSim). + +The reranker model lives in its own multiprocessing.Process so it can have: +- Its own OMP_NUM_THREADS (separate OMP runtime). +- Its own CPU affinity + memory binding (per-NUMA-node placement). +- Isolation from the main process's GIL / thread pool. + +Public API kept: RerankerQueue.submit(query, passages) -> [(passage, score), ...] +""" + +import ctypes +import multiprocessing as mp +import os +import queue +import signal +import threading +import time +from typing import List, Tuple + + +# Linux prctl(PR_SET_PDEATHSIG, signo): kernel sends `signo` to this process when +# its parent dies, even if parent dies via SIGKILL. +_PR_SET_PDEATHSIG = 1 + + +def _set_parent_death_signal(signo: int = signal.SIGTERM) -> None: + try: + libc = ctypes.CDLL("libc.so.6", use_errno=True) + libc.prctl(_PR_SET_PDEATHSIG, signo, 0, 0, 0) + except Exception: + pass # non-Linux or no libc; daemon=True still kills on normal exit + + +def _reranker_worker_main( + model_name: str, + device: str, + numa_node, + omp_threads, + request_q: mp.Queue, + response_q: mp.Queue, + ready_event, +): + """Child-process entry point. + + Order matters: pin NUMA → set OMP_NUM_THREADS → import torch → load model. + Setting OMP_NUM_THREADS after torch is imported has no effect. + """ + _set_parent_death_signal() + + if numa_node is not None: + from utils import _physical_cores_for_node, pin_worker_to_node + cores = _physical_cores_for_node(numa_node) + if omp_threads: + cores = cores[:omp_threads] + pin_worker_to_node(numa_node, cores) + elif omp_threads: + os.environ["OMP_NUM_THREADS"] = str(omp_threads) + + if device == "cpu": + from utils import apply_cpu_threading_env + apply_cpu_threading_env() + + import torch + from transformers import AutoModel, AutoTokenizer + + print(f"[reranker child] loading {model_name} on {device}") + model = AutoModel.from_pretrained(model_name) + tokenizer = AutoTokenizer.from_pretrained(model_name) + model = model.to(device) + model.eval() + print(f"[reranker child] ready (pid={os.getpid()})") + + ready_event.set() + + while True: + item = request_q.get() + if item is None: + break + request_id, query, passages = item + try: + result = _do_rerank(model, tokenizer, device, query, passages) + response_q.put((request_id, result, None)) + except Exception as e: + response_q.put((request_id, None, repr(e))) + + +def _do_rerank(model, tokenizer, device: str, query: str, passages: List[str]) -> List[Tuple[str, float]]: + """ColBERT late-interaction reranking with MaxSim scoring.""" + import torch + + with torch.no_grad(): + q_inputs = tokenizer( + query, return_tensors='pt', truncation=True, + max_length=32, padding='max_length' + ) + q_inputs = {k: v.to(device) for k, v in q_inputs.items()} + q_emb = model(**q_inputs).last_hidden_state.squeeze(0) + q_mask = q_inputs['attention_mask'].squeeze(0).bool() + q_emb = q_emb[q_mask] + + d_inputs = tokenizer( + passages, return_tensors='pt', truncation=True, + max_length=512, padding=True + ) + d_inputs = {k: v.to(device) for k, v in d_inputs.items()} + d_emb = model(**d_inputs).last_hidden_state + d_mask = d_inputs['attention_mask'] + + sim = torch.einsum('qd,bld->bql', q_emb, d_emb) + sim = sim.masked_fill(~d_mask.unsqueeze(1).bool(), float('-inf')) + scores = sim.max(dim=-1).values.sum(dim=-1) + + scored_passages = list(zip(passages, scores.float().tolist())) + scored_passages.sort(key=lambda x: x[1], reverse=True) + return scored_passages + + +class RerankerQueue: + """Submit reranking requests to an out-of-process ColBERT model. + + NUMA placement (optional): + numa_node: pin child to this NUMA node (CPU + memory). None = inherit. + omp_threads: cap OMP threads in the child. None = use all node cores. + """ + + def __init__(self, reranker_model_name: str, device: str = "cpu", + numa_node=None, omp_threads=None): + self._model_name = reranker_model_name + self._device = device + self._numa_node = numa_node + self._omp_threads = omp_threads + + try: + mp.set_start_method('spawn', force=True) + except RuntimeError: + pass + + ctx = mp.get_context('spawn') + self._request_q = ctx.Queue() + self._response_q = ctx.Queue() + self._ready_event = ctx.Event() + self._process = None + + # Shared response queue + dispatcher: one entry per pending request. + self._pending: dict = {} + self._pending_lock = threading.Lock() + self._next_id = 0 + self._dispatcher_thread = None + self._dispatcher_running = False + + # Stats (best-effort; reranker child does the actual work). + self.total_requests = 0 + self.total_documents = 0 + self.total_latency_ms = 0.0 + + def start(self): + ctx = mp.get_context('spawn') + self._process = ctx.Process( + target=_reranker_worker_main, + args=(self._model_name, self._device, self._numa_node, self._omp_threads, + self._request_q, self._response_q, self._ready_event), + daemon=True, + ) + self._process.start() + + if not self._ready_event.wait(timeout=300): + raise RuntimeError("reranker child failed to become ready within 300s") + + self._dispatcher_running = True + self._dispatcher_thread = threading.Thread(target=self._dispatcher_loop, daemon=True) + self._dispatcher_thread.start() + + def stop(self): + if self._process is None: + return + try: + self._request_q.put(None) + except Exception: + pass + self._process.join(timeout=10) + if self._process.is_alive(): + self._process.terminate() + self._process.join(timeout=5) + self._dispatcher_running = False + + def _dispatcher_loop(self): + """Pull responses off the shared queue, route to the right awaiter.""" + while self._dispatcher_running: + try: + item = self._response_q.get(timeout=1.0) + except queue.Empty: + continue + if item is None: + break + request_id, result, err = item + with self._pending_lock: + slot = self._pending.pop(request_id, None) + if slot is None: + continue + event, container = slot + if err is not None: + container["error"] = RuntimeError(f"reranker child error: {err}") + else: + container["result"] = result + event.set() + + def submit(self, query: str, passages: List[str]) -> List[Tuple[str, float]]: + if self._process is None or not self._process.is_alive(): + raise RuntimeError("reranker process is not running") + + with self._pending_lock: + request_id = self._next_id + self._next_id += 1 + event = threading.Event() + container = {} + self._pending[request_id] = (event, container) + + t0 = time.perf_counter() + self._request_q.put((request_id, query, passages)) + event.wait() + elapsed_ms = (time.perf_counter() - t0) * 1000 + + if "error" in container: + raise container["error"] + self.total_requests += 1 + self.total_documents += len(passages) + self.total_latency_ms += elapsed_ms + return container["result"] diff --git a/e2e/retrieve/__init__.py b/e2e/retrieve/__init__.py new file mode 100644 index 0000000000..c68135e188 --- /dev/null +++ b/e2e/retrieve/__init__.py @@ -0,0 +1,10 @@ +""" +Retrieve package for vector database operations and retrieval filtering. +""" + +from .ragdb import RagDB +from .vectordb import VectorDB +from .bm25db import BM25DB +from .filter import filter, get_score_statistics + +__all__ = ['RagDB', 'VectorDB', 'BM25DB', 'filter', 'get_score_statistics'] \ No newline at end of file diff --git a/e2e/retrieve/bm25db.py b/e2e/retrieve/bm25db.py new file mode 100644 index 0000000000..3a9b3ee918 --- /dev/null +++ b/e2e/retrieve/bm25db.py @@ -0,0 +1,426 @@ +import bm25s +import pickle +from pathlib import Path +from typing import List, Dict, Any +from .ragdb import RagDB + +class BM25DB(RagDB): + """BM25 database implementation for lexical search.""" + + @classmethod + def get_default_db_name(cls) -> str: + """Get the default database filename for BM25DB.""" + return "bm25.db" + + def __init__(self, reranker_model: str = None, device: str = "auto", k1: float = None, b: float = None, method: str = None, + database: str = None, delta: float = None, idf_method: str = None, dtype: str = None, + backend: str = None, token_pattern: str = None, stopwords = None, stemmer = None, + lower: bool = None, show_progress: bool = None, benchmark: bool = False, + reranker_device: str = None, **kwargs): + super().__init__(reranker_model, device, benchmark, reranker_device=reranker_device) + self._bm25_retriever = None + self._doc_list = [] + self._passages_metadata = [] + self._num_threads = 4 + + # Set database name (use default if not provided) + self._database_name = database if database is not None else "bm25" + + # Set BM25 parameters with defaults + self._k1 = k1 if k1 is not None else 1.5 + self._b = b if b is not None else 0.75 + self._method = method if method is not None else "lucene" + self._delta = delta if delta is not None else 0.5 + self._idf_method = idf_method if idf_method is not None else None # Will default to method + self._dtype = dtype if dtype is not None else "float32" + self._backend = backend if backend is not None else "numba" # Default to numba for speed + + # Tokenization parameters + self._token_pattern = token_pattern if token_pattern is not None else r"(?u)\b\w\w+\b" + self._stopwords = stopwords if stopwords is not None else "en" # Default to English stopwords + self._stemmer = self._create_stemmer_func(stemmer) # Create stemmer function + self._lower = lower if lower is not None else True + self._show_progress = show_progress if show_progress is not None else True + + # Ignore vector-specific parameters + ignored_params = [] + if 'retriever_model' in kwargs: + ignored_params.append('retriever_model') + if ignored_params: + print(f"BM25DB: Ignoring vector-specific parameters: {ignored_params}") + + def _create_stemmer_func(self, stemmer: str): + """Create stemmer function based on stemmer type.""" + self._original_stemmer_type = stemmer # Store for serialization + if stemmer is None: + return None + elif stemmer == "porter": + try: + from nltk.stem import PorterStemmer + stemmer_obj = PorterStemmer() + def porter_wrapper(tokens): + if isinstance(tokens, list): + return [stemmer_obj.stem(token) for token in tokens] + else: + return stemmer_obj.stem(tokens) + return porter_wrapper + except ImportError: + print("Warning: NLTK not available, falling back to no stemming") + return None + elif stemmer == "snowball": + try: + from nltk.stem import SnowballStemmer + stemmer_obj = SnowballStemmer("english") + def snowball_wrapper(tokens): + if isinstance(tokens, list): + return [stemmer_obj.stem(token) for token in tokens] + else: + return stemmer_obj.stem(tokens) + return snowball_wrapper + except ImportError: + print("Warning: NLTK not available, falling back to no stemming") + return None + elif stemmer == "lancaster": + try: + from nltk.stem import LancasterStemmer + stemmer_obj = LancasterStemmer() + def lancaster_wrapper(tokens): + if isinstance(tokens, list): + return [stemmer_obj.stem(token) for token in tokens] + else: + return stemmer_obj.stem(tokens) + return lancaster_wrapper + except ImportError: + print("Warning: NLTK not available, falling back to no stemming") + return None + elif stemmer == "pystemmer": + try: + import Stemmer + stemmer_obj = Stemmer.Stemmer("english") + def pystemmer_wrapper(tokens): + if isinstance(tokens, list): + return stemmer_obj.stemWords(tokens) # Note: stemWords for list + else: + return stemmer_obj.stemWord(tokens) # Note: stemWord for single + return pystemmer_wrapper + except ImportError: + print("Warning: PyStemmer not available, falling back to no stemming") + return None + else: + print(f"Warning: Unknown stemmer '{stemmer}', falling back to no stemming") + return None + + def _get_stemmer_type(self): + """Get the stemmer type for serialization.""" + if self._stemmer is None: + return None + # Store the original stemmer type for reconstruction + return getattr(self, '_original_stemmer_type', None) + + def _calculate_index_output_size(self): + """Calculate the size of BM25 output data (index + passages). + + Returns the total size in bytes of: + - BM25 index files (sparse matrix data and vocab) + - Raw passage text (uncompressed, excluding metadata) + + Excludes: BM25 configuration metadata, passage metadata + """ + from pathlib import Path + + if not self._database_name: + return 0 + + total_size = 0 + + # 1. Get BM25 index files size + index_dir = Path(self.get_data_dir(self._database_name)) + if index_dir.exists(): + index_files = [ + 'data.csc.index.npy', # Sparse matrix data + 'indices.csc.index.npy', # Sparse matrix indices + 'indptr.csc.index.npy', # Sparse matrix indptr + 'vocab.index.json', # Vocabulary + 'params.index.json' # Index parameters + ] + + for filename in index_files: + file_path = index_dir / filename + if file_path.exists(): + total_size += file_path.stat().st_size + + # 2. Get raw passage text size (uncompressed, exclude metadata) + if hasattr(self, '_doc_list'): + # Sum up the byte size of all passages (UTF-8 encoded) + for passage in self._doc_list: + total_size += len(passage.encode('utf-8')) + + return total_size + + def ingest(self, passages: List[str], metadatas: List[dict], **kwargs): + """Ingest passages using BM25 indexing with performance monitoring.""" + + # Start unified timer (handles both benchmark and non-benchmark modes) + ingestion_start = self._start_ingestion_timer() + + self._doc_list = passages + self._passages_metadata = metadatas + self._num_threads = kwargs.get('num_threads', 4) + total_chars = sum(len(passage) for passage in passages) + + corpus_tokens = self._track_component("bm25_tokenization", total_chars, len(passages), + lambda: bm25s.tokenize(passages, stopwords=self._stopwords, + token_pattern=self._token_pattern, stemmer=self._stemmer, + lower=self._lower, show_progress=self._show_progress), + is_pipeline_input=True) + + # Create BM25 retriever + self._bm25_retriever = bm25s.BM25( + k1=self._k1, b=self._b, method=self._method, + delta=self._delta, idf_method=self._idf_method, + dtype=self._dtype, backend=self._backend + ) + + self._track_component("bm25_indexing", total_chars, len(passages), + lambda: self._bm25_retriever.index(corpus_tokens, show_progress=self._show_progress), + is_pipeline_output=True) + + # Store ingestion metrics for later reporting (after serialization) + self._ingestion_start = ingestion_start + self._ingestion_item_count = len(passages) + self._ingestion_total_chars = total_chars + + def ingest_from_folder(self, folder_path: str, **kwargs): + """Ingest whole txt files from a folder instead of passages""" + from pathlib import Path + import sys + import os + sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + from utils import load_url_mapping + + folder_path = Path(folder_path) + url_mapping = load_url_mapping(str(folder_path)) + + doc_list = [] + passage_metadata = [] + + # Process all .txt files in the folder + txt_files = list(folder_path.glob("*.txt")) + if not txt_files: + print(f"Warning: No .txt files found in {folder_path}") + return + + for txt_file in txt_files: + try: + # Read the file content + with open(txt_file, 'r', encoding='utf-8') as f: + content = f.read().strip() + + if not content: + continue + + # Get base filename without extension for URL lookup + base_filename = txt_file.stem + original_url = url_mapping.get(base_filename, "") + + # Create passage and metadata + doc_list.append(content) + metadata = { + 'pdf_filename': txt_file.name, + 'original_url': original_url, + 'base_filename': base_filename + } + passage_metadata.append(metadata) + + except Exception as e: + print(f"Warning: Could not read {txt_file}: {e}") + continue + + print(f"Loaded {len(doc_list)} documents from {folder_path}") + + # Call regular ingest method + num_threads = kwargs.get('num_threads', 4) + self.ingest(doc_list, passage_metadata, num_threads=num_threads, **kwargs) + + def lookup(self, query: str, k: int) -> List[Any]: + """Retrieve top-k passages using BM25.""" + if self._bm25_retriever is None: + raise ValueError("BM25 retriever not initialized. Call ingest() first.") + + query_tokens = bm25s.tokenize([query], + stopwords=self._stopwords, + token_pattern=self._token_pattern, + stemmer=self._stemmer, + lower=self._lower) + results_data, scores = self._bm25_retriever.retrieve(query_tokens, k=k, n_threads=self._num_threads) + + results = [] + + for i in range(len(results_data[0])): + result_item = results_data[0, i] + score = scores[0, i] + + # Handle different result formats from BM25S + if isinstance(result_item, dict): + # Dictionary format: {'id': idx, 'text': content} + doc_idx = int(result_item['id']) + page_content = result_item.get('text', '') + else: + # Index format: result_item is the document index (convert to int) + doc_idx = int(result_item) + page_content = self._doc_list[doc_idx] if doc_idx < len(self._doc_list) else "" + + # Create result object if valid index found + if doc_idx is not None and 0 <= doc_idx < len(self._passages_metadata): + result = type('Result', (), { + 'page_content': page_content, + 'metadata': self._passages_metadata[doc_idx] + })() + results.append(result) + + return results + + def lookup_with_scores(self, query: str, k: int): + """Return results with BM25 scores for score-based filtering.""" + if self._bm25_retriever is None: + raise ValueError("BM25 retriever not initialized. Call ingest() first.") + + query_tokens = bm25s.tokenize([query], + stopwords=self._stopwords, + token_pattern=self._token_pattern, + stemmer=self._stemmer, + lower=self._lower) + results_data, scores = self._bm25_retriever.retrieve(query_tokens, k=k, n_threads=self._num_threads) + + results_with_scores = [] + + for i in range(len(results_data[0])): + result_item = results_data[0, i] + score = float(scores[0, i]) + + # Handle different result formats from BM25S + if isinstance(result_item, dict): + doc_idx = int(result_item['id']) + page_content = result_item.get('text', '') + else: + doc_idx = int(result_item) + page_content = self._doc_list[doc_idx] if doc_idx < len(self._doc_list) else "" + + # Create result object if valid index found + if doc_idx is not None and 0 <= doc_idx < len(self._passages_metadata): + result = type('Result', (), { + 'page_content': page_content, + 'metadata': self._passages_metadata[doc_idx] + })() + results_with_scores.append((result, score)) + + return results_with_scores + + def serialize(self, path: str): + """Save BM25 index and metadata.""" + if self._bm25_retriever is None: + raise ValueError("BM25 retriever not initialized") + + # Save BM25 index to separate directory based on database name + bm25_dir = Path(self.get_data_dir(path)) + self._bm25_retriever.save(str(bm25_dir)) + + # Save database file + db_path = Path(path) + + data = { + 'type': 'BM25DB', + 'bm25_directory': str(bm25_dir), + 'passages_metadata': self._passages_metadata, + 'doc_list': self._doc_list, # Save the actual document content + 'num_passages': len(self._doc_list), + 'num_threads': getattr(self, '_num_threads', 4), + 'k1': self._k1, + 'b': self._b, + 'method': self._method, + 'delta': self._delta, + 'idf_method': self._idf_method, + 'dtype': self._dtype, + 'backend': self._backend, + 'token_pattern': self._token_pattern, + 'stopwords': self._stopwords, + 'stemmer_type': self._get_stemmer_type(), # Save stemmer type, not function + 'lower': self._lower, + 'show_progress': self._show_progress + } + + with open(db_path, 'wb') as f: + pickle.dump(data, f, protocol=pickle.HIGHEST_PROTOCOL) + + db_size = db_path.stat().st_size + bm25_size = sum(f.stat().st_size for f in bm25_dir.rglob('*') if f.is_file()) + total_size = db_size + bm25_size + print(f"BM25 database saved to {db_path} ({db_size / (1024**2):.1f} MB)") + print(f"BM25 index saved to {bm25_dir} ({bm25_size / (1024**2):.1f} MB)") + print(f"Total: {total_size / (1024**2):.1f} MB") + + # Update output size and report performance if benchmarking + if self._benchmark and self._monitor: + self._monitor.set_output_size_callback("bm25_indexing", self._calculate_index_output_size) + # Report performance after serialization so we have accurate output size + if hasattr(self, '_ingestion_start'): + self._report_performance(self._ingestion_start, self._ingestion_item_count, + self._ingestion_total_chars, "BM25DB") + + def from_serialized(self, path: str): + """Load BM25 index and metadata.""" + db_path = Path(path) + + with open(db_path, "rb") as f: + data = pickle.load(f) + + # Check if BM25 parameters match current instance + saved_k1 = data.get('k1', 1.5) + saved_b = data.get('b', 0.75) + saved_method = data.get('method', 'lucene') + + # Only warn about mismatches for explicitly specified parameters (not None) + mismatches = [saved_k1 != self._k1, saved_b != self._b, saved_method != self._method] + + if any(mismatches): + print(f"WARNING: Explicitly specified BM25 parameters don't match database:") + for mismatch in mismatches: + print(f" {mismatch}") + print(f" Using database parameters. To use new parameters, recreate the database.") + + # Always use database parameters (whether there was a warning or not) + self._k1, self._b, self._method = saved_k1, saved_b, saved_method + + # Load all tokenization parameters + self._delta = data.get('delta', 0.5) + self._idf_method = data.get('idf_method', None) + self._dtype = data.get('dtype', 'float32') + self._backend = data.get('backend', 'numba') # Default to numba for speed + self._token_pattern = data.get('token_pattern', r"(?u)\b\w\w+\b") + self._stopwords = data.get('stopwords', 'en') + self._lower = data.get('lower', True) + self._show_progress = data.get('show_progress', True) + + # Recreate stemmer function from saved type + stemmer_type = data.get('stemmer_type', None) + self._stemmer = self._create_stemmer_func(stemmer_type) + + self._passages_metadata = data['passages_metadata'] + self._num_threads = data.get('num_threads', 4) + # Use the data directory based on the database path, or fallback to saved directory + default_bm25_dir = self.get_data_dir(path) + bm25_directory = data.get('bm25_directory', default_bm25_dir) + + # Load BM25 without corpus to avoid the same issue we fixed in __init__ + self._bm25_retriever = bm25s.BM25.load(bm25_directory, load_corpus=False) + + # Load doc_list from saved data if available, otherwise use fallback + if 'doc_list' in data: + self._doc_list = data['doc_list'] + else: + # Fallback for older databases that didn't save doc_list + print("Warning: doc_list not found in database, using URLs as fallback") + self._doc_list = [metadata.get('original_url', '') for metadata in self._passages_metadata] + + print(f"BM25 database loaded from {db_path} ({len(self._doc_list)} passages)") + print(f"BM25 parameters: k1={self._k1}, b={self._b}, method='{self._method}'") \ No newline at end of file diff --git a/e2e/retrieve/filter.py b/e2e/retrieve/filter.py new file mode 100644 index 0000000000..622d6f88ca --- /dev/null +++ b/e2e/retrieve/filter.py @@ -0,0 +1,281 @@ +#!/usr/bin/env python3 +""" +Retrieval Filter: Score-based retrieval methods for dynamic result filtering. + +This module provides adaptive retrieval strategies that adjust result count +based on score distributions rather than fixed-k limits. + +Implements various thresholding approaches: +- Top-p (nucleus sampling) - popular in NLP +- Score threshold - absolute quality bar +- Relative threshold - adaptive to query difficulty +- Elbow method - natural breakpoints +- Percentile-based - statistical cutoffs + +Usage: + from retrieve import retrieval_filter + results = retrieval_filter(rag_db, query, method="relative", ratio=0.75) +""" + +import numpy as np +from typing import List, Tuple, Any, Dict +import math + + +def softmax(scores: List[float], temperature: float = 1.0) -> List[float]: + """Convert scores to probabilities using softmax with temperature scaling. + + Args: + scores: List of scores to convert + temperature: Temperature parameter (lower = sharper distribution) + - temperature = 1.0: standard softmax + - temperature < 1.0: sharper (more weight on top scores) + - temperature > 1.0: smoother (more uniform) + + Returns: + List of probabilities that sum to 1.0 + """ + # Apply temperature scaling + scaled_scores = [s / temperature for s in scores] + exp_scores = [math.exp(s) for s in scaled_scores] + sum_exp = sum(exp_scores) + return [exp_s / sum_exp for exp_s in exp_scores] + + +def top_p_filter(results_with_scores: List[Tuple[Any, float]], p: float = 0.9) -> List[Any]: + """ + Top-p (nucleus) sampling: Take results until cumulative probability >= p + + Args: + results_with_scores: List of (result, score) tuples, sorted by score DESC + p: Cumulative probability threshold (0.8-0.95 typical) + + Returns: + Filtered results list + """ + if not results_with_scores: + return [] + + scores = [score for _, score in results_with_scores] + + #print(f"\n[DEBUG top_p_filter] p={p}, num_candidates={len(scores)}") + #print(f"[DEBUG] Score range: [{min(scores):.4f}, {max(scores):.4f}]") + #print(f"[DEBUG] Score mean: {sum(scores)/len(scores):.4f}") + #print(f"[DEBUG] First 10 scores: {[f'{s:.4f}' for s in scores[:10]]}") + + # Use temperature scaling to sharpen the distribution + # Lower temperature = more discriminative (top docs get higher probability) + # For vector embeddings with compressed L2 distances, use very low temperature + # Temperature = 0.01 to 0.05 for L2 distances in range [0.27-0.42] + temperature = 1 + #temperature = 0.02 + probs = softmax(scores, temperature=temperature) + + #print(f"[DEBUG] Temperature: {temperature}") + #print(f"[DEBUG] Probability range: [{min(probs):.6f}, {max(probs):.6f}]") + #print(f"[DEBUG] First 10 probs: {[f'{p:.6f}' for p in probs[:10]]}") + #print(f"[DEBUG] Prob sum: {sum(probs):.6f}") + + cumulative_prob = 0.0 + selected_results = [] + + for i, ((result, score), prob) in enumerate(zip(results_with_scores, probs)): + cumulative_prob += prob + selected_results.append(result) + + if cumulative_prob >= p: + print(f"[DEBUG] Selected {i+1} documents (cumulative_prob={cumulative_prob:.4f} >= p={p})") + break + + return selected_results + + +def score_threshold_filter(results_with_scores: List[Tuple[Any, float]], + threshold: float, higher_better: bool = True) -> List[Any]: + """ + Absolute score threshold filtering. + + Args: + results_with_scores: List of (result, score) tuples + threshold: Absolute score cutoff + higher_better: If True, keep scores >= threshold, else <= threshold + + Returns: + Filtered results list + """ + selected_results = [] + + for result, score in results_with_scores: + if higher_better and score >= threshold: + selected_results.append(result) + elif not higher_better and score <= threshold: + selected_results.append(result) + + return selected_results + + +def relative_threshold_filter(results_with_scores: List[Tuple[Any, float]], + ratio: float = 0.8) -> List[Any]: + """ + Relative threshold: Keep top ratio fraction of results based on score range. + + For both positive and negative scores: + - Calculates score range between best and worst + - Keeps only results within top ratio% of that range + + Args: + results_with_scores: List of (result, score) tuples (sorted desc, best first) + ratio: Fraction of score range to keep (0.7-0.9 typical) + e.g., 0.9 means keep top 90% of score range + + Returns: + Filtered results list + + Example with negative scores: + Scores: [-0.42, -0.43, -0.44, ..., -0.49] + best=-0.42, worst=-0.49, range=0.07 + ratio=0.9 → cutoff_range = 0.07 * 0.9 = 0.063 + threshold = -0.42 - 0.063 = -0.483 + Keeps: scores >= -0.483 (top 90% of range) + """ + if not results_with_scores: + return [] + + # Get best and worst scores + best_score = results_with_scores[0][1] + worst_score = results_with_scores[-1][1] + + # Calculate the score range + score_range = best_score - worst_score # Always positive since sorted desc + + # Calculate threshold: start from best, move down by (1-ratio) of range + cutoff_distance = score_range * (1 - ratio) + threshold = best_score - cutoff_distance + + return score_threshold_filter(results_with_scores, threshold, higher_better=True) + + +def elbow_method_filter(results_with_scores: List[Tuple[Any, float]]) -> List[Any]: + """ + Elbow method: Find largest score gap and cut there. + + Args: + results_with_scores: List of (result, score) tuples, sorted by score DESC + + Returns: + Filtered results list + """ + if len(results_with_scores) <= 1: + return [result for result, _ in results_with_scores] + + scores = [score for _, score in results_with_scores] + + # Calculate gaps between consecutive scores + gaps = [] + for i in range(len(scores) - 1): + gap = scores[i] - scores[i + 1] # Assuming DESC order + gaps.append(gap) + + # Find largest gap + if not gaps: + return [result for result, _ in results_with_scores] + + max_gap_idx = gaps.index(max(gaps)) + cutoff_point = max_gap_idx + 1 # Include the score before the gap + + return [result for result, _ in results_with_scores[:cutoff_point]] + + +def percentile_filter(results_with_scores: List[Tuple[Any, float]], + percentile: float = 90.0) -> List[Any]: + """ + Percentile-based filtering: Keep top X percentile of scores. + + Args: + results_with_scores: List of (result, score) tuples + percentile: Percentile threshold (80-95 typical) + + Returns: + Filtered results list + """ + if not results_with_scores: + return [] + + scores = [score for _, score in results_with_scores] + threshold = np.percentile(scores, percentile) + + return score_threshold_filter(results_with_scores, threshold, higher_better=True) + + +def filter(rag_db, query: str, method: str = "top_p", + max_results: int = 100, **kwargs) -> List[Any]: + """ + Perform adaptive retrieval using score-based filtering. + + Args: + rag_db: RAG database instance (BM25DB or VectorDB) + query: Search query + method: Filtering method ("top_p", "score_threshold", "relative", "elbow", "percentile") + max_results: Maximum results to retrieve initially + **kwargs: Method-specific parameters + + Returns: + Filtered results list + """ + # Get results with scores + if hasattr(rag_db, 'lookup_with_scores'): + results_with_scores = rag_db.lookup_with_scores(query, k=max_results) + else: + raise ValueError(f"Database {type(rag_db)} doesn't support score-based retrieval") + + # Results already have proper similarity scores (higher is better) from lookup_with_scores + # Sort by score (descending - higher is better) + results_with_scores.sort(key=lambda x: x[1], reverse=True) + + # Apply filtering method + if method == "top_p": + p = kwargs.get("p", 0.9) + return top_p_filter(results_with_scores, p) + + elif method == "score_threshold": + threshold = kwargs.get("threshold", 5.0) # Default for BM25 + return score_threshold_filter(results_with_scores, threshold) + + elif method == "relative": + ratio = kwargs.get("ratio", 0.8) + return relative_threshold_filter(results_with_scores, ratio) + + elif method == "elbow": + return elbow_method_filter(results_with_scores) + + elif method == "percentile": + percentile = kwargs.get("percentile", 90.0) + return percentile_filter(results_with_scores, percentile) + + else: + raise ValueError(f"Unknown filtering method: {method}") + + +def get_score_statistics(rag_db, query: str, k: int = 100) -> Dict[str, float]: + """Get score distribution statistics for threshold calibration.""" + results_with_scores = rag_db.lookup_with_scores(query, k=k) + scores = [score for _, score in results_with_scores] + + if not scores: + return {} + + return { + "min": min(scores), + "max": max(scores), + "mean": np.mean(scores), + "median": np.median(scores), + "std": np.std(scores), + "p75": np.percentile(scores, 75), + "p90": np.percentile(scores, 90), + "p95": np.percentile(scores, 95), + "count": len(scores) + } + + +# Backward compatibility alias +adaptive_retrieval = filter \ No newline at end of file diff --git a/e2e/retrieve/ragdb.py b/e2e/retrieve/ragdb.py new file mode 100644 index 0000000000..dfe82003c1 --- /dev/null +++ b/e2e/retrieve/ragdb.py @@ -0,0 +1,264 @@ +import abc +import os +from typing import List, Dict, Any + +class RagDB(abc.ABC): + """Base class for retrieval-augmented generation databases.""" + + def __init__(self, reranker_model: str = None, device: str = "auto", + benchmark: bool = False, reranker_device: str = None): + self._reranker_model_name = reranker_model + self._device = self._determine_device(device) + # Reranker device defaults to inheriting from --device. + self._reranker_device = self._determine_device(reranker_device) if reranker_device else self._device + self._reranker_queue = None + self._benchmark = benchmark + self._monitor = None + + # Initialize monitoring if benchmark mode enabled + if self._benchmark: + from ingestion_monitor import IngestionMonitor + self._monitor = IngestionMonitor() + + # Initialize out-of-process reranker if specified + if self._reranker_model_name: + self._init_reranker() + + def _determine_device(self, device: str) -> str: + """Determine the best device to use. + + Delegates to utils.detect_device() for auto detection so device-selection + logic lives in one place. ROCm maps to "cuda" + """ + if device == "rocm": + return "cuda" + if device == "auto": + from utils import detect_device + return detect_device() + return device + + @staticmethod + def get_data_dir(db_name: str) -> str: + """Get data directory based on database name.""" + from pathlib import Path + base_name = Path(db_name).stem # Remove .db extension if present + return f"{base_name}_data" + + @staticmethod + def get_db_path(db_name: str) -> str: + """Get database file path based on database name.""" + from pathlib import Path + base_name = Path(db_name).stem # Remove .db extension if present + return f"{base_name}.db" + + def _init_reranker(self): + """Spawn the reranker in its own process.""" + from utils import resolve_gpu_device + from reranker_worker import RerankerQueue + + device = resolve_gpu_device( + self._reranker_device, name="reranker", + override_env="INFERENCE_RERANKER_GPU_DEVICES", + ) + self._reranker_device = device + + numa_node = os.environ.get("INFERENCE_RERANKER_NUMA_NODE") + omp_threads = os.environ.get("INFERENCE_RERANKER_OMP_NUM_THREADS") + print(f"Using {device} for reranker" + f"{f' (NUMA node {numa_node})' if numa_node else ''}" + f"{f' OMP={omp_threads}' if omp_threads else ''}") + + self._reranker_queue = RerankerQueue( + self._reranker_model_name, device=device, + numa_node=int(numa_node) if numa_node is not None else None, + omp_threads=int(omp_threads) if omp_threads else None, + ) + self._reranker_queue.start() + + def _track_component(self, name: str, total_chars: int, item_count: int, func, + is_pipeline_input: bool = False, is_pipeline_output: bool = False): + """Execute function with optional component tracking. + + Args: + name: Component name + total_chars: Input size in bytes + item_count: Number of items processed + func: Function to execute + is_pipeline_input: Mark as pipeline input for aggregation + is_pipeline_output: Mark as pipeline output for aggregation + """ + if self._benchmark and self._monitor: + with self._monitor.track_component(name, input_size_bytes=total_chars, + items_count=item_count, text_only=True, + is_pipeline_input=is_pipeline_input, + is_pipeline_output=is_pipeline_output) as ctx: + result = func() + ctx.add_text_bytes(total_chars) + return result + else: + return func() + + def _start_ingestion_timer(self): + """Start the ingestion timer. Works for both benchmark and non-benchmark modes.""" + import time + if self._benchmark and self._monitor: + self._monitor.start_ingestion() + return time.perf_counter() + + def _report_performance(self, ingestion_start_time: float, item_count: int, total_chars: int, db_type: str): + """Report performance metrics with optional detailed breakdown. + + Args: + ingestion_start_time: Start time from _start_ingestion_timer() (used only in non-benchmark mode) + item_count: Number of items processed + total_chars: Total characters processed + db_type: Database type string for display + """ + import time + + if self._benchmark and self._monitor: + with self._monitor.track_ingestion() as ingestion_ctx: + ingestion_ctx.set_item_count(item_count) + print(f"\n=== {db_type} Performance ===") + self._monitor.print_summary() + else: + end_time = time.perf_counter() + duration = end_time - ingestion_start_time + docs_per_sec = item_count / duration if duration > 0 else 0 + chars_per_sec = total_chars / duration if duration > 0 else 0 + print(f"{db_type} ingestion: {item_count} docs, {total_chars:,} chars in {duration:.2f}s") + print(f" Performance: {docs_per_sec:.1f} docs/sec, {chars_per_sec/1024:.1f} KB/sec") + + def enable_threading(self): + """Enable thread-safe access. Override in subclasses that need locks.""" + pass + + @abc.abstractmethod + def ingest(self, passages: List[str], metadatas: List[Dict[str, Any]]): + """Ingest passages and their metadata into the database.""" + pass + + @abc.abstractmethod + def lookup(self, query: str, k: int) -> List[Any]: + """Retrieve top-k relevant passages for a query.""" + pass + + @abc.abstractmethod + def serialize(self, path: str): + """Serialize the database to disk.""" + pass + + @abc.abstractmethod + def from_serialized(self, path: str): + """Load the database from disk.""" + pass + + def ingest_from_folder(self, folder_path: str, **kwargs): + """Ingest data from a folder. Default implementation raises NotImplementedError.""" + raise NotImplementedError(f"Folder ingestion not supported for {self.__class__.__name__}") + + def ingest_from_file(self, file_path: str, **kwargs): + """Ingest data from a JSON file. Default implementation for JSON files. + + Supports both flat and hierarchical passage formats: + - Flat: entry['passage'] → passage text + - Hierarchical: entry['child_passage'] → child text (for embedding) + entry['parent_passage'] → parent text (stored in metadata) + """ + import json + + with open(file_path, 'r', encoding='utf-8') as f: + payload = json.load(f) + + passage_data = payload.get('passages', []) + config = payload.get('config', {}) + + # Detect hierarchical format + is_hierarchical = ( + config.get('strategy') == 'hierarchical' or + (len(passage_data) > 0 and 'child_passage' in passage_data[0]) + ) + + doc_list = [] + passage_metadata = [] + + if is_hierarchical: + print(f"Detected hierarchical format") + for entry in passage_data: + # Use child for embedding + doc_list.append(entry['child_passage']) + # Store all metadata including parent + metadata = {k: v for k, v in entry.items() if k != 'child_passage'} + passage_metadata.append(metadata) + else: + # Flat format + for entry in passage_data: + doc_list.append(entry['passage']) + passage_metadata.append({k: v for k, v in entry.items() if k != 'passage'}) + + print(f"Ingesting {len(doc_list)} passages from JSON file {file_path}") + return self.ingest(doc_list, passage_metadata, passages_path=file_path, **kwargs) + + def ingest_from_path(self, source_path: str, **kwargs): + """Handle both file and folder ingestion. + + Default implementation that delegates to appropriate methods: + - Folders: calls ingest_from_folder() (may raise NotImplementedError if not overridden) + - Files: calls ingest_from_file() (default JSON implementation) + """ + from pathlib import Path + + source_path = Path(source_path) + + if source_path.is_dir(): + print(f"Ingesting documents from folder {source_path}") + return self.ingest_from_folder(source_path, **kwargs) + elif source_path.is_file(): + return self.ingest_from_file(source_path, **kwargs) + else: + raise ValueError(f"Source path {source_path} is neither a file nor a directory") + + def shutdown_reranker(self): + """Tear down the reranker child process. Safe to call multiple times.""" + if self._reranker_queue is not None: + self._reranker_queue.stop() + self._reranker_queue = None + + def rerank(self, query: str, passages: List[str]): + """Rerank passages via the reranker queue (ColBERT MaxSim).""" + if self._reranker_queue: + return self._reranker_queue.submit(query, passages) + return [(p, 0.0) for p in passages] + + def lookup_with_rerank(self, query: str, k: int, rerank_k: int = None) -> List[Any]: + """Retrieve and rerank passages.""" + if rerank_k is None: + rerank_k = k + + # Get initial results + results = self.lookup(query, k=rerank_k) + + # If no reranker or fewer results than requested, return as-is + if self._reranker_queue is None or len(results) <= k: + return results[:k] + + # Extract passages for reranking + passages = [result.page_content for result in results] + + # Rerank + reranked_passages = self.rerank(query, passages) + + # Map back to original results and return top-k + reranked_results = [] + for passage, score in reranked_passages[:k]: + for result in results: + if result.page_content == passage: + reranked_results.append(result) + break + + return reranked_results + + @property + def device(self) -> str: + """Get the device being used.""" + return self._device diff --git a/e2e/retrieve/vectordb.py b/e2e/retrieve/vectordb.py new file mode 100644 index 0000000000..c74a7596e8 --- /dev/null +++ b/e2e/retrieve/vectordb.py @@ -0,0 +1,823 @@ +import os +import sys +import faiss +import torch +import numpy as np +from langchain_community.docstore.in_memory import InMemoryDocstore +from langchain_community.vectorstores import FAISS +from langchain_huggingface import HuggingFaceEmbeddings +from typing import List +from transformers import AutoModelForSequenceClassification, AutoTokenizer +from .ragdb import RagDB + + +def _alias_missing_faiss_swigfaiss_modules() -> None: + """Make all FAISS SWIG submodules resolve to whichever build is available. + + `langchain_community.FAISS.serialize_to_bytes` pickles the FAISS index using + Python's pickle protocol, which stores fully-qualified class paths like + "faiss.swigfaiss_avx512.IndexHNSWFlat". When a `.db` file is loaded on a + machine whose installed `faiss-cpu` wheel does not ship that exact SWIG + submodule (different version, ARM build, conda build, CPU without AVX-512, + etc.), unpickling fails with `ModuleNotFoundError: No module named + 'faiss.swigfaiss_avx512'`. + + All FAISS SWIG submodules expose the same class names (the AVX variants + are SIMD-optimized builds of the same C++ classes), so we can safely alias + any missing submodule to the one that did get built. This keeps existing + serialized databases loadable across heterogeneous installs without + rebuilding them. + """ + candidates = ("swigfaiss_avx512_spr", "swigfaiss_avx512", "swigfaiss_avx2", "swigfaiss") + available = None + for name in candidates: + full = f"faiss.{name}" + if full in sys.modules: + available = sys.modules[full] + break + try: + available = __import__(full, fromlist=[name]) + break + except ImportError: + continue + if available is None: + return # Nothing we can do; let pickle raise the original error. + for name in candidates: + full = f"faiss.{name}" + if full not in sys.modules: + try: + __import__(full) + except ImportError: + sys.modules[full] = available + +# Worker function for parallel embedding generation (must be at module level for multiprocessing) +def _parallel_embed_worker(device_id, input_chunk_indices, input_chunks, result_queue, + model_name, encode_kwargs, base_device, numa_plan=None): + """Worker function to generate embeddings on a specific device. + + This worker processes multiple input chunks on a single device to avoid + loading the model multiple times. + + Args: + device_id: Device index assigned by DeviceAllocator (e.g. 2 for xpu:2). + input_chunk_indices: Position(s) in the parent's `input_chunks` list. + Used as a sortable key so the parent can reassemble + in order even when workers finish out of order. + input_chunks: Per-worker shards of passages. + result_queue: Multiprocessing queue for results. + model_name: Name of the embedding model. + encode_kwargs: Encoding arguments. + base_device: Base device type ('xpu', 'cuda', 'hpu', 'cpu'). + numa_plan: Optional (node, cpu_set) tuple. When set, this worker pins + itself to that node before importing torch. + """ + try: + import os + + # NUMA pinning must happen before torch import + before allocations. + if numa_plan is not None: + from utils import pin_worker_to_node + node, cpu_set = numa_plan + pin_worker_to_node(node, cpu_set) + + import torch + from langchain_huggingface import HuggingFaceEmbeddings + + # Format device string: CPU doesn't use indices, others do + if base_device == 'cpu': + device = 'cpu' + if numa_plan is None: + from utils import apply_cpu_threading_env + apply_cpu_threading_env() + elif base_device == 'hpu': + device = 'hpu' + import habana_frameworks.torch.core as htcore + else: + device = f'{base_device}:{device_id}' + + model_kwargs = {'device': device, 'local_files_only': True} + + # Load model once for this device + embedder = HuggingFaceEmbeddings( + model_name=model_name, + model_kwargs=model_kwargs, + encode_kwargs=encode_kwargs + ) + + print(f"✓ Device {device}: Loaded model, processing {len(input_chunks)} input chunk(s)") + + for input_chunk_idx, input_chunk in zip(input_chunk_indices, input_chunks): + embeddings = embedder.embed_documents(input_chunk) + result_queue.put((input_chunk_idx, embeddings)) + + except Exception as e: + print(f"❌ Error on device {device_id} ({base_device}): {e}") + import traceback + traceback.print_exc() + for input_chunk_idx in input_chunk_indices: + result_queue.put((input_chunk_idx, None)) + +class VectorDB(RagDB): + @classmethod + def get_default_db_name(cls) -> str: + """Get the default database filename for VectorDB.""" + return "vector.db" + + def __init__(self, + retriever_model: str = None, + reranker_model: str = None, + device: str = "auto", + vector_index_method: str = "hnsw", + ivf_nprobe: int = 10, + load_embeddings: bool = True, + num_embedding_devices: int = 1, + benchmark: bool = False, + hierarchical: bool = False, + embedding_device: str = None, + reranker_device: str = None, + **kwargs + ): + super().__init__(reranker_model, device, benchmark, reranker_device=reranker_device) + self._retriever_model_name = retriever_model + self._reranker_model_name = reranker_model + self._vector_index_method = vector_index_method + self._ivf_nprobe = ivf_nprobe + self._load_embeddings = load_embeddings + self._num_embedding_devices = num_embedding_devices + self._hierarchical = hierarchical + self._embedding_lock = None + # Embedding device defaults to inheriting from --device. + self._embedding_device = self._determine_device(embedding_device) if embedding_device else self._device + + # For hierarchical mode: map child_index -> parent_passage + self._parent_map = {} + + if self._embedding_device == "hpu": + import habana_frameworks.torch.core as htcore + os.environ["PT_HPU_LAZY_MODE"] = "1" + + if self._embedding_device == "cpu": + from utils import apply_cpu_threading_env + apply_cpu_threading_env() + + # For single-device embedding, allocate one GPU now so the reranker + # (allocated later) can't pick the same one. Multi-device path + # allocates inside _embed_documents_parallel. + if num_embedding_devices == 1 and self._embedding_device in ("cuda", "xpu"): + from utils import resolve_gpu_device + self._embedding_device = resolve_gpu_device( + self._embedding_device, name="embedding", + override_env="INFERENCE_EMBEDDING_GPU_DEVICES", + ) + + # Initialize embedding model with device configuration + model_kwargs = {'device': self._embedding_device, 'local_files_only': True} + encode_kwargs = {'normalize_embeddings': True} + + self._embedding_model = HuggingFaceEmbeddings( + model_name=self._retriever_model_name, + model_kwargs=model_kwargs, + encode_kwargs=encode_kwargs + ) + self._embedding_dimension = len(self._embedding_model.embed_query("hello world")) + + # Check the dtype of the embedding without using numpy + test_embedding_raw = self._embedding_model.embed_query("test") + + # Calculate dtype and itemsize from Python native list + if isinstance(test_embedding_raw, list) and len(test_embedding_raw) > 0: + test_element = test_embedding_raw[0] + embedding_dtype = type(test_element) + embedding_itemsize = test_element.__sizeof__() # Size in bytes of one element + self._embedding_bytes_per_element = embedding_itemsize + else: + raise ValueError("Embedding query did not return a valid list of floats.") + + if self._benchmark: + print(f" Embedding element type: {embedding_dtype}") + print(f" Bytes per element: {embedding_itemsize}") + + # The index defines the algorithm used for the similarity search + # Support multiple vector index types (currently FAISS-based) + self._index = self._create_vector_index(self._vector_index_method, self._embedding_dimension) + + # The docstore is used to store the documents and their metadata + self._docstore = InMemoryDocstore() + + self._vector_store = FAISS( + embedding_function=self._embedding_model, + index=self._index, + docstore=self._docstore, + index_to_docstore_id={}, # This will be populated as documents are added + ) + + # Keep track of ingested documents for consistency with BM25DB + self._doc_list = [] + + def _create_vector_index(self, method: str, dimension: int): + """Create a vector index based on the specified method. + + Currently uses FAISS backend, but abstracted to allow future support + for other vector databases (e.g., Milvus, Qdrant, Weaviate). + + Args: + method: Index method - 'flat', 'hnsw', or 'ivf' + dimension: Embedding dimension + + Returns: + Vector index object (FAISS index) + + Index Method Details: + + 1. FLAT (IndexFlatL2): + - Exact brute-force search using L2 distance + - Pros: Perfect accuracy, simple + - Cons: O(N) search time, slow for large datasets + - Best for: Small datasets (<10K), when accuracy is critical + + 2. HNSW (Hierarchical Navigable Small World): + - Graph-based approximate nearest neighbor search + - Pros: Very fast search O(log N), excellent recall, no training needed + - Cons: Higher memory usage (stores graph), slower indexing + - Best for: Most use cases, default choice + + 3. IVF (Inverted File): + - Clustering-based approximate search + - Parameters: + * nlist: number of clusters (auto-adjusted to ~2*sqrt(N)) + * nprobe: clusters to search per query (default: 10) + - nprobe=1: fastest but lowest accuracy (~80-90%) + - nprobe=10: good balance (~95-98% accuracy) + - nprobe=50: high accuracy (~99%) but slower + - Pros: Memory efficient, good for large datasets, faster than flat + - Cons: Requires training, slightly lower recall than HNSW + - Best for: Very large datasets (>1M), when memory is limited + """ + if method == "flat": + return faiss.IndexFlatL2(dimension) + elif method == "hnsw": + # M: number of connections per layer (higher = better recall, more memory) + # efConstruction: quality of index construction (higher = better quality, slower build) + M = 32 # Default: 32, good balance + index = faiss.IndexHNSWFlat(dimension, M) + index.hnsw.efConstruction = 200 # Default: 40 + index.hnsw.efSearch = 100 # Search-time parameter, can be adjusted later + return index + elif method == "ivf": + # nlist: number of clusters/cells (sqrt(N) is a good heuristic for N docs) + nlist = 100 # Will be adjusted based on dataset size during training + quantizer = faiss.IndexFlatL2(dimension) + index = faiss.IndexIVFFlat(quantizer, dimension, nlist) + # Note: IVF index needs training before use (will be done during ingest) + return index + else: + raise ValueError(f"Unknown vector index method: {method}. Choose 'flat', 'hnsw', or 'ivf'.") + + def _train_vector_index(self, index, embeddings: np.ndarray): + """Train IVF index on embeddings if needed. + + IVF (Inverted File Index) requires a one-time training phase to: + 1. Cluster the embedding space into nlist regions using k-means + 2. Build an inverted index mapping cluster_id -> vector_ids + + After training, search works by: + 1. Finding the nprobe nearest cluster centroids to the query + 2. Searching only within those clusters (much faster than full scan) + + Note: In incremental scenarios, this trains on the FIRST batch only. + Subsequent batches are assigned to existing clusters without retraining. + For production systems handling continuous data growth, consider: + - Periodic retraining when dataset size doubles + - Using all accumulated data for retraining + - Online clustering algorithms that adapt to new data + + Args: + index: FAISS index (only IVF types need training) + embeddings: Numpy array of embeddings to train on + """ + import numpy as np + + # Convert embeddings to numpy array + embeddings_array = np.array(embeddings).astype('float32') + + # Adjust nlist (number of clusters) based on dataset size + # Rule of thumb: nlist = sqrt(N) to 4*sqrt(N) + n_samples = len(embeddings) + optimal_nlist = max(10, min(int(np.sqrt(n_samples) * 2), 1000)) + + # Update nlist if different from default + if optimal_nlist != self._index.nlist: + print(f"Adjusting IVF nlist from {self._index.nlist} to {optimal_nlist} based on {n_samples} samples") + # Need to recreate index with new nlist + quantizer = faiss.IndexFlatL2(self._embedding_dimension) + self._index = faiss.IndexIVFFlat(quantizer, self._embedding_dimension, optimal_nlist) + # Update vector store's index + self._vector_store.index = self._index + + print(f"Training IVF index on {n_samples} samples...") + self._index.train(embeddings_array) + + # Set nprobe (number of clusters to search) for better accuracy + self._index.nprobe = self._ivf_nprobe + print(f"IVF index trained successfully with {self._index.nlist} clusters, nprobe={self._ivf_nprobe}") + print(f" → Will search {self._ivf_nprobe} clusters per query (~{100*self._ivf_nprobe/self._index.nlist:.1f}% of clusters)") + + def _get_embeddings_cache_path(self, passages_path: str) -> str: + """Get the cache path for embeddings based on passages file path.""" + from pathlib import Path + passages_path = Path(passages_path) + # Replace extension with .emb.pkl + cache_path = passages_path.with_suffix('.emb.pkl') + return str(cache_path) + + def _save_embeddings_cache(self, embeddings: list, passages_path: str): + """Save embeddings to a pickle file for reuse.""" + import os, pickle + from pathlib import Path + + cache_path = self._get_embeddings_cache_path(passages_path) + Path(cache_path).parent.mkdir(parents=True, exist_ok=True) + + if os.path.exists(cache_path): + print(f"Embeddings cache exists: {cache_path}") + return + + with open(cache_path, 'wb') as f: + pickle.dump(embeddings, f) + print(f"💾 Saved embeddings cache to {cache_path}") + + def _load_embeddings_cache(self, passages_path: str) -> list: + """Load embeddings from cache if available.""" + import pickle + from pathlib import Path + + cache_path = self._get_embeddings_cache_path(passages_path) + if not Path(cache_path).exists(): + return None + + try: + with open(cache_path, 'rb') as f: + embeddings = pickle.load(f) + print(f"✓ Loaded embeddings from cache: {cache_path}") + return embeddings + except Exception as e: + print(f"⚠️ Failed to load embeddings cache: {e}") + return None + + def _build_numa_plans(self, num_workers: int): + """Parse INFERENCE_EMBEDDING_NUMA_NODES into per-worker (node, cpu_set). + + Returns None if the env var isn't set (no NUMA pinning). + """ + nodes_env = os.environ.get("INFERENCE_EMBEDDING_NUMA_NODES") + if not nodes_env: + return None + + try: + numa_nodes = [int(x) for x in nodes_env.split(",") if x.strip()] + except ValueError: + raise RuntimeError( + f"Invalid INFERENCE_EMBEDDING_NUMA_NODES={nodes_env!r}; " + f"expected comma-separated ints" + ) + if len(numa_nodes) != num_workers: + raise RuntimeError( + f"INFERENCE_EMBEDDING_NUMA_NODES has {len(numa_nodes)} entries " + f"but num_embedding_devices={num_workers}" + ) + + omp_env = os.environ.get("INFERENCE_EMBEDDING_OMP_NUM_THREADS") + omp_per_worker = int(omp_env) if omp_env else 0 + + from utils import slice_cores_for_workers + return slice_cores_for_workers(numa_nodes, omp_per_worker) + + def _embed_documents_parallel(self, passages: List[str]) -> list: + """Generate embeddings using multiple devices in parallel. + + Uses the device type from --device option and spawns multiple workers. + + Args: + passages: List of text passages to embed + + Returns: + List of embeddings (one per passage) + """ + import torch + import multiprocessing as mp + + # Use the embedding device (may differ from the global --device). + # Strip any GPU index already allocated for the single-device path so + # the parallel allocator gets a clean device-type string. + base_device = self._embedding_device.split(":")[0] # e.g., 'xpu', 'cuda', 'cpu', 'hpu' + + num_workers = min(self._num_embedding_devices, len(passages)) + + if num_workers <= 1: + # Fallback to single device + return self._embedding_model.embed_documents(passages) + + # Resolve per-worker device indices. + if base_device in ("cuda", "xpu"): + from utils import get_device_allocator + device_indices = get_device_allocator(base_device).allocate( + count=num_workers, name="parallel-embedding", + override_env="INFERENCE_EMBEDDING_GPU_DEVICES", + ) + else: + # CPU / HPU: workers use the same device string; index field is ignored. + device_indices = list(range(num_workers)) + + # Set spawn method for device compatibility (required for XPU/CUDA) + try: + mp.set_start_method('spawn', force=True) + except RuntimeError: + # Already set, ignore + pass + + print(f"🚀 Parallel embedding on {num_workers} {base_device.upper()} device(s)...") + + # Optional per-worker NUMA pinning (CPU + memory + OMP). + numa_plans = self._build_numa_plans(num_workers) + + # Split passages into per-worker input chunks. + input_chunk_size = (len(passages) + num_workers - 1) // num_workers + input_chunks = [passages[i:i + input_chunk_size] for i in range(0, len(passages), input_chunk_size)] + + print(f" Split {len(passages)} passages into {len(input_chunks)} input chunk(s) (~{input_chunk_size} passages/device)") + + result_queue = mp.Queue() + processes = [] + + encode_kwargs = {'normalize_embeddings': True} + + # input_chunk_idx is the position in `input_chunks`; device_id is the GPU + # index from the allocator. They differ when the allocator returns + # non-contiguous indices. + for input_chunk_idx, device_id in enumerate(device_indices[:len(input_chunks)]): + numa_plan = numa_plans[input_chunk_idx] if numa_plans else None + p = mp.Process(target=_parallel_embed_worker, + args=(device_id, [input_chunk_idx], [input_chunks[input_chunk_idx]], result_queue, + self._retriever_model_name, encode_kwargs, base_device, numa_plan)) + p.start() + processes.append(p) + + results = {} + for _ in range(min(num_workers, len(input_chunks))): + input_chunk_idx, embeddings = result_queue.get() + if embeddings is not None: + results[input_chunk_idx] = embeddings + + for p in processes: + p.join() + + # Reassemble in original passage order. + all_embeddings = [] + for i in range(len(input_chunks)): + if i in results: + all_embeddings.extend(results[i]) + + print(f"✓ Generated {len(all_embeddings)} embeddings across {num_workers} devices") + + return all_embeddings + + def _calculate_index_output_size(self): + """Calculate the size of VectorDB output data (db file - metadata). + + Returns the total size in bytes of the serialized database file, + excluding configuration metadata overhead. + + The .db file contains: + - FAISS index (vectors) + - Passages (docstore) + - Metadata (small overhead) + + We estimate metadata size and subtract it from total file size. + """ + from pathlib import Path + + # VectorDB uses serialize path, not _database_name like BM25 + if not hasattr(self, '_serialize_path') or not self._serialize_path: + return 0 + + db_path = Path(self._serialize_path) + if not db_path.exists(): + return 0 + + total_file_size = db_path.stat().st_size + return total_file_size + + def ingest(self, passages: List[str], metadatas: List[dict], **kwargs): + """Ingest passages with performance monitoring. + + For hierarchical mode: + - passages: list of child passage texts (for embedding) + - metadatas: list of dicts with 'parent_passage' key (for retrieval) + """ + # Handle BM25-specific parameters gracefully + if 'num_threads' in kwargs: + print(f"Warning: num_threads parameter is not used in VectorDB, ignoring") + + # Extract passages source path for embeddings caching + passages_path = kwargs.get('passages_path', None) + + # Start timing (works for both benchmark and non-benchmark modes) + ingestion_start = self._start_ingestion_timer() + + # For hierarchical mode: extract parent passages and build mapping + if self._hierarchical: + print("Hierarchical mode: Indexing children, storing parents for retrieval") + for idx, metadata in enumerate(metadatas): + if 'parent_passage' in metadata: + self._parent_map[idx] = metadata['parent_passage'] + print(f" Stored {len(self._parent_map)} parent mappings") + + total_chars = sum(len(passage) for passage in passages) + + # Handle embeddings: try to load from cache or generate new ones + embeddings = None + + if self._load_embeddings and passages_path: + embeddings = self._load_embeddings_cache(passages_path) + + # Generate embeddings if not cached + if embeddings is None: + if self._num_embedding_devices > 1: + # Use parallel embedding generation across multiple devices + embeddings = self._track_component("embedding_generation", total_chars, len(passages), + lambda: self._embed_documents_parallel(passages), + is_pipeline_input=True) + else: + # Single device embedding generation + embeddings = self._track_component("embedding_generation", total_chars, len(passages), + lambda: self._embedding_model.embed_documents(passages), + is_pipeline_input=True) + + # Train IVF index if needed (before adding any embeddings) + if self._vector_index_method == "ivf" and not self._index.is_trained: + self._train_vector_index(self._index, embeddings) + + # Determine batch size: single batch for small datasets, multiple batches for scaling analysis + track_incremental = self._benchmark and self._monitor and len(passages) >= 500 + if track_incremental: + batch_size = max(1000, len(passages) // 10) # 10 batches, minimum 1000 docs per batch + print(f"🔬 Incremental indexing analysis: {len(passages)} docs in batches of {batch_size}") + else: + batch_size = len(passages) # Single batch + + # Track total indexing time for component metrics + import time + indexing_component_start = time.perf_counter() + + # Process in batches + for i in range(0, len(passages), batch_size): + batch_end = min(i + batch_size, len(passages)) + self._ingest_single_batch(passages, metadatas, embeddings, i, batch_end, track_incremental) + + indexing_component_end = time.perf_counter() + indexing_component_duration = indexing_component_end - indexing_component_start + + # Create component metrics for the entire indexing operation + if not track_incremental: + # For single batch, component was tracked inside _ingest_single_batch + pass + elif self._monitor: + # For incremental, create component metrics here for the entire operation + embedding_bytes = len(passages) * self._embedding_dimension * self._embedding_bytes_per_element + + from ingestion_monitor import ComponentMetrics + self._monitor.components["faiss_indexing"] = ComponentMetrics( + name="faiss_indexing", + duration=indexing_component_duration, + input_size_bytes=embedding_bytes, + output_size_bytes=embedding_bytes, # Vectors stored in FAISS index + items_processed=len(passages), + throughput_mb_per_sec=(embedding_bytes / (1024 * 1024)) / indexing_component_duration if indexing_component_duration > 0 else 0, + throughput_items_per_sec=len(passages) / indexing_component_duration if indexing_component_duration > 0 else 0, + is_pipeline_input=False, + is_pipeline_output=True + ) + + # Store ingestion metrics for later reporting + self._ingestion_start = ingestion_start + self._ingestion_item_count = len(passages) + self._ingestion_total_chars = total_chars + + # Save embeddings to cache + if self._load_embeddings and passages_path: + self._save_embeddings_cache(embeddings, passages_path) + + def _ingest_single_batch(self, passages: List[str], metadatas: List[dict], embeddings: list, + batch_start: int, batch_end: int, track_incremental: bool): + """Ingest a batch of passages. Can be used for single or incremental indexing. + + Args: + passages: All passages + metadatas: All metadata + embeddings: All embeddings + batch_start: Start index for this batch + batch_end: End index for this batch (exclusive) + track_incremental: Whether to track this batch for incremental analysis + """ + import time + + # Extract batch data + batch_passages = passages[batch_start:batch_end] + batch_metadatas = metadatas[batch_start:batch_end] if metadatas else [{}] * (batch_end - batch_start) + batch_embeddings = embeddings[batch_start:batch_end] + + # Track DB size before adding (for incremental tracking) + db_size_before = len(self._doc_list) if track_incremental else 0 + + # Calculate embedding size for this batch + batch_embedding_bytes = len(batch_passages) * self._embedding_dimension * self._embedding_bytes_per_element + + # Time and execute indexing operation + indexing_start = time.perf_counter() + + if track_incremental: + # For incremental: just add embeddings without component tracking + self._vector_store.add_embeddings( + list(zip(batch_passages, batch_embeddings)), + batch_metadatas + ) + else: + # For single batch: use component tracking + self._track_component("faiss_indexing", batch_embedding_bytes, len(batch_passages), + lambda: self._vector_store.add_embeddings( + list(zip(batch_passages, batch_embeddings)), batch_metadatas), + is_pipeline_output=True) + + indexing_end = time.perf_counter() + indexing_time = indexing_end - indexing_start + + # Update document list + self._doc_list.extend(batch_passages) + + # Track for incremental analysis if requested + if track_incremental and self._monitor: + self._monitor.track_incremental_indexing( + db_size_before=db_size_before, + batch_size=len(batch_passages), + indexing_time=indexing_time + ) + + def enable_threading(self): + """Enable thread-safe access to the embedding model.""" + import threading + self._embedding_lock = threading.Lock() + + def embed_query(self, query: str) -> List[float]: + """Embed a single query string. Caller must hold _embedding_lock if threading.""" + return self._embedding_model.embed_query(query) + + def lookup(self, query: str, k: int): + if self._embedding_lock: + with self._embedding_lock: + embedding = self.embed_query(query) + else: + embedding = self.embed_query(query) + results = self._vector_store.similarity_search_by_vector(embedding, k=k) + + # In hierarchical mode: replace child passages with parents, deduplicate + if self._hierarchical and self._parent_map: + parent_docs = [] + seen_parents = set() + + for doc in results: + # Get document index from metadata + doc_idx = doc.metadata.get('index', None) + if doc_idx is not None and doc_idx in self._parent_map: + parent_text = self._parent_map[doc_idx] + # Deduplicate: only add each unique parent once + if parent_text not in seen_parents: + # Create new document with parent content + from langchain_core.documents import Document + parent_doc = Document( + page_content=parent_text, + metadata=doc.metadata.copy() + ) + parent_docs.append(parent_doc) + seen_parents.add(parent_text) + + # Stop if we have k unique parents + if len(parent_docs) >= k: + break + else: + # No parent mapping, use original + parent_docs.append(doc) + + return parent_docs + + return results + + def lookup_with_scores(self, query: str, k: int): + """ + Lookup documents with similarity scores. + Returns list of (document, score) tuples. + + Note: FAISS returns L2 distances (lower is better), but we convert to + similarity scores (higher is better) for consistency with BM25. + """ + if self._embedding_lock: + with self._embedding_lock: + embedding = self.embed_query(query) + else: + embedding = self.embed_query(query) + results_with_scores = self._vector_store.similarity_search_with_score_by_vector(embedding, k=k) + + # FAISS returns (document, distance) where distance is L2 distance (lower is better) + # Convert to similarity score (higher is better) by negating + # This makes it consistent with BM25 scores for filtering algorithms + results_with_similarity = [(doc, -distance) for doc, distance in results_with_scores] + + # In hierarchical mode: replace child passages with parents, deduplicate + if self._hierarchical and self._parent_map: + parent_results = [] + seen_parents = set() + + for doc, score in results_with_similarity: + # Get document index from metadata + doc_idx = doc.metadata.get('index', None) + if doc_idx is not None and doc_idx in self._parent_map: + parent_text = self._parent_map[doc_idx] + # Deduplicate: only add each unique parent once + if parent_text not in seen_parents: + # Create new document with parent content + from langchain_core.documents import Document + parent_doc = Document( + page_content=parent_text, + metadata=doc.metadata.copy() + ) + parent_results.append((parent_doc, score)) + seen_parents.add(parent_text) + + # Stop if we have k unique parents + if len(parent_results) >= k: + break + else: + # No parent mapping, use original + parent_results.append((doc, score)) + + return parent_results + + return results_with_similarity + + + + def serialize(self, path: str): + # Store path for output size calculation + self._serialize_path = path + + data = self._vector_store.serialize_to_bytes() + with open(path, "wb") as f: + f.write(data) + + # Save parent map if hierarchical mode + if self._hierarchical and self._parent_map: + import pickle + from pathlib import Path + parent_map_path = Path(path).with_suffix('.parent_map.pkl') + with open(parent_map_path, 'wb') as f: + pickle.dump(self._parent_map, f) + print(f"💾 Saved parent map ({len(self._parent_map)} entries) to {parent_map_path}") + + # Update output size after serialization (now file exists) + if self._benchmark and self._monitor: + self._monitor.set_output_size_callback("faiss_indexing", self._calculate_index_output_size) + + # Report performance after serialization if benchmarking + if self._benchmark and self._monitor and hasattr(self, '_ingestion_start'): + # Determine db_type based on whether incremental was used + db_type = "VectorDB (Incremental)" if hasattr(self._monitor, 'indexing_trend') and len(self._monitor.indexing_trend) > 0 else "VectorDB" + self._report_performance(self._ingestion_start, self._ingestion_item_count, + self._ingestion_total_chars, db_type) + + def from_serialized(self, path: str): + assert len(self._vector_store.index_to_docstore_id) == 0, "Vector store already has documents" + # Pickled FAISS indexes reference a specific SWIG submodule (e.g. + # `faiss.swigfaiss_avx512`). Alias any missing submodules so DBs built + # on one host load on hosts with a different faiss-cpu build. + _alias_missing_faiss_swigfaiss_modules() + with open(path, "rb") as f: + data = f.read() + self._vector_store = FAISS.deserialize_from_bytes(embeddings=self._embedding_model, + serialized=data, + allow_dangerous_deserialization=True) # <--- USE WITH CAUTION - Only deserialize files you trust + + # If it's an IVF index, restore nprobe setting + if self._vector_index_method == "ivf" and hasattr(self._vector_store.index, 'nprobe'): + self._vector_store.index.nprobe = self._ivf_nprobe + print(f"Restored IVF index with nprobe={self._ivf_nprobe}") + + # Load parent map if hierarchical mode + if self._hierarchical: + import pickle + from pathlib import Path + parent_map_path = Path(path).with_suffix('.parent_map.pkl') + if parent_map_path.exists(): + with open(parent_map_path, 'rb') as f: + self._parent_map = pickle.load(f) + print(f"✓ Loaded parent map ({len(self._parent_map)} entries) from {parent_map_path}") + else: + print(f"⚠️ Warning: Hierarchical mode enabled but no parent map found at {parent_map_path}") diff --git a/e2e/scripts/calc_prefix_cache_rate.py b/e2e/scripts/calc_prefix_cache_rate.py new file mode 100644 index 0000000000..abb901381c --- /dev/null +++ b/e2e/scripts/calc_prefix_cache_rate.py @@ -0,0 +1,223 @@ +#!/usr/bin/env python3 +"""Calculate prefix cache hit rate from multi-hop RAG LLM logs.""" + +import json +import os +import sys +from collections import defaultdict +from pathlib import Path +from tokenizers import Tokenizer + +BLOCK_SIZE = 16 +HF_CACHE = Path(os.environ.get("HF_HOME", Path.home() / ".cache" / "huggingface")) / "hub" + + +def find_token_prefix_len(tokens1, tokens2): + for i in range(min(len(tokens1), len(tokens2))): + if tokens1[i] != tokens2[i]: + return i + return min(len(tokens1), len(tokens2)) + + +def simulate_prefix_cache(calls, block_size=BLOCK_SIZE): + previous_tokens = [] + results_by_hop = defaultdict(lambda: {"total_isl": 0, "cached": 0, "count": 0}) + total_isl = 0 + total_cached = 0 + + for call in calls: + tokens = call["_tokens"] + isl = call["metrics"]["isl"] + template_overhead = isl - len(tokens) + + best_prefix = 0 + for prev in previous_tokens: + best_prefix = max(best_prefix, find_token_prefix_len(tokens, prev)) + + if not previous_tokens: + cached = 0 + else: + cached = min((best_prefix // block_size) * block_size + template_overhead, isl) + + hop = call["hop_count"] + results_by_hop[hop]["total_isl"] += isl + results_by_hop[hop]["cached"] += cached + results_by_hop[hop]["count"] += 1 + total_isl += isl + total_cached += cached + + previous_tokens.append(tokens) + + return total_isl, total_cached, results_by_hop + + +MODEL_PATTERNS = { + "gpt-oss-20b": "openai/gpt-oss-20b", + "gpt-oss-120b": "openai/gpt-oss-120b", +} + + +def normalize_model_name(model_name): + for pattern, hf_name in MODEL_PATTERNS.items(): + if pattern in model_name: + return hf_name + return model_name + + +def resolve_tokenizer(model_name, explicit_path=None): + if explicit_path: + return Tokenizer.from_file(explicit_path) + hf_name = normalize_model_name(model_name) + cache_dir = HF_CACHE / f"models--{hf_name.replace('/', '--')}" + if not cache_dir.exists(): + sys.exit(f"Error: tokenizer not found for '{model_name}' (resolved: '{hf_name}') at {cache_dir}\n" + f" Provide explicit path or run: huggingface-cli download {hf_name} tokenizer.json") + snapshots = cache_dir / "snapshots" + snapshot = next(snapshots.iterdir()) + tok_file = snapshot / "tokenizer.json" + if not tok_file.exists(): + sys.exit(f"Error: no tokenizer.json in {snapshot}") + return Tokenizer.from_file(str(tok_file)) + + +def main(): + import argparse + parser = argparse.ArgumentParser(description="Calculate prefix cache hit rate from multi-hop RAG LLM logs.") + parser.add_argument("llm_log", help="Path to LLM log JSON file") + parser.add_argument("--tokenizer-grader", help="Explicit tokenizer path for grader model") + parser.add_argument("--tokenizer-llm", help="Explicit tokenizer path for LLM model (query/sufficiency/answer)") + parser.add_argument("--block-size", type=int, default=BLOCK_SIZE, + help=f"KV cache block size in tokens (default: {BLOCK_SIZE})") + args = parser.parse_args() + + log_path = args.llm_log + explicit_20b = args.tokenizer_grader + explicit_120b = args.tokenizer_llm + block_size = args.block_size + + with open(log_path) as f: + data = json.load(f) + + meta = data["experiment_metadata"] + print(f"Experiment: {meta['experiment_name']}") + print(f"Models: grader={meta['grader_model']}, " + f"query={meta['query_model']}, " + f"sufficiency={meta['sufficiency_checker_model']}") + print(f"Block size: {block_size}") + print() + + model_to_tokenizer = {} + component_models = { + "evaluate_document_relevance": meta["grader_model"], + "generate_search_queries": meta["query_model"], + "check_sufficiency": meta["sufficiency_checker_model"], + "answer_generator": meta["answer_generator_model"], + } + unique_models = list(dict.fromkeys(normalize_model_name(m) for m in component_models.values())) + + for i, model in enumerate(unique_models): + explicit = [explicit_20b, explicit_120b][min(i, 1)] if (explicit_20b or explicit_120b) else None + model_to_tokenizer[model] = resolve_tokenizer(model, explicit) + + component_tokenizer = { + comp: model_to_tokenizer[normalize_model_name(model)] + for comp, model in component_models.items() + } + + all_calls = [] + for query in data["queries"]: + for call in query["llm_calls"]: + call["query_id"] = query["query_id"] + all_calls.append(call) + + all_calls.sort(key=lambda x: x["timestamp"]) + + for call in all_calls: + prompt = "\n".join(msg["content"] for msg in call["input"]["messages"]) + tok = component_tokenizer[call["component"]] + call["_tokens"] = tok.encode(prompt).ids + + by_component = defaultdict(list) + for call in all_calls: + by_component[call["component"]].append(call) + + model_map = { + "evaluate_document_relevance": data["experiment_metadata"]["grader_model"], + "generate_search_queries": data["experiment_metadata"]["query_model"], + "check_sufficiency": data["experiment_metadata"]["sufficiency_checker_model"], + "answer_generator": data["experiment_metadata"]["answer_generator_model"], + } + + components = ["check_sufficiency", "generate_search_queries", + "evaluate_document_relevance", "answer_generator"] + + # --- ISL / OSL table --- + print("=" * 90) + print("Table 1: Input/Output Sequence Length Statistics") + print("-" * 90) + print(f"{'Component':<30} {'N':>4} {'ISL (min/avg/max/eff)':^28} {'OSL (min/avg/max)':^21}") + print("-" * 90) + + comp_cache_results = {} + grand_isl = 0 + grand_cached = 0 + endpoint_stats = defaultdict(lambda: {"isl": 0, "cached": 0}) + + for comp in components: + calls = sorted(by_component[comp], key=lambda x: x["timestamp"]) + total_isl, total_cached, by_hop = simulate_prefix_cache(calls, block_size) + comp_cache_results[comp] = (total_isl, total_cached, by_hop) + grand_isl += total_isl + grand_cached += total_cached + + model = normalize_model_name(model_map[comp]) + endpoint_stats[model]["isl"] += total_isl + endpoint_stats[model]["cached"] += total_cached + + isls = [c["metrics"]["isl"] for c in calls] + osls = [c["metrics"]["osl"] for c in calls] + eff_isl = (total_isl - total_cached) // len(calls) + + print(f"{comp:<30} {len(calls):>4} " + f"{min(isls):>5} / {sum(isls)//len(isls):>5} / {max(isls):>5} / {eff_isl:>5} " + f"{min(osls):>5} / {sum(osls)//len(osls):>5} / {max(osls):>5}") + + print() + + # --- Prefix cache table --- + print("=" * 75) + print("Table 2: Prefix Cache Hit Rate (weighted by ISL)") + print("-" * 75) + print(f"{'Component':<35} {'Hop 2':>7} {'Hop 3':>7} {'Hop 4':>7} {'Hop 5':>7} {'Avg':>7}") + print("-" * 75) + + for comp in components: + total_isl, total_cached, by_hop = comp_cache_results[comp] + + hop_rates = {} + for hop in sorted(by_hop.keys()): + h = by_hop[hop] + hop_rates[hop] = 100 * h["cached"] / h["total_isl"] if h["total_isl"] > 0 else 0 + + avg_rate = 100 * total_cached / total_isl if total_isl > 0 else 0 + row = f"{comp:<35}" + for hop in [2, 3, 4, 5]: + row += f" {hop_rates.get(hop, 0):>6.2f}%" + row += f" {avg_rate:>6.2f}%" + print(row) + + print("-" * 75) + print(f"{'Overall':<35} {'':>7} {'':>7} {'':>7} {'':>7} {100*grand_cached/grand_isl:>6.2f}%") + + print() + print("=" * 75) + print("Table 3: Prefix Cache Hit Rate by Model Endpoint (weighted by ISL)") + print("-" * 75) + for model, stats in endpoint_stats.items(): + rate = 100 * stats["cached"] / stats["isl"] if stats["isl"] > 0 else 0 + print(f" {model:<30} {rate:>6.2f}% (ISL={stats['isl']:,}, Cached={stats['cached']:,})") + print(f" {'Overall':<30} {100*grand_cached/grand_isl:>6.2f}% (ISL={grand_isl:,}, Cached={grand_cached:,})") + + +if __name__ == "__main__": + main() diff --git a/e2e/scripts/db_manifest_intel_xpu.json.gz b/e2e/scripts/db_manifest_intel_xpu.json.gz new file mode 100644 index 0000000000..3a46be5dc0 Binary files /dev/null and b/e2e/scripts/db_manifest_intel_xpu.json.gz differ diff --git a/e2e/scripts/run_ingestion.sh b/e2e/scripts/run_ingestion.sh new file mode 100644 index 0000000000..73b500f554 --- /dev/null +++ b/e2e/scripts/run_ingestion.sh @@ -0,0 +1,62 @@ +#!/bin/bash +# ============================================================================= +# Setup: Build passages JSON and vector DB from downloaded HTML documents +# +# Run this once before any retrieval experiments. +# Assumes: doc_html/ is populated and data/frames_dataset.tsv exists. +# +# Usage (from repo root): +# bash scripts/run_ingestion.sh +# +# Output (paths configurable via INGESTION_PASSAGES_JSON / INGESTION_DB): +# ${INGESTION_PASSAGES_JSON} — passage chunks +# ${INGESTION_DB}.db — FAISS HNSW vector index +# ============================================================================= + +set -e + +CONFIG="${CONFIG:-config.sh}" +if [[ -f "${CONFIG}" ]]; then + source "${CONFIG}" +else + echo "WARNING: ${CONFIG} not found; using built-in defaults" >&2 +fi + +INGESTION_DEVICE="${INGESTION_DEVICE:-cpu}" +INGESTION_EMBEDDING_DEVICE="${INGESTION_EMBEDDING_DEVICE:-${INGESTION_DEVICE}}" +INGESTION_NUM_EMBEDDING_DEVICES="${INGESTION_NUM_EMBEDDING_DEVICES:-4}" +INGESTION_CHUNK_LEN="${INGESTION_CHUNK_LEN:-768}" +INGESTION_CHUNK_OVERLAP="${INGESTION_CHUNK_OVERLAP:-32}" +INGESTION_RETRIEVER_MODEL="${INGESTION_RETRIEVER_MODEL:-/data/model/e5-base-v2}" +INGESTION_DOC_DIR="${INGESTION_DOC_DIR:-doc_html}" +INGESTION_PASSAGES_JSON="${INGESTION_PASSAGES_JSON:-passages/doc_html_len${INGESTION_CHUNK_LEN}_ov${INGESTION_CHUNK_OVERLAP}_word.json}" +INGESTION_DB="${INGESTION_DB:-vector_html_hnsw_len${INGESTION_CHUNK_LEN}_ov${INGESTION_CHUNK_OVERLAP}_word}" + +echo "=== Step 1: Extract passages from HTML documents ===" +mkdir -p "$(dirname "${INGESTION_PASSAGES_JSON}")" + +python3 -u read_docs.py "${INGESTION_DOC_DIR}" "${INGESTION_DOC_DIR}_text" \ + --fixed-length "${INGESTION_CHUNK_LEN}" \ + --fixed-overlap "${INGESTION_CHUNK_OVERLAP}" \ + --text-boundary word \ + --json "${INGESTION_PASSAGES_JSON}" \ + |& tee setup_read_docs.log + +echo "" +echo "=== Step 2: Build FAISS HNSW vector index ===" + +python3 -u single_shot_retrieval.py \ + --ingest "${INGESTION_PASSAGES_JSON}" \ + --db "${INGESTION_DB}" \ + --retrieval_method vector \ + --vector_index_method hnsw \ + --device "${INGESTION_DEVICE}" \ + --embedding-device "${INGESTION_EMBEDDING_DEVICE}" \ + --retriever_model "${INGESTION_RETRIEVER_MODEL}" \ + --num_embedding_devices "${INGESTION_NUM_EMBEDDING_DEVICES}" \ + |& tee setup_build_db.log + +echo "" +echo "=== DB setup complete ===" +echo " Passages: ${INGESTION_PASSAGES_JSON}" +echo " Vector DB: ${INGESTION_DB}.db" diff --git a/e2e/scripts/run_multi_shot.sh b/e2e/scripts/run_multi_shot.sh new file mode 100644 index 0000000000..803070028e --- /dev/null +++ b/e2e/scripts/run_multi_shot.sh @@ -0,0 +1,143 @@ +#!/bin/bash +# ============================================================================= +# Multi-shot retrieval experiment +# +# Usage (from repo root): +# bash scripts/run_multi_shot.sh [N_QUERIES] [NUM_WORKERS] +# +# N_QUERIES, NUM_WORKERS: optional positional overrides for INFERENCE_N_QUERIES +# and INFERENCE_NUM_WORKERS in config.sh. +# Use 'all' for the full dataset (824 queries). +# +# Configuration: +# See config.template.sh. Override with config.sh, or one-off via env var, +# e.g.: INFERENCE_DEVICE=cpu bash scripts/run_multi_shot.sh 50 +# +# Prerequisites: +# - OPENROUTER_API_KEY environment variable set +# - scripts/run_ingestion.sh has been run (vector DB exists) +# +# For strict memory binding, invoke under numactl: +# numactl --membind=0 bash scripts/run_multi_shot.sh ... +# ============================================================================= + +set -e + +CONFIG="${CONFIG:-config.sh}" +if [[ -f "${CONFIG}" ]]; then + source "${CONFIG}" +else + echo "WARNING: ${CONFIG} not found; using built-in defaults" >&2 +fi + +if [ -z "$OPENROUTER_API_KEY" ]; then + echo "WARNING: OPENROUTER_API_KEY environment variable not set" + echo "Usage: OPENROUTER_API_KEY=\"sk-or-v1-YOUR_KEY_HERE\" bash $0" + #exit 1 +fi + +# Architecture: +# - Document grader: INFERENCE_MODEL via INFERENCE_LLM_URL +# - Sufficiency checker / query generator / answer generator: INFERENCE_QUERY_MODEL via INFERENCE_LLM_URL +# - Embeddings / reranking: device controlled by INFERENCE_EMBEDDING_DEVICE / INFERENCE_RERANKER_DEVICE + +INFERENCE_DEVICE="${INFERENCE_DEVICE:-cpu}" +INFERENCE_EMBEDDING_DEVICE="${INFERENCE_EMBEDDING_DEVICE:-${INFERENCE_DEVICE}}" +INFERENCE_RERANKER_DEVICE="${INFERENCE_RERANKER_DEVICE:-${INFERENCE_DEVICE}}" +INFERENCE_DB="${INFERENCE_DB:-vector_html_hnsw_len768_ov32_word}" +INFERENCE_RETRIEVER_MODEL="${INFERENCE_RETRIEVER_MODEL:-/data/model/e5-base-v2}" +INFERENCE_TOP_K_RETRIEVER="${INFERENCE_TOP_K_RETRIEVER:-15}" +INFERENCE_MAX_ITERATIONS="${INFERENCE_MAX_ITERATIONS:-5}" +INFERENCE_MAX_SUB_QUERIES="${INFERENCE_MAX_SUB_QUERIES:-3}" +INFERENCE_TEMPERATURE="${INFERENCE_TEMPERATURE:-1.0}" +INFERENCE_MAX_RETRIES="${INFERENCE_MAX_RETRIES:-5}" +INFERENCE_N_QUERIES="${INFERENCE_N_QUERIES:-5}" +INFERENCE_NUM_WORKERS="${INFERENCE_NUM_WORKERS:-1}" +INFERENCE_LLM_URL="${INFERENCE_LLM_URL:-http://127.0.0.1:8123/v1/chat/completions}" +INFERENCE_MODEL="${INFERENCE_MODEL:-/model/gpt-oss-20b-mxfp4}" +INFERENCE_QUERY_MODEL="${INFERENCE_QUERY_MODEL:-/model/gpt-oss-120b-mxfp4}" + +# Per-component endpoint splits. Empty -> inherit INFERENCE_LLM_URL / INFERENCE_MODEL. +INFERENCE_GRADER_URL="${INFERENCE_GRADER_URL:-}" +INFERENCE_GRADER_MODEL="${INFERENCE_GRADER_MODEL:-}" +INFERENCE_QUERY_URL="${INFERENCE_QUERY_URL:-}" +INFERENCE_SUFFICIENCY_URL="${INFERENCE_SUFFICIENCY_URL:-}" +INFERENCE_SUFFICIENCY_MODEL="${INFERENCE_SUFFICIENCY_MODEL:-}" +INFERENCE_JUDGE_URL="${INFERENCE_JUDGE_URL:-https://openrouter.ai/api/v1/chat/completions}" +INFERENCE_JUDGE_MODEL="${INFERENCE_JUDGE_MODEL:-openai/gpt-oss-20b}" + +# Positional args override config. +N_QUERIES="${1:-${INFERENCE_N_QUERIES}}" +NUM_WORKERS="${2:-${INFERENCE_NUM_WORKERS}}" + +if [[ "${N_QUERIES}" == "all" ]]; then + EVAL_FLAG="--eval" + TAG="full" +else + EVAL_FLAG="--eval ${N_QUERIES}" + TAG="n${N_QUERIES}" +fi + +OUTPUT_DIR="output_multi_shot_${TAG}_w${NUM_WORKERS}_$(date +%Y%m%d_%H%M%S)" +mkdir -p "${OUTPUT_DIR}" + +RESULT_JSON="${OUTPUT_DIR}/result_multi_shot_${TAG}.json" +LOG_FILE="${OUTPUT_DIR}/run.log" +SCORE_FILE="${OUTPUT_DIR}/score_multi_shot_${TAG}.txt" + +echo "=== Multi-shot retrieval ===" +echo " Model (grader): ${INFERENCE_MODEL}" +echo " Model (query gen): ${INFERENCE_QUERY_MODEL}" +echo " DB: ${INFERENCE_DB}" +echo " Workers: ${NUM_WORKERS}" +echo " Queries: ${N_QUERIES}" +echo " Device: ${INFERENCE_DEVICE} (embedding=${INFERENCE_EMBEDDING_DEVICE}, reranker=${INFERENCE_RERANKER_DEVICE})" +echo " Output dir: ${OUTPUT_DIR}" +echo "" + +python3 -u multi_shot_retrieval.py \ + --retrieval_method vector \ + --db "${INFERENCE_DB}" \ + ${EVAL_FLAG} \ + --max-iterations "${INFERENCE_MAX_ITERATIONS}" \ + --max-sub-queries "${INFERENCE_MAX_SUB_QUERIES}" \ + --device "${INFERENCE_DEVICE}" \ + --embedding-device "${INFERENCE_EMBEDDING_DEVICE}" \ + --reranker-device "${INFERENCE_RERANKER_DEVICE}" \ + --retrieval_strategy fixed_k \ + --retriever_model "${INFERENCE_RETRIEVER_MODEL}" \ + --top_k_retriever "${INFERENCE_TOP_K_RETRIEVER}" \ + --generate-answer \ + --num-workers "${NUM_WORKERS}" \ + --temperature "${INFERENCE_TEMPERATURE}" \ + --max-retries "${INFERENCE_MAX_RETRIES}" \ + --output-dir "${OUTPUT_DIR}" \ + --llm_model "${INFERENCE_MODEL}" \ + --query_model "${INFERENCE_QUERY_MODEL}" \ + --llm_service_url "${INFERENCE_LLM_URL}" \ + ${INFERENCE_GRADER_URL:+--grader-service-url "${INFERENCE_GRADER_URL}"} \ + ${INFERENCE_GRADER_MODEL:+--grader-model "${INFERENCE_GRADER_MODEL}"} \ + ${INFERENCE_QUERY_URL:+--query-service-url "${INFERENCE_QUERY_URL}"} \ + ${INFERENCE_SUFFICIENCY_URL:+--sufficiency-service-url "${INFERENCE_SUFFICIENCY_URL}"} \ + ${INFERENCE_SUFFICIENCY_MODEL:+--sufficiency-model "${INFERENCE_SUFFICIENCY_MODEL}"} \ + 2>&1 | tee "${LOG_FILE}" + +if [[ -f "${OUTPUT_DIR}/result_multi_shot.json" ]]; then + mv "${OUTPUT_DIR}/result_multi_shot.json" "${RESULT_JSON}" + echo "Saved results to ${RESULT_JSON}" +fi + +echo "" +echo "=== Scoring with LLM judge ===" +python3 -u evaluate.py "${RESULT_JSON}" \ + --dataset "${DATASET:-data/frames_dataset.tsv}" \ + --judge-url "${INFERENCE_JUDGE_URL}" \ + --judge-model "${INFERENCE_JUDGE_MODEL}" \ + --batch-size 4 + +echo "" +echo "=== Done ===" +echo " Output dir: ${OUTPUT_DIR}" +echo " Results: ${RESULT_JSON}" +echo " Score: ${SCORE_FILE}" +echo " Run log: ${LOG_FILE}" diff --git a/e2e/scripts/run_oracle.sh b/e2e/scripts/run_oracle.sh new file mode 100644 index 0000000000..7539a6ac15 --- /dev/null +++ b/e2e/scripts/run_oracle.sh @@ -0,0 +1,90 @@ +#!/bin/bash +# ============================================================================= +# Oracle E2E evaluation — feeds ground-truth Wikipedia articles to the LLM. +# Bypasses retrieval entirely; gives an upper bound on answer accuracy. +# +# Usage (from repo root): +# bash scripts/run_oracle.sh [N_QUERIES] +# +# N_QUERIES: optional positional override for INFERENCE_N_QUERIES. +# Use 'all' for the full dataset. +# +# Configuration: see config.template.sh (INFERENCE_ORACLE_* knobs). +# +# Prerequisites: +# - LLM server running on the configured INFERENCE_LLM_URL +# - wiki_articles/ directory exists (run download_docs.py first) +# ============================================================================= + +set -e + +CONFIG="${CONFIG:-config.sh}" +if [[ -f "${CONFIG}" ]]; then + source "${CONFIG}" +else + echo "WARNING: ${CONFIG} not found; using built-in defaults" >&2 +fi + +INFERENCE_LLM_URL="${INFERENCE_LLM_URL:-http://127.0.0.1:8123/v1/chat/completions}" +INFERENCE_MODEL="${INFERENCE_MODEL:-/model/gpt-oss-20b-mxfp4}" +INFERENCE_N_QUERIES="${INFERENCE_N_QUERIES:-5}" +INFERENCE_ORACLE_BATCH_SIZE="${INFERENCE_ORACLE_BATCH_SIZE:-4}" +INFERENCE_ORACLE_TIMEOUT="${INFERENCE_ORACLE_TIMEOUT:-2400}" +INFERENCE_ORACLE_DATASET="${INFERENCE_ORACLE_DATASET:-data/frames_dataset.tsv}" +INFERENCE_ORACLE_WIKI_DIR="${INFERENCE_ORACLE_WIKI_DIR:-wiki_articles}" +INFERENCE_JUDGE_URL="${INFERENCE_JUDGE_URL:-${INFERENCE_LLM_URL}}" +INFERENCE_JUDGE_MODEL="${INFERENCE_JUDGE_MODEL:-${INFERENCE_MODEL}}" + +THINKING_FLAG="" +if [[ "${INFERENCE_ORACLE_ENABLE_THINKING}" == "1" ]]; then + THINKING_FLAG="--enable-thinking" +fi + +N_QUERIES="${1:-${INFERENCE_N_QUERIES}}" +if [[ "${N_QUERIES}" == "all" ]]; then + TAG="full" + MAX_QUERIES_FLAG="" +else + TAG="n${N_QUERIES}" + MAX_QUERIES_FLAG="--max-queries ${N_QUERIES}" +fi + +CHECKPOINT="oracle_checkpoint_${TAG}.pkl" +SCORE_FILE="score_oracle_${TAG}.txt" +LOG_FILE="oracle_${TAG}.log" + +echo "=== Oracle E2E evaluation ===" +echo " Model: ${INFERENCE_MODEL}" +echo " Dataset: ${INFERENCE_ORACLE_DATASET}" +echo " Wiki dir: ${INFERENCE_ORACLE_WIKI_DIR}" +echo " Queries: ${N_QUERIES}" +echo " Checkpoint: ${CHECKPOINT}" +echo "" + +echo "=== Step 1: Generating answers with oracle documents ===" +python3 -u oracle_single_shot.py \ + --dataset "${INFERENCE_ORACLE_DATASET}" \ + --wiki-articles-dir "${INFERENCE_ORACLE_WIKI_DIR}" \ + --checkpoint-file "${CHECKPOINT}" \ + --service-url "${INFERENCE_LLM_URL}" \ + --model-name "${INFERENCE_MODEL}" \ + --batch-size "${INFERENCE_ORACLE_BATCH_SIZE}" \ + --timeout "${INFERENCE_ORACLE_TIMEOUT}" \ + ${THINKING_FLAG} \ + ${MAX_QUERIES_FLAG} \ + |& tee "${LOG_FILE}" + +echo "" +echo "=== Step 2: Scoring with LLM judge ===" +python3 -u evaluate.py "${CHECKPOINT}" \ + --dataset "${INFERENCE_ORACLE_DATASET}" \ + --judge-url "${INFERENCE_JUDGE_URL}" \ + --judge-model "${INFERENCE_JUDGE_MODEL}" \ + --batch-size 4 \ + |& tee "${SCORE_FILE}" + +echo "" +echo "=== Done ===" +echo " Oracle log: ${LOG_FILE}" +echo " Checkpoint: ${CHECKPOINT}" +echo " Score: ${SCORE_FILE}" diff --git a/e2e/scripts/run_single_shot.sh b/e2e/scripts/run_single_shot.sh new file mode 100644 index 0000000000..bb7e20ed37 --- /dev/null +++ b/e2e/scripts/run_single_shot.sh @@ -0,0 +1,94 @@ +#!/bin/bash +# ============================================================================= +# Single-shot retrieval experiment +# +# Usage (from repo root): +# bash scripts/run_single_shot.sh [N_QUERIES] +# +# N_QUERIES: optional positional override for INFERENCE_N_QUERIES. +# Use 'all' for the full dataset. +# +# Configuration: see config.template.sh. +# +# Prerequisites: +# - LLM server running (or OPENROUTER_API_KEY for remote judge) +# - scripts/run_ingestion.sh has been run (vector DB exists) +# ============================================================================= + +set -e + +CONFIG="${CONFIG:-config.sh}" +if [[ -f "${CONFIG}" ]]; then + source "${CONFIG}" +else + echo "WARNING: ${CONFIG} not found; using built-in defaults" >&2 +fi + +INFERENCE_DEVICE="${INFERENCE_DEVICE:-cpu}" +INFERENCE_EMBEDDING_DEVICE="${INFERENCE_EMBEDDING_DEVICE:-${INFERENCE_DEVICE}}" +INFERENCE_RERANKER_DEVICE="${INFERENCE_RERANKER_DEVICE:-${INFERENCE_DEVICE}}" +INFERENCE_DB="${INFERENCE_DB:-vector_html_hnsw_len768_ov32_word}" +INFERENCE_RETRIEVER_MODEL="${INFERENCE_RETRIEVER_MODEL:-/data/model/e5-base-v2}" +INFERENCE_TOP_K_RETRIEVER="${INFERENCE_TOP_K_RETRIEVER:-15}" +INFERENCE_N_QUERIES="${INFERENCE_N_QUERIES:-5}" +INFERENCE_LLM_URL="${INFERENCE_LLM_URL:-http://127.0.0.1:8123/v1/chat/completions}" +INFERENCE_MODEL="${INFERENCE_MODEL:-/model/gpt-oss-20b-mxfp4}" +INFERENCE_JUDGE_URL="${INFERENCE_JUDGE_URL:-https://openrouter.ai/api/v1/chat/completions}" +INFERENCE_JUDGE_MODEL="${INFERENCE_JUDGE_MODEL:-openai/gpt-oss-20b}" + +N_QUERIES="${1:-${INFERENCE_N_QUERIES}}" + +if [[ "${N_QUERIES}" == "all" ]]; then + EVAL_FLAG="--eval" + TAG="full" +else + EVAL_FLAG="--eval ${N_QUERIES}" + TAG="n${N_QUERIES}" +fi + +OUTPUT_DIR="output_single_shot_${TAG}_$(date +%Y%m%d_%H%M%S)" +mkdir -p "${OUTPUT_DIR}" +RESULT_JSON="${OUTPUT_DIR}/result_single_shot_${TAG}.json" +LOG_FILE="${OUTPUT_DIR}/run.log" + +echo "=== Single-shot retrieval ===" +echo " Model: ${INFERENCE_MODEL}" +echo " DB: ${INFERENCE_DB}" +echo " Queries: ${N_QUERIES}" +echo " Device: ${INFERENCE_DEVICE} (embedding=${INFERENCE_EMBEDDING_DEVICE}, reranker=${INFERENCE_RERANKER_DEVICE})" +echo " Output dir: ${OUTPUT_DIR}" +echo "" + +python3 -u single_shot_retrieval.py \ + --retrieval_method vector \ + --db "${INFERENCE_DB}" \ + ${EVAL_FLAG} \ + --device "${INFERENCE_DEVICE}" \ + --embedding-device "${INFERENCE_EMBEDDING_DEVICE}" \ + --reranker-device "${INFERENCE_RERANKER_DEVICE}" \ + --retriever_model "${INFERENCE_RETRIEVER_MODEL}" \ + --top_k_retriever "${INFERENCE_TOP_K_RETRIEVER}" \ + --generate-answer \ + --llm_model "${INFERENCE_MODEL}" \ + --llm_service_url "${INFERENCE_LLM_URL}" \ + 2>&1 | tee "${LOG_FILE}" + +if [[ -f result_single_shot.json ]]; then + mv result_single_shot.json "${RESULT_JSON}" +fi + +if [[ -f "${RESULT_JSON}" ]]; then + echo "" + echo "=== Scoring with LLM judge ===" + python3 -u evaluate.py "${RESULT_JSON}" \ + --dataset "${DATASET:-data/frames_dataset.tsv}" \ + --judge-url "${INFERENCE_JUDGE_URL}" \ + --judge-model "${INFERENCE_JUDGE_MODEL}" \ + --batch-size 4 +fi + +echo "" +echo "=== Done ===" +echo " Output dir: ${OUTPUT_DIR}" +echo " Results: ${RESULT_JSON}" +echo " Run log: ${LOG_FILE}" diff --git a/e2e/scripts/start_vllm_server.sh b/e2e/scripts/start_vllm_server.sh new file mode 100644 index 0000000000..6a7871a555 --- /dev/null +++ b/e2e/scripts/start_vllm_server.sh @@ -0,0 +1,15 @@ +python3 -m vllm.entrypoints.openai.api_server \ + --model /model/gpt-oss-20b-mxfp4 \ + --dtype bfloat16 \ + --enforce-eager \ + --host 0.0.0.0 \ + --trust-remote-code \ + --gpu-memory-util=0.95 \ + --enable-prefix-caching \ + --max-num-batched-tokens=8192 \ + --disable-log-requests \ + --max-model-len=131072 \ + --block-size 64 \ + --port 8123 \ + -tp 4 \ + --async_scheduling \ No newline at end of file diff --git a/e2e/scripts/verify_db_manifest.sh b/e2e/scripts/verify_db_manifest.sh new file mode 100644 index 0000000000..495f61478a --- /dev/null +++ b/e2e/scripts/verify_db_manifest.sh @@ -0,0 +1,48 @@ +#!/bin/bash +# ============================================================================= +# Verify this system's vector DB against a reference manifest. +# +# Usage (from repo root): +# bash scripts/verify_db_manifest.sh MANIFEST [COSINE_THRESHOLD] [TOP_K_DEPTH] +# +# MANIFEST: path to the reference manifest JSON (required). +# COSINE_THRESHOLD: minimum sample-embedding cosine similarity (default: 0.9999). +# TOP_K_DEPTH: probe-query top-K rank match depth (default: 3). +# +# Configuration: see config.template.sh (uses INFERENCE_DB and +# INFERENCE_RETRIEVER_MODEL). +# ============================================================================= + +set -e + +if [[ -z "$1" ]]; then + echo "ERROR: manifest path required" >&2 + echo "Usage: $0 MANIFEST [COSINE_THRESHOLD] [TOP_K_DEPTH]" >&2 + exit 1 +fi + +CONFIG="${CONFIG:-config.sh}" +if [[ -f "${CONFIG}" ]]; then + source "${CONFIG}" +else + echo "WARNING: ${CONFIG} not found; using built-in defaults" >&2 +fi + +INFERENCE_DB="${INFERENCE_DB:-vector_html_hnsw_len768_ov32_word}" + +MANIFEST="$1" +COSINE_THRESHOLD="${2:-0.9999}" +TOP_K_DEPTH="${3:-3}" + +echo "=== Verifying DB against manifest ===" +echo " DB: ${INFERENCE_DB}" +echo " Manifest: ${MANIFEST}" +echo " Cosine threshold: ${COSINE_THRESHOLD}" +echo " Top-K depth: ${TOP_K_DEPTH}" +echo "" + +python3 -u db_manifest.py verify \ + --db "${INFERENCE_DB}" \ + --manifest "${MANIFEST}" \ + --cosine-threshold "${COSINE_THRESHOLD}" \ + --top-k-depth "${TOP_K_DEPTH}" diff --git a/e2e/scripts/write_db_manifest.sh b/e2e/scripts/write_db_manifest.sh new file mode 100644 index 0000000000..01b6580f8d --- /dev/null +++ b/e2e/scripts/write_db_manifest.sh @@ -0,0 +1,38 @@ +#!/bin/bash +# ============================================================================= +# Generate a reference DB manifest from this system's vector DB. +# +# Usage (from repo root): +# bash scripts/write_db_manifest.sh [OUTPUT] +# +# OUTPUT: optional path for the manifest JSON. Use .json.gz to compress. +# default: db_manifest_$(hostname -s).json.gz +# +# Configuration: see config.template.sh (uses INFERENCE_DB and +# INFERENCE_RETRIEVER_MODEL). +# ============================================================================= + +set -e + +CONFIG="${CONFIG:-config.sh}" +if [[ -f "${CONFIG}" ]]; then + source "${CONFIG}" +else + echo "WARNING: ${CONFIG} not found; using built-in defaults" >&2 +fi + +INFERENCE_DB="${INFERENCE_DB:-vector_html_hnsw_len768_ov32_word}" +INFERENCE_RETRIEVER_MODEL="${INFERENCE_RETRIEVER_MODEL:-/data/model/e5-base-v2}" + +OUTPUT="${1:-db_manifest_$(hostname -s).json.gz}" + +echo "=== Writing DB manifest ===" +echo " DB: ${INFERENCE_DB}" +echo " Retriever: ${INFERENCE_RETRIEVER_MODEL}" +echo " Output: ${OUTPUT}" +echo "" + +python3 -u db_manifest.py write \ + --db "${INFERENCE_DB}" \ + --retriever_model "${INFERENCE_RETRIEVER_MODEL}" \ + --output "${OUTPUT}" diff --git a/e2e/setup.sh b/e2e/setup.sh new file mode 100644 index 0000000000..52cc4b3094 --- /dev/null +++ b/e2e/setup.sh @@ -0,0 +1,6 @@ +#!/bin/bash + +pip install -r requirements.txt +apt-get update +apt-get install -y --no-install-recommends \ + wkhtmltopdf \ No newline at end of file diff --git a/e2e/single_shot_retrieval.py b/e2e/single_shot_retrieval.py new file mode 100644 index 0000000000..a52ee92fe4 --- /dev/null +++ b/e2e/single_shot_retrieval.py @@ -0,0 +1,342 @@ +import argparse +import json +import time +import os +import requests +from pathlib import Path +from functools import lru_cache +from retrieve import VectorDB, BM25DB +from evaluation import evaluate_retrieval_query, run_evaluation +from utils import set_deterministic_seeds, setup_llm_config +from params import add_all_args + +# Taken below from frames: https://huggingface.co/datasets/google/frames-benchmark +DEFAULT_QUERY = "Who won the French Open Mens Singles tournament the year that New York City FC won their first MLS Cup title?" +MAX_PASSAGE_PREVIEW = 4096 +FULL_DOC_MAX_CHARS = 39000 + + +def _get_metadata(doc): + if hasattr(doc, "metadata"): + return doc.metadata or {} + if isinstance(doc, dict): + return doc + return {} + + +def _serialize_params(args): + params = {} + for key, value in vars(args).items(): + if isinstance(value, (str, int, float, bool)) or value is None: + params[key] = value + else: + params[key] = str(value) + return params + + +@lru_cache(maxsize=256) +def _read_text(path: str) -> str: + path_obj = Path(path) + if not path_obj.exists(): + return "" + return path_obj.read_text(encoding="utf-8", errors="ignore") + + +def _load_document_text(metadata, base_dir=None, default_base_dir="doc_html", max_chars=FULL_DOC_MAX_CHARS): + target_dir = base_dir or default_base_dir + base_filename = metadata.get("base_filename") + if not base_filename: + return "", None + + base_path = Path(target_dir) + candidates = [ + base_path / f"{base_filename}.txt", + base_path / f"{base_filename}.html", + base_path / f"{base_filename}.htm" + ] + + for candidate in candidates: + candidate_path = str(candidate) + content = _read_text(candidate_path) + if content: + return content[:max_chars], candidate_path + return "", None + + +def _convert_results_to_entries(results, limit=5, full_doc=False, base_dir=None, default_base_dir="doc_html"): + entries = [] + seen_ids = set() + count = 0 + for doc in results: + metadata = _get_metadata(doc) + url = metadata.get("original_url") or metadata.get("source") + doc_id = url or metadata.get("base_filename") + if doc_id and doc_id in seen_ids: + continue + if full_doc: + content, source_path = _load_document_text( + metadata, + base_dir=base_dir, + default_base_dir=default_base_dir + ) + if not content: + content = getattr(doc, "page_content", metadata.get("content", ""))[:MAX_PASSAGE_PREVIEW] + else: + content = getattr(doc, "page_content", metadata.get("content", ""))[:MAX_PASSAGE_PREVIEW] + source_path = None + entry = {"url": url, "content": content} + if source_path: + entry["source_path"] = source_path + entries.append(entry) + if doc_id: + seen_ids.add(doc_id) + count += 1 + if limit and limit > 0 and count >= limit: + break + return entries + + +def _extract_unique_urls(results): + urls = [] + seen = set() + for doc in results: + metadata = _get_metadata(doc) + url = metadata.get("original_url") or metadata.get("source") + if url and url not in seen: + urls.append(url) + seen.add(url) + return urls + + +def _generate_llm_answer(query, doc_entries, llm_config): + context_parts = [] + for idx, doc in enumerate(doc_entries, 1): + source = doc.get("url") or "Unknown source" + snippet = doc.get("content", "").strip() + context_parts.append(f"[{idx}] Source: {source}\n{snippet}") + evidence_block = "\n\n".join(context_parts) if context_parts else "No supporting documents were retrieved." + user_prompt = ( + "Answer the question using only the provided evidence." + " Respond with a single word or short phrase, or 'Unknown' if the evidence is insufficient.\n\n" + f"Question:\n{query}\n\nEvidence:\n{evidence_block}" + ) + max_tokens = llm_config["max_tokens"] + if isinstance(max_tokens, int): + max_tokens = min(max_tokens, 256) + else: + max_tokens = 256 + payload = { + "model": llm_config["model_name"], + "messages": [ + { + "role": "system", + "content": "You are a concise retrieval QA assistant who trusts the supplied context." + }, + {"role": "user", "content": user_prompt} + ], + "temperature": 0.0, + "max_tokens": max_tokens + } + response = requests.post(llm_config["service_url"], json=payload, timeout=60) + response.raise_for_status() + data = response.json() + message = data["choices"][0]["message"] + content = message.get("content") or "" + # Thinking models may return output in reasoning_content instead of content + if not content.strip(): + content = message.get("reasoning_content") or "" + return content.strip() if content.strip() else "Unknown" + + + +if __name__ == "__main__": + args = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter) + + # Add all parameters from centralized definitions + # This includes: Common, General, BM25, Vector, Strategy, and Reranking parameters + add_all_args(args) + + # Special handling for --eval argument (needs custom type) + # Override the default eval argument with custom type + for action in args._actions: + if '--eval' in action.option_strings: + action.type = lambda x: int(x) if x.isdigit() else True + action.const = True + break + + args = args.parse_args() # Set deterministic seeds for reproducible results + set_deterministic_seeds(args.seed) + llm_config = setup_llm_config(args) if args.generate_answer else None + doc_base_dir = args.base_doc_dir + + # Initialize the appropriate database class + if args.retrieval_method == "bm25": + db_class = BM25DB + else: + db_class = VectorDB + + # Set default database path based on database class if not provided + if args.database is None: + args.database = db_class.get_default_db_name() + + # Normalize database path: ensure .db extension for file operations + db_file_path = args.database if args.database.endswith('.db') else f"{args.database}.db" + db_base_name = args.database.replace('.db', '') if args.database.endswith('.db') else args.database + + # Create database instance (pass base name without .db) + rag_db = db_class(retriever_model=args.retriever_model, reranker_model=args.reranker_model, device=args.device, + k1=args.bm25_k1, b=args.bm25_b, method=args.bm25_method, database=db_base_name, + delta=args.bm25_delta, backend=args.bm25_backend, stopwords=args.bm25_stopwords, + show_progress=args.bm25_show_progress, stemmer=args.bm25_stemmer, + vector_index_method=args.vector_index_method, ivf_nprobe=args.ivf_nprobe, + load_embeddings=args.load_embeddings, num_embedding_devices=args.num_embedding_devices, + hierarchical=args.hierarchical, + embedding_device=args.embedding_device, + reranker_device=args.reranker_device, + benchmark=args.benchmark) + + if os.path.exists(db_file_path): + # Load existing database + print(f"Loading existing database from {db_file_path}") + rag_db.from_serialized(db_file_path) + else: + if not args.ingest: + raise ValueError("Either --database (existing) or --ingest (to create new) must be provided") + + # Ingest from file or folder + tic = time.time() + rag_db.ingest_from_path(args.ingest, num_threads=args.threads) + + # Get number of passages for timing calculation + num_passages = len(rag_db._doc_list) # This should be available after ingestion + toc = time.time() + ingestion_speed = num_passages/(toc-tic) + print(f"Ingestion of {num_passages} passages took {toc - tic:.2f} seconds. {ingestion_speed:.2f} docs/sec") + + # Save the database (unless --no-save is specified) + if not args.no_save: + print(f"Saving database to {db_file_path}") + rag_db.serialize(db_file_path) + else: + print("Skipping database save (--no-save specified)") + + # Run evaluation or single query lookup + if args.eval: + max_queries = args.eval if isinstance(args.eval, int) and not isinstance(args.eval, bool) and args.eval > 0 else None + + # Build strategy_params with correct parameter names for filter function + strategy_params = {"max_results": args.max_results} + if args.retrieval_strategy == "top_p": + strategy_params["p"] = args.top_p + elif args.retrieval_strategy == "relative": + strategy_params["ratio"] = args.relative_ratio + + answer_records = [] + def handle_result(prompt, retrieved_docs, metrics): + urls = _extract_unique_urls(retrieved_docs) + answer_text = None + if args.generate_answer: + doc_entries = _convert_results_to_entries( + retrieved_docs, + limit=5, + full_doc=args.full_doc_context, + base_dir=doc_base_dir + ) + answer_text = _generate_llm_answer(prompt, doc_entries, llm_config) + print(f"LLM Answer: {answer_text}") + if args.save_results: + record = { + "prompt": prompt, + "retrieved_urls": urls + } + if answer_text is not None: + record["llm_answer"] = answer_text + answer_records.append(record) + + metrics = run_evaluation( + rag_db, + args.dataset, + top_k_retriever=args.top_k_retriever, + top_k_reranking=args.top_k_reranking, + max_queries=max_queries, + no_rerank=args.no_rerank, + retrieval_strategy=args.retrieval_strategy, + detailed_analysis=True, + difficulty=args.difficulty, + result_handler=handle_result if (args.generate_answer or args.save_results) else None, + **strategy_params + ) + + # Save results for optimization + results_data = { + "accuracy": metrics.get('legacy_score', 0.0), # Backward compatibility + "metrics": metrics + } + + with open("results.json", "w") as f: + json.dump(results_data, f, indent=2) + + if args.save_results: + with open("result_single_shot.json", "w") as f: + json.dump({ + "params": _serialize_params(args), + "results": answer_records + }, f, indent=2) + exit(0) # Exit after evaluation + else: + # Single query lookup - reuse evaluation code for consistency + + strategy_params = {} + if args.retrieval_strategy == "top_p": + strategy_params["p"] = args.top_p + elif args.retrieval_strategy == "relative": + strategy_params["ratio"] = args.relative_ratio + + # Time the retrieval + tic = time.time() + need_results = args.generate_answer or args.save_results + eval_output = evaluate_retrieval_query( + rag_db, + args.query, + expected_urls=[], + top_k_retriever=args.top_k_retriever, + top_k_reranking=args.top_k_reranking, + verbose=False, + no_rerank=getattr(args, 'no_rerank', False), + retrieval_strategy=args.retrieval_strategy, + print_results=True, + return_results=need_results, + max_results=args.max_results, + **strategy_params + ) + if need_results: + _, retrieved_docs = eval_output + else: + retrieved_docs = [] + + answer_value = None + if args.generate_answer: + doc_entries = _convert_results_to_entries( + retrieved_docs, + limit=5, + full_doc=args.full_doc_context, + base_dir=doc_base_dir + ) + answer_value = _generate_llm_answer(args.query, doc_entries, llm_config) + print(f"LLM Answer: {answer_value}") + + if args.save_results: + record = { + "prompt": args.query, + "retrieved_urls": _extract_unique_urls(retrieved_docs) + } + if answer_value is not None: + record["llm_answer"] = answer_value + with open("result_single_shot.json", "w") as f: + json.dump({ + "params": _serialize_params(args), + "results": [record] + }, f, indent=2) + toc = time.time() + + print(f"\nLookup took {toc - tic:.3f} seconds") diff --git a/e2e/text_splitter.py b/e2e/text_splitter.py new file mode 100644 index 0000000000..29a71539d9 --- /dev/null +++ b/e2e/text_splitter.py @@ -0,0 +1,244 @@ +#!/usr/bin/env python3 +""" +Text Splitter Module + +Shared text splitting functionality for both PDF and HTML pipelines. +Provides intelligent text chunking with sentence-aware boundaries and overlap. +""" + +import re +from typing import List, Optional + + +def clean_text(text: str) -> str: + """Clean and normalize text.""" + # Normalize whitespace + text = re.sub(r'\s+', ' ', text.strip()) + + # Remove excessive newlines but preserve paragraph structure + text = re.sub(r'\n\s*\n\s*\n+', '\n\n', text) + + return text + + +def find_sentence_boundary(text: str, start: int, end: int, search_window: int = 100) -> int: + """ + Find the best sentence boundary within the search window. + + Args: + text: The text to search in + start: Start position of the passage + end: Desired end position + search_window: Number of characters to look back for sentence boundary + + Returns: + Best boundary position + """ + if end >= len(text): + return len(text) + + # Look for sentence endings within the search window + search_start = max(start, end - search_window) + sentence_endings = ['.', '!', '?', '\n'] + + best_break = end + for i in range(end - 1, search_start - 1, -1): + if text[i] in sentence_endings: + # Check if it's followed by whitespace and uppercase letter (proper sentence end) + if i + 1 < len(text) and text[i + 1].isspace(): + # Look for the next non-whitespace character + j = i + 1 + while j < len(text) and text[j].isspace(): + j += 1 + if j < len(text) and (text[j].isupper() or text[j].isdigit()): + best_break = i + 1 + break + + return best_break + + +def split_into_passages(text: str, max_length: int = 512, overlap: int = 50) -> List[str]: + """ + Split text into passages suitable for retrieval systems like ColBERT. + + Args: + text: Input text to split + max_length: Maximum length of each passage in characters + overlap: Number of characters to overlap between passages + + Returns: + List of passage texts + """ + # Clean up the text + text = clean_text(text) + + if len(text) <= max_length: + return [text] if text else [] + + passages = [] + start = 0 + + while start < len(text): + end = start + max_length + + # If we're not at the end of the text, try to break at a sentence boundary + if end < len(text): + end = find_sentence_boundary(text, start, end) + + passage = text[start:end].strip() + if passage: + passages.append(passage) + + # Move start position with overlap + start = end - overlap + if start >= len(text): + break + + return passages + + +def split_into_fixed_passages(text: str, fixed_length: int = 256, overlap: int = 32) -> List[str]: + """ + Split text into fixed-length passages with exact character counts. + Useful for consistent passage lengths across datasets. + + Args: + text: Input text to split + fixed_length: Exact length of each passage in characters + overlap: Number of characters to overlap between passages + + Returns: + List of passage texts with fixed lengths + """ + # Clean up the text + text = clean_text(text) + + if len(text) <= fixed_length: + return [text] if text else [] + + passages = [] + start = 0 + + while start < len(text): + end = min(start + fixed_length, len(text)) + passage = text[start:end].strip() + + if passage: + passages.append(passage) + + # Move start position with overlap + start = start + fixed_length - overlap + if start >= len(text): + break + + return passages + + +def create_passage_metadata(filename: str, passage_index: int, original_url: Optional[str] = None) -> dict: + """ + Create standardized metadata for a passage. + + Args: + filename: Source filename (PDF or HTML) + passage_index: Index of this passage within the document + original_url: Original URL if available + + Returns: + Dictionary containing passage metadata + """ + # Extract base filename without extension + base_filename = filename + if '.' in filename: + base_filename = '.'.join(filename.split('.')[:-1]) + + metadata = { + 'index': passage_index, + 'base_filename': base_filename + } + + if original_url: + metadata['original_url'] = original_url + + return metadata + + +def estimate_passage_count(text: str, max_length: int = 512, overlap: int = 50) -> int: + """ + Estimate the number of passages that will be created from text. + Useful for progress tracking without actually splitting. + """ + if len(text) <= max_length: + return 1 if text.strip() else 0 + + # Rough estimate based on overlap + effective_length = max_length - overlap + return max(1, (len(text) - overlap) // effective_length) + + +def split_into_hierarchical_passages( + text: str, + parent_length: int = 2048, + parent_overlap: int = 32, + child_length: int = 512, + child_overlap: int = 100 +) -> List[dict]: + """ + Split text into hierarchical parent-child passages for retrieval. + + This creates a two-level hierarchy where: + - Parent chunks (large): Provide complete context for LLM + - Child chunks (small): Enable precise retrieval matching + + Retrieval strategy: Search using child embeddings, return parent text to LLM. + + Args: + text: Input text to split + parent_length: Length of parent chunks (default: 2048 chars) + parent_overlap: Overlap between parent chunks (default: 32 chars) + child_length: Length of child chunks (default: 512 chars) + child_overlap: Overlap between child chunks (default: 100 chars) + + Returns: + List of dicts with keys: + - 'child_passage': Small chunk text (for embedding/retrieval) + - 'parent_passage': Large parent chunk text (for LLM context) + - 'parent_id': ID of parent chunk + - 'child_index': Index of this child within parent + """ + # Clean up the text + text = clean_text(text) + + if len(text) <= parent_length: + # Single parent case - still create child chunks + parent_text = text + children = split_into_passages(parent_text, child_length, child_overlap) + + results = [] + for child_idx, child_text in enumerate(children): + results.append({ + 'child_passage': child_text, + 'parent_passage': parent_text, + 'parent_id': 0, + 'child_index': child_idx + }) + return results + + # Split into parent chunks first + parent_chunks = split_into_passages(text, parent_length, parent_overlap) + + # For each parent, split into children + all_results = [] + for parent_id, parent_text in enumerate(parent_chunks): + # Split parent into children + children = split_into_passages(parent_text, child_length, child_overlap) + + # Create hierarchical entries + for child_idx, child_text in enumerate(children): + all_results.append({ + 'child_passage': child_text, + 'parent_passage': parent_text, + 'parent_id': parent_id, + 'child_index': child_idx + }) + + return all_results \ No newline at end of file diff --git a/e2e/utils.py b/e2e/utils.py new file mode 100644 index 0000000000..b4cc4130dc --- /dev/null +++ b/e2e/utils.py @@ -0,0 +1,584 @@ +#!/usr/bin/env python3 +""" +General utilities for document processing, deterministic operations, and other common functions. +""" + +import json +import os +import requests +import torch +from pathlib import Path +from typing import Dict, Optional, Union, Any + + + +def load_url_mapping(directory: str) -> Dict[str, str]: + """Load URL mapping from url_mapping.json in specified directory.""" + mapping_path = Path(directory) / "url_mapping.json" + if mapping_path.exists(): + with open(mapping_path, 'r', encoding='utf-8') as f: + return json.load(f) + return {} + + +def get_base_filename(filename: str) -> str: + """Extract base filename without extension.""" + if '.' in filename: + return '.'.join(filename.split('.')[:-1]) + return filename + + +def save_url_mapping(directory: str, url_mapping: Dict[str, str]) -> None: + """Save URL mapping to url_mapping.json in specified directory.""" + mapping_path = Path(directory) / "url_mapping.json" + with open(mapping_path, 'w', encoding='utf-8') as f: + json.dump(url_mapping, f, indent=2, ensure_ascii=False) + + +def set_deterministic_seeds(seed: int = 42) -> None: + """Set seeds for reproducible results across all components. + + Covers: Python random, NumPy, PyTorch (CPU + all CUDA/XPU devices). + Note: LLM responses are stochastic and cannot be made deterministic via seed. + """ + import random + import numpy as np + import torch + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + + +def filter_dataset_by_difficulty(df, difficulty: int = 0): + """ + Filter dataset by minimum number of answer links (difficulty level). + + Args: + df: pandas DataFrame with dataset + difficulty: Minimum number of answer links required (0 = no filtering) + + Returns: + Filtered DataFrame with queries having >= difficulty answer links + """ + if difficulty <= 0: + return df + + # Count answer links for each row + link_counts = df.apply( + lambda row: sum(1 for col in df.columns + if col.startswith('wikipedia_link_') and row.notna()[col]), + axis=1 + ) + + filtered_df = df[link_counts >= difficulty].reset_index(drop=True) + print(f"Filtered dataset by difficulty >= {difficulty}: {len(filtered_df)} queries remaining (from {len(df)} total)") + + return filtered_df + + +def _parse_cpulist(s: str) -> list: + """Parse a Linux cpulist string ("0-3,7,9-11") into a list of ints.""" + result = [] + for part in s.strip().split(","): + if not part: + continue + if "-" in part: + lo, hi = part.split("-", 1) + result.extend(range(int(lo), int(hi) + 1)) + else: + result.append(int(part)) + return result + + +def _physical_cores_for_node(node: int) -> list: + """Return one logical CPU per physical core on the given NUMA node. + + Reads /sys/devices/system/node/nodeN/cpulist for the node's CPUs, then + filters HT siblings via /sys/devices/system/cpu/cpuN/topology/thread_siblings_list. + """ + try: + with open(f"/sys/devices/system/node/node{node}/cpulist") as f: + node_cpus = set(_parse_cpulist(f.read())) + except OSError: + return [] + + seen_cores = set() + primary = [] + for cpu in sorted(node_cpus): + try: + with open(f"/sys/devices/system/cpu/cpu{cpu}/topology/thread_siblings_list") as f: + siblings = _parse_cpulist(f.read()) + except OSError: + primary.append(cpu) + continue + core_key = min(siblings) + if core_key in seen_cores: + continue + seen_cores.add(core_key) + primary.append(cpu) + return primary + + +def set_mempolicy_membind(node: int) -> None: + """Bind this process's memory allocations to a NUMA node. + + Equivalent to `numactl --membind=N` but applied per-process from inside + Python. Linux x86_64-only; raises OSError on failure. + + glibc doesn't export set_mempolicy() as a regular symbol, so we issue the + syscall directly. Try libnuma first (which does export it), fall back to + raw syscall. + """ + import ctypes + MPOL_BIND = 2 + nodemask = ctypes.c_ulong(1 << node) + maxnode = ctypes.c_ulong(64) + + try: + libnuma = ctypes.CDLL("libnuma.so.1", use_errno=True) + rc = libnuma.set_mempolicy(MPOL_BIND, ctypes.byref(nodemask), maxnode) + except (OSError, AttributeError): + # set_mempolicy is syscall 238 on x86_64; 237 on aarch64 (rare in this codebase). + SYS_SET_MEMPOLICY_X86_64 = 238 + libc = ctypes.CDLL("libc.so.6", use_errno=True) + rc = libc.syscall(SYS_SET_MEMPOLICY_X86_64, MPOL_BIND, + ctypes.byref(nodemask), maxnode) + + if rc != 0: + err = ctypes.get_errno() + raise OSError(err, f"set_mempolicy(MPOL_BIND, node={node}) failed") + + +def slice_cores_for_workers(numa_nodes: list, omp_per_worker: int = 0) -> list: + """Compute disjoint per-worker (node, cpu_set) plans for parallel workers. + + Args: + numa_nodes: One node ID per worker, e.g. [0, 0, 1, 1] for 4 workers. + omp_per_worker: Cores to assign each worker. 0 = even split of each + node's cores among the workers assigned to that node. + + Returns: + List of (node, [cpu_ids]) tuples, one per worker. Workers on the same + node get disjoint core slices. + """ + import collections + workers_per_node = collections.Counter(numa_nodes) + cores_taken = collections.defaultdict(int) + plan = [] + + for worker_idx, node in enumerate(numa_nodes): + node_cores = _physical_cores_for_node(node) + if not node_cores: + raise RuntimeError( + f"NUMA node {node} cpulist not readable for worker {worker_idx}" + ) + if omp_per_worker > 0: + slice_len = omp_per_worker + else: + slice_len = len(node_cores) // workers_per_node[node] + if slice_len == 0: + raise RuntimeError( + f"Node {node} has {len(node_cores)} cores, can't split among " + f"{workers_per_node[node]} workers" + ) + offset = cores_taken[node] + cpu_set = node_cores[offset:offset + slice_len] + if len(cpu_set) < slice_len: + raise RuntimeError( + f"Worker {worker_idx} on node {node}: needs {slice_len} cores, " + f"only {len(cpu_set)} left ({len(node_cores)} total - {offset} taken)." + ) + plan.append((node, cpu_set)) + cores_taken[node] += slice_len + + return plan + + +def pin_worker_to_node(node: int, cpu_set: list) -> None: + """Pin THIS process to a specific NUMA node + CPU set. + + Call as the very first thing in a child process, before importing torch + or allocating large objects. Sets CPU affinity, memory binding, and + OMP_NUM_THREADS. + """ + if not cpu_set: + print(f" [worker] no cpu_set for node {node}; skipping pinning") + return + + if hasattr(os, "sched_setaffinity"): + try: + os.sched_setaffinity(0, set(cpu_set)) + except OSError as e: + print(f" [worker] sched_setaffinity failed: {e}") + + try: + set_mempolicy_membind(node) + except OSError as e: + print(f" [worker] set_mempolicy failed: {e}") + + if "OMP_NUM_THREADS" not in os.environ: + os.environ["OMP_NUM_THREADS"] = str(len(cpu_set)) + + print(f" [worker] node={node} cores={cpu_set[0]}..{cpu_set[-1]} ({len(cpu_set)}) OMP_NUM_THREADS={os.environ['OMP_NUM_THREADS']}") + + +def apply_numa_pinning() -> None: + """Pin CPU affinity to one NUMA node's physical cores. + + Honors CPU_DISABLE_NUMA / CPU_NUMA_NODE / CPU_NUMA_CORES. + Memory locality relies on Linux first-touch (good enough for our small + Python working set). For strict membind, invoke under `numactl --membind=N`. + """ + if os.environ.get("CPU_DISABLE_NUMA") == "1": + print(" NUMA pinning disabled via CPU_DISABLE_NUMA=1") + return + + if not hasattr(os, "sched_setaffinity"): + print(" NUMA pinning unavailable (os.sched_setaffinity missing)") + return + + cores_env = os.environ.get("CPU_NUMA_CORES") + if cores_env: + try: + cores = _parse_cpulist(cores_env) + except ValueError: + print(f" Invalid CPU_NUMA_CORES={cores_env!r}; skipping pinning") + return + else: + node = int(os.environ.get("CPU_NUMA_NODE", "0")) + cores = _physical_cores_for_node(node) + if not cores: + print(f" NUMA node {node} cpulist not readable; skipping pinning") + return + + try: + os.sched_setaffinity(0, set(cores)) + except OSError as e: + print(f" sched_setaffinity failed: {e}; skipping pinning") + return + + print(f" Pinned CPU affinity to {len(cores)} cores: {cores[0]}..{cores[-1]}") + + +def apply_cpu_threading_env() -> None: + """Pin CPU affinity to a NUMA node and configure OpenMP env vars. + + Only call this when a model is actually being placed on CPU. + Never overwrites a user-set env var. + """ + apply_numa_pinning() + + if "OMP_NUM_THREADS" not in os.environ: + override = os.environ.get("CPU_OMP_NUM_THREADS") + if override: + n_threads = override + else: + try: + # sched_getaffinity reflects the pinning we just applied. + n_threads = str(len(os.sched_getaffinity(0))) + except (AttributeError, OSError): + n_threads = str(os.cpu_count() or 1) + os.environ["OMP_NUM_THREADS"] = n_threads + print(f" Set OMP_NUM_THREADS={n_threads}") + + vendor = detect_cpu_vendor() + if vendor == "intel" and "KMP_AFFINITY" not in os.environ: + os.environ["KMP_AFFINITY"] = "granularity=fine,compact,1,0" + print(" Set KMP_AFFINITY (Intel OpenMP)") + elif vendor != "intel": + print(f" Skipping KMP_AFFINITY (CPU vendor={vendor})") + + +def detect_cpu_vendor() -> str: + """Detect host CPU vendor from /proc/cpuinfo. + + Returns "intel", "amd", or "unknown". Used to gate vendor-specific tuning + """ + try: + with open("/proc/cpuinfo", "r") as f: + for line in f: + if line.startswith("vendor_id"): + vendor = line.split(":", 1)[1].strip() + if vendor == "GenuineIntel": + return "intel" + if vendor == "AuthenticAMD": + return "amd" + return "unknown" + except OSError: + pass + return "unknown" + + +_DEVICE_ALLOCATORS: Dict[str, "DeviceAllocator"] = {} + + +class DeviceAllocator: + """Per-process GPU index allocator. + + Picks GPU indices that are empty (not running other workloads) and + unused within this process. Errors if not enough are available. + + Per-component override via env var (e.g. INFERENCE_RERANKER_GPU_DEVICES, + INFERENCE_EMBEDDING_GPU_DEVICES). Indices are 0-based and post any vendor + visibility mask (CUDA_VISIBLE_DEVICES, HIP_VISIBLE_DEVICES, + ROCR_VISIBLE_DEVICES, ZE_AFFINITY_MASK, etc.) — i.e. they match + torch.{cuda,xpu}.device_count() output. + """ + + EMPTY_FREE_RATIO = 0.95 # device counts as 'empty' if >=95% memory free + + def __init__(self, device_type: str): + self.device_type = device_type + self._taken: set = set() + self._all_indices: list = self._enumerate() + + def _enumerate(self) -> list: + if torch is None: + return [] + if self.device_type == "cuda": + return list(range(torch.cuda.device_count())) + if self.device_type == "xpu" and hasattr(torch, "xpu"): + return list(range(torch.xpu.device_count())) + return [] + + def _is_empty(self, idx: int) -> bool: + try: + if self.device_type == "cuda": + free, total = torch.cuda.mem_get_info(idx) + elif self.device_type == "xpu" and hasattr(torch.xpu, "mem_get_info"): + free, total = torch.xpu.mem_get_info(idx) + else: + return True # can't probe, assume usable + return total > 0 and (free / total) >= self.EMPTY_FREE_RATIO + except Exception: + return True + + def _parse_override(self, override_env: str) -> list: + raw = os.environ.get(override_env) + if not raw: + return [] + try: + indices = [int(x) for x in raw.split(",") if x.strip()] + except ValueError: + raise RuntimeError( + f"Invalid {override_env}={raw!r}; expected comma-separated ints" + ) + invalid = [i for i in indices if i not in self._all_indices] + if invalid: + raise RuntimeError( + f"{override_env}={raw!r}: indices {invalid} not visible " + f"(available: {self._all_indices})" + ) + return indices + + def allocate(self, count: int = 1, name: str = "", override_env: str = "") -> list: + if override_env: + requested = self._parse_override(override_env) + if requested: + avail = [i for i in requested if i not in self._taken] + source = f"{override_env}={','.join(map(str, requested))}" + else: + avail = [i for i in self._all_indices if i not in self._taken and self._is_empty(i)] + source = "auto" + else: + avail = [i for i in self._all_indices if i not in self._taken and self._is_empty(i)] + source = "auto" + + if len(avail) < count: + override_hint = f" or set {override_env}=" if override_env else "" + raise RuntimeError( + f"DeviceAllocator: need {count} empty {self.device_type} device(s) for {name!r}, " + f"got {len(avail)} via {source} (taken={sorted(self._taken)}, " + f"all={self._all_indices}). Free a GPU{override_hint}." + ) + chosen = avail[:count] + self._taken.update(chosen) + label = name or self.device_type + print(f" Allocated {self.device_type}:{chosen} for {label} (via {source})") + return chosen + + +def get_device_allocator(device_type: str) -> DeviceAllocator: + """Return the process-wide allocator for the given GPU device type.""" + if device_type not in _DEVICE_ALLOCATORS: + _DEVICE_ALLOCATORS[device_type] = DeviceAllocator(device_type) + return _DEVICE_ALLOCATORS[device_type] + + +def resolve_gpu_device(device: str, name: str = "", override_env: str = "") -> str: + """Map a bare device type ('cuda' / 'xpu') to a specific 'cuda:N' string. + + Returns `device` unchanged for cpu/hpu/auto/already-indexed strings. + Errors if no empty GPU is available (use override_env to override). + """ + if device in ("cuda", "xpu"): + idx = get_device_allocator(device).allocate(count=1, name=name, override_env=override_env)[0] + return f"{device}:{idx}" + return device + + +def detect_device() -> str: + """Auto-detect the best available device.""" + if torch is None: + return "cpu" + + if torch.cuda.is_available(): + if getattr(torch.version, "hip", None): + print(f"Using AMD ROCm GPU (torch.version.hip={torch.version.hip})") + else: + print("Using NVIDIA CUDA GPU") + return "cuda" + + if hasattr(torch, "xpu") and torch.xpu.is_available(): + print("Using Intel XPU GPU") + return "xpu" + + try: + import habana_frameworks.torch.core as htcore # noqa: F401 + if torch.hpu.is_available(): + os.environ["PT_HPU_LAZY_MODE"] = "1" + print("Using Habana HPU") + return "hpu" + except ImportError: + pass + + print("Using CPU") + return "cpu" + + +def get_model_info_from_service(service_url: str) -> Optional[Dict]: + """Get model information from LLM service.""" + try: + # Try OpenAI-compatible API first + models_response = requests.get(f"{service_url.rstrip('/v1/chat/completions').rstrip('/v1')}/v1/models", timeout=10) + if models_response.status_code == 200: + models_data = models_response.json() + if "data" in models_data and len(models_data["data"]) > 0: + return models_data["data"][0] + + # Try alternative endpoints + base_url = service_url.rstrip('/v1/chat/completions').rstrip('/v1') + for endpoint in ["/models", "/info", "/v1/model"]: + try: + response = requests.get(f"{base_url}{endpoint}", timeout=5) + if response.status_code == 200: + return response.json() + except: + continue + + except Exception as e: + print(f"Warning: Could not auto-detect model from {service_url}: {e}") + + return None + + +def get_model_name_from_service(service_url: str) -> str: + """Auto-detect model name from LLM service.""" + model_info = get_model_info_from_service(service_url) + + if model_info: + # Try different possible fields for model name + for field in ["id", "model", "name", "model_name"]: + if field in model_info: + return model_info[field] + + # Default fallback + return "/mnt/weka/data/pytorch/llama3.3/Meta-Llama-3.3-70B-Instruct/" + + +def get_max_tokens_from_service(service_url: str) -> int: + """Auto-detect max tokens from LLM service.""" + model_info = get_model_info_from_service(service_url) + + if model_info: + # Try different possible fields for max tokens + for field in ["max_tokens", "max_length", "context_length", "max_context_length"]: + if field in model_info and isinstance(model_info[field], int): + return model_info[field] + + # Default fallback based on common models + return 10240 + + +def resolve_config_value(value: Union[str, int], auto_func, *args) -> Union[str, int]: + """Resolve configuration value that might be 'auto'.""" + if value == "auto": + return auto_func(*args) + return value + + +def get_device_config(): + """Get comprehensive device configuration.""" + config = { + "device_type": detect_device(), + "device_count": 1, + "device_memory": None + } + + if torch is None: + return config + + if config["device_type"] == "hpu": + config["device_count"] = torch.hpu.device_count() + + elif config["device_type"] == "cuda": + config["device_count"] = torch.cuda.device_count() + if torch.cuda.is_available(): + config["device_memory"] = torch.cuda.get_device_properties(0).total_memory + + elif config["device_type"] == "xpu": + config["device_count"] = torch.xpu.device_count() + + return config + + +def setup_llm_config(args): + """Setup LLM configuration with auto-detection and OpenRouter support.""" + # Resolve device + device = resolve_config_value(args.device, detect_device) + + # Resolve model name + model_name = resolve_config_value( + args.llm_model, + get_model_name_from_service, + args.llm_service_url + ) + + # Resolve max tokens + if isinstance(args.max_tokens, str): + max_tokens = resolve_config_value( + args.max_tokens, + get_max_tokens_from_service, + args.llm_service_url + ) + else: + max_tokens = args.max_tokens + + # Per-component URL/model resolution. + # Each component falls back to --llm_service_url / --llm_model when not set; + # query and sufficiency further fall back to --query_model when set. + base_url = args.llm_service_url + query_model_name = getattr(args, 'query_model', None) or model_name + sufficiency_model_name = ( + getattr(args, 'sufficiency_model', None) or query_model_name + ) + + grader_service_url = getattr(args, 'grader_service_url', None) or base_url + grader_model_name = getattr(args, 'grader_model', None) or model_name + query_service_url = getattr(args, 'query_service_url', None) or base_url + sufficiency_service_url = getattr(args, 'sufficiency_service_url', None) or base_url + + return { + "service_url": base_url, + "model_name": model_name, + "query_model_name": query_model_name, + "max_tokens": max_tokens, + "device": device, + "grader_service_url": grader_service_url, + "grader_model_name": grader_model_name, + "query_service_url": query_service_url, + "sufficiency_service_url": sufficiency_service_url, + "sufficiency_model_name": sufficiency_model_name, + } diff --git a/language/deepseek-r1/eval_accuracy.py b/language/deepseek-r1/eval_accuracy.py index bf537e9d3a..9c103fdcba 100644 --- a/language/deepseek-r1/eval_accuracy.py +++ b/language/deepseek-r1/eval_accuracy.py @@ -773,7 +773,7 @@ def print_evaluation_results(df_evaluated: pd.DataFrame, 'tokens_per_sample': mean_output_len, 'num-samples': len(df_evaluated), } - + print("\nResults\n") print(results) diff --git a/language/llama3.1-8b/download_cnndm.py b/language/llama3.1-8b/download_cnndm.py index d8694be720..90c9ad8d7a 100644 --- a/language/llama3.1-8b/download_cnndm.py +++ b/language/llama3.1-8b/download_cnndm.py @@ -100,8 +100,8 @@ def preprocess_function(sample, padding="max_length"): # create list of samples inputs = [] - #print(f"Num samples: {len(sample[text_column])}") - #for i in range(0, len(sample[text_column])): + # print(f"Num samples: {len(sample[text_column])}") + # for i in range(0, len(sample[text_column])): x = dict() x["instruction"] = instruction_template x["input"] = sample[text_column] @@ -109,7 +109,7 @@ def preprocess_function(sample, padding="max_length"): instruction_template[instruction].format_map(x) ) x["output"] = sample[summary_column] - #inputs.append(x) + # inputs.append(x) model_inputs = dict() model_inputs["text"] = x diff --git a/loadgen/issue_query_controller.cc b/loadgen/issue_query_controller.cc index c1abea9d14..4c5ca66f0c 100644 --- a/loadgen/issue_query_controller.cc +++ b/loadgen/issue_query_controller.cc @@ -459,8 +459,8 @@ void IssueQueryController::IssueQueriesInternal(size_t query_stride, #if USE_NEW_LOGGING_FORMAT std::stringstream ss; ss << "IssueQueryThread " << thread_idx - << " Ending early: Too many outstanding queries." << " issued " - << queries_issued_total << " outstanding " + << " Ending early: Too many outstanding queries." + << " issued " << queries_issued_total << " outstanding " << queries_outstanding; MLPERF_LOG_ERROR(detail, "error_runtime", ss.str()); #else @@ -499,8 +499,8 @@ void IssueQueryController::IssueQueriesInternal(size_t query_stride, #if USE_NEW_LOGGING_FORMAT std::stringstream ss; ss << "IssueQueryThread " << thread_idx - << " Ending early: Max query count reached." << " query_count " - << queries_issued; + << " Ending early: Max query count reached." + << " query_count " << queries_issued; MLPERF_LOG_ERROR(detail, "error_runtime", ss.str()); #else detail.Error("IssueQueryThread ", std::to_string(thread_idx), @@ -519,8 +519,8 @@ void IssueQueryController::IssueQueriesInternal(size_t query_stride, #if USE_NEW_LOGGING_FORMAT std::stringstream ss; ss << "IssueQueryThread " << thread_idx - << " Ending early: Max test duration reached." << " duration_ns " - << duration.count(); + << " Ending early: Max test duration reached." + << " duration_ns " << duration.count(); MLPERF_LOG_ERROR(detail, "error_runtime", ss.str()); #else detail.Error("IssueQueryThread ", std::to_string(thread_idx), diff --git a/loadgen/logging.cc b/loadgen/logging.cc index 807c1954a8..d7e83e54b9 100644 --- a/loadgen/logging.cc +++ b/loadgen/logging.cc @@ -812,7 +812,8 @@ void Logger::CollectTlsLoggerStats(TlsLogger* tls_logger) { if (max_entry_vector_size > kTlsLogReservedEntryCount) { #if USE_NEW_LOGGING_FORMAT std::stringstream msg; - msg << "Logging allocation detected:" << " tid: " << tls_logger->Tid() + msg << "Logging allocation detected:" + << " tid: " << tls_logger->Tid() << " reserved_entries: " << kTlsLogReservedEntryCount << " max_entries: " << max_entry_vector_size; MLPERF_LOG_WARNING((*this), "warning_generic_message", msg.str());