Dream-RSI for pi: assess projects, apply verified code improvements, and evolve exploration policies within bounded runs.
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Updated
Nov 4, 2026 - TypeScript
Dream-RSI for pi: assess projects, apply verified code improvements, and evolve exploration policies within bounded runs.
Independent, unofficial Dream-RSI-inspired Python SDK. Alpha: model-agnostic adapters, recorded-tree replay and policy search. Not affiliated with Google.
Dream-RSI recursive self-improvement as a DeepSeek Harness plugin: discovery history becomes a replay simulator; candidate exploration policies are dream-evaluated off-policy and redeployed under a no-regression guarantee.
Self-improvement research testbed for LLMs: Dream-RSI-style policy evolution (arXiv:2609.14858) + STaR-style weight upgrades, with a Rust rootless sandbox. Apache-2.0.
Self-evolving long-term memory plugin for opencode: sessions become a decision tree, retrieval never regresses, and a strategy library auto-selects proven architecture choices. Independent TypeScript implementation of Dream-RSI (arXiv:2609.14858).
The first trainable, 100% domain-agnostic PyTorch Cortical MCP & Multi-Agent Engine. Wakes n (default=4) orthogonal specialists from a 1-sentence prompt, gates working memory at 0 routing tokens (-60% context), verifies designs in Python/SciPy before answering, and trains its own weights on the fly via Dream-RSI.
Dream-RSI 100-Layer Centum Formal Verification Engine for Zero-Defect AI Coding Agents
Source-linked AI study guides on agent evaluation, prompt optimization, experience memory and recursive self-improvement, with framework comparisons and learning exercises.
Evidence-driven visual iteration for agents: owner gates, calibrated critics, max-zoom fidelity, discovery history and domain packs.
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