FE-666: Basic Monte Carlo Simulation by kube · Pull Request #8709 · hashintel/hash

kube · 2026-05-10T13:49:18Z

🌟 What is the purpose of this PR?

Reworks Petrinaut simulation internals and adds the first Monte Carlo experiment workflow. The branch separates Core/React/UI boundaries, moves frame access behind reader/binary-frame APIs, introduces seeded Monte Carlo runs with streaming token-count distributions, and exposes experiments in the Simulate tab plus CLI tooling.

flowchart LR
  UI[Simulate > Experiments] --> Context[ExperimentsContext]
  Context --> Runtime[Experiment runtime]
  Runtime <--> Worker[Monte Carlo worker protocol]
  Worker --> Simulator[Monte Carlo simulator]
  Simulator --> Metrics[Streaming token-count distributions]
  Metrics --> Context
  Context --> Timeline[uPlot experiment timeline]

🔗 Related links

Changeset — Petrinaut patch release note

🚫 Blocked by

None.

🔍 What does this change?

Refactors simulation Core boundaries, public APIs, frame readers, runtime modules, and worker-facing types.
Moves engine frames toward binary ArrayBuffer storage and keeps layout details behind SimulationFrameReader.
Makes simulation inputs JSON-serializable, including initial markings.
Simplifies deterministic simulation/playback lifecycle and reorganizes the Simulation Timeline modules.
Adds a seeded Monte Carlo simulator with per-run buffers, active-only token-count histograms, runtime orchestration, and worker tests.
Adds Simulate > Experiments UI for creating, running, cancelling, removing, and viewing Monte Carlo experiments.
Adds a Petrinaut Monte Carlo CLI and removes the temporary local stress-test file.

Pre-Merge Checklist 🚀

🚢 Has this modified a publishable library?

This PR:

modifies an npm-publishable library and I have added a changeset file(s)

📜 Does this require a change to the docs?

The changes in this PR:

require changes to docs which are made as part of this PR

🕸️ Does this require a change to the Turbo Graph?

The changes in this PR:

I am unsure / need advice

⚠️ Known issues

The Monte Carlo metric storage is still a first implementation; windowed/binary metric persistence is left for follow-up work.

🐾 Next steps

Narrow the Simulation Engine around binary frame storage, metric storage, and stable worker protocols.
Improve Monte Carlo metric aggregation/storage before scaling the UI to very large experiments.

🛡 What tests cover this?

yarn workspace @hashintel/petrinaut lint:tsc
yarn workspace @hashintel/petrinaut lint:eslint
yarn workspace @hashintel/petrinaut test:unit src/core/simulation/monte-carlo/runtime/experiment.test.ts src/react/experiments/provider.test.tsx src/core/simulation/monte-carlo/monte-carlo-simulator.test.ts src/core/simulation/authoring/scenario/compile-scenario.test.ts --run

❓ How to test this?

Open Petrinaut and go to Simulate > Experiments.
Create an experiment with a scenario, parameters, max time, and run count.
Click Run and confirm the dialog shows progress, closes on success, and opens the new experiment.
Confirm the token-count distribution timeline streams p10/median/mean/p90 data per place.
Cancel/remove an experiment and confirm the provider state updates correctly.

vercel · 2026-05-10T13:49:23Z

The latest updates on your projects. Learn more about Vercel for GitHub.

Project	Deployment	Actions	Updated (UTC)
hash	Ready	Preview, Comment	May 15, 2026 11:49pm
petrinaut	Ready	Preview, Comment	May 15, 2026 11:49pm

1 Skipped Deployment

Project	Deployment	Actions	Updated (UTC)
hashdotdesign-tokens	Ignored	Preview	May 15, 2026 11:49pm

codecov · 2026-05-10T13:53:54Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 6.20%. Comparing base (7b67b01) to head (d3127af).
⚠️ Report is 4 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main   #8709       +/-   ##
==========================================
- Coverage   62.31%   6.20%   -56.12%     
==========================================
  Files        1354     506      -848     
  Lines      137313   14817   -122496     
  Branches     5793    2994     -2799     
==========================================
- Hits        85568     919    -84649     
+ Misses      50838   13865    -36973     
+ Partials      907      33      -874

Flag	Coverage Δ
apps.hash-ai-worker-py	`?`
apps.hash-ai-worker-ts	`1.41% <ø> (ø)`
apps.hash-api	`0.00% <ø> (ø)`
backend-integration-tests	`?`
blockprotocol.type-system	`?`
deer	`?`
error-stack	`?`
local.claude-hooks	`?`
local.harpc-client	`51.24% <ø> (ø)`
local.hash-backend-utils	`2.81% <ø> (ø)`
local.hash-graph-sdk	`9.63% <ø> (ø)`
local.hash-isomorphic-utils	`0.00% <ø> (ø)`
local.hash-subgraph	`?`
rust.antsi	`?`
rust.deer	`?`
rust.error-stack	`?`
rust.harpc-codec	`?`
rust.harpc-net	`?`
rust.harpc-tower	`?`
rust.harpc-types	`?`
rust.harpc-wire-protocol	`?`
rust.hash-codec	`?`
rust.hash-graph-api	`?`
rust.hash-graph-authorization	`?`
rust.hash-graph-postgres-store	`?`
rust.hash-graph-store	`?`
rust.hash-graph-temporal-versioning	`?`
rust.hash-graph-types	`?`
rust.hash-graph-validation	`?`
rust.hashql-ast	`?`
rust.hashql-compiletest	`?`
rust.hashql-core	`?`
rust.hashql-diagnostics	`?`
rust.hashql-eval	`?`
rust.hashql-hir	`?`
rust.hashql-mir	`?`
rust.hashql-syntax-jexpr	`?`
rust.sarif	`?`
sarif	`?`
tests.hash-backend-integration	`?`
unit-tests	`?`

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codspeed-hq · 2026-05-10T14:02:08Z

Merging this PR will not alter performance

✅ 80 untouched benchmarks

_{Comparing cf/fe-666-stochastic-simulation-experiments-v1 (a880232) with main (b922dd2)¹}

No successful run was found on main (8452cc0) during the generation of this report, so b922dd2 was used instead as the comparison base. There might be some changes unrelated to this pull request in this report. ↩

github-actions · 2026-05-10T15:06:19Z

Benchmark results

@rust/hash-graph-benches – Integrations

policy_resolution_large

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2002	$$26.5 \mathrm{ms} \pm 228 \mathrm{μs}\left({\color{gray}1.97 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.52 \mathrm{ms} \pm 19.2 \mathrm{μs}\left({\color{gray}2.99 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$12.3 \mathrm{ms} \pm 75.2 \mathrm{μs}\left({\color{gray}-1.471 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$43.3 \mathrm{ms} \pm 282 \mathrm{μs}\left({\color{gray}-0.211 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$15.1 \mathrm{ms} \pm 115 \mathrm{μs}\left({\color{gray}3.13 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$23.2 \mathrm{ms} \pm 139 \mathrm{μs}\left({\color{gray}-4.023 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$26.9 \mathrm{ms} \pm 161 \mathrm{μs}\left({\color{gray}1.67 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.87 \mathrm{ms} \pm 20.8 \mathrm{μs}\left({\color{gray}3.70 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$13.2 \mathrm{ms} \pm 87.9 \mathrm{μs}\left({\color{gray}-0.309 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.68 \mathrm{ms} \pm 22.4 \mathrm{μs}\left({\color{gray}-2.123 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.98 \mathrm{ms} \pm 14.4 \mathrm{μs}\left({\color{gray}-1.841 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$3.36 \mathrm{ms} \pm 23.8 \mathrm{μs}\left({\color{gray}0.307 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$5.05 \mathrm{ms} \pm 27.6 \mathrm{μs}\left({\color{gray}-0.597 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.53 \mathrm{ms} \pm 15.7 \mathrm{μs}\left({\color{gray}-0.118 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$4.10 \mathrm{ms} \pm 23.0 \mathrm{μs}\left({\color{gray}0.100 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.35 \mathrm{ms} \pm 26.2 \mathrm{μs}\left({\color{gray}-1.078 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.40 \mathrm{ms} \pm 13.8 \mathrm{μs}\left({\color{gray}-2.555 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$4.03 \mathrm{ms} \pm 25.2 \mathrm{μs}\left({\color{gray}-0.717 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.63 \mathrm{ms} \pm 18.4 \mathrm{μs}\left({\color{gray}0.495 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.55 \mathrm{ms} \pm 18.0 \mathrm{μs}\left({\color{gray}0.559 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$2.58 \mathrm{ms} \pm 11.2 \mathrm{μs}\left({\color{gray}-0.240 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.86 \mathrm{ms} \pm 12.9 \mathrm{μs}\left({\color{gray}0.053 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.61 \mathrm{ms} \pm 10.1 \mathrm{μs}\left({\color{gray}-2.382 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.81 \mathrm{ms} \pm 10.1 \mathrm{μs}\left({\color{gray}-1.171 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$3.01 \mathrm{ms} \pm 14.6 \mathrm{μs}\left({\color{gray}0.163 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.74 \mathrm{ms} \pm 15.2 \mathrm{μs}\left({\color{gray}-0.598 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$2.96 \mathrm{ms} \pm 11.1 \mathrm{μs}\left({\color{gray}0.719 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.36 \mathrm{ms} \pm 22.5 \mathrm{μs}\left({\color{gray}-1.467 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.94 \mathrm{ms} \pm 14.7 \mathrm{μs}\left({\color{gray}-1.906 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$3.26 \mathrm{ms} \pm 16.5 \mathrm{μs}\left({\color{gray}-0.501 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.29 \mathrm{ms} \pm 18.1 \mathrm{μs}\left({\color{gray}-0.852 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.96 \mathrm{ms} \pm 11.8 \mathrm{μs}\left({\color{gray}-0.353 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.28 \mathrm{ms} \pm 17.6 \mathrm{μs}\left({\color{gray}-0.252 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$54.6 \mathrm{ms} \pm 349 \mathrm{μs}\left({\color{gray}0.491 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$46.1 \mathrm{ms} \pm 184 \mathrm{μs}\left({\color{gray}-0.314 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$51.5 \mathrm{ms} \pm 340 \mathrm{μs}\left({\color{gray}-0.440 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$45.6 \mathrm{ms} \pm 363 \mathrm{μs}\left({\color{gray}2.40 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$62.3 \mathrm{ms} \pm 313 \mathrm{μs}\left({\color{gray}-0.984 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$61.0 \mathrm{ms} \pm 334 \mathrm{μs}\left({\color{gray}-0.528 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$55.7 \mathrm{ms} \pm 311 \mathrm{μs}\left({\color{gray}-0.498 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$102 \mathrm{ms} \pm 388 \mathrm{μs}\left({\color{gray}-1.590 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$61.9 \mathrm{ms} \pm 4.26 \mathrm{ms}\left({\color{red}31.7 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$290 \mathrm{ms} \pm 763 \mathrm{μs}\left({\color{gray}-0.950 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$19.4 \mathrm{ms} \pm 108 \mathrm{μs}\left({\color{gray}-1.094 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$20.2 \mathrm{ms} \pm 103 \mathrm{μs}\left({\color{gray}0.398 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$20.3 \mathrm{ms} \pm 116 \mathrm{μs}\left({\color{gray}0.679 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$19.2 \mathrm{ms} \pm 63.1 \mathrm{μs}\left({\color{gray}-0.156 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$25.8 \mathrm{ms} \pm 173 \mathrm{μs}\left({\color{gray}3.97 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$19.2 \mathrm{ms} \pm 93.1 \mathrm{μs}\left({\color{gray}-0.220 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$19.2 \mathrm{ms} \pm 96.8 \mathrm{μs}\left({\color{gray}-2.065 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$19.7 \mathrm{ms} \pm 115 \mathrm{μs}\left({\color{gray}0.994 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$20.0 \mathrm{ms} \pm 81.4 \mathrm{μs}\left({\color{gray}-1.055 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$26.8 \mathrm{ms} \pm 200 \mathrm{μs}\left({\color{gray}-0.515 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity

Function	Value	Mean	Flame graphs
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/block/v/1`	$$35.5 \mathrm{ms} \pm 213 \mathrm{μs}\left({\color{gray}-0.274 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/book/v/1`	$$35.8 \mathrm{ms} \pm 282 \mathrm{μs}\left({\color{gray}1.11 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/building/v/1`	$$34.8 \mathrm{ms} \pm 263 \mathrm{μs}\left({\color{gray}-1.419 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/organization/v/1`	$$36.4 \mathrm{ms} \pm 368 \mathrm{μs}\left({\color{red}8.15 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/page/v/2`	$$35.4 \mathrm{ms} \pm 374 \mathrm{μs}\left({\color{gray}1.96 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/person/v/1`	$$34.9 \mathrm{ms} \pm 280 \mathrm{μs}\left({\color{gray}3.44 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/playlist/v/1`	$$34.8 \mathrm{ms} \pm 276 \mathrm{μs}\left({\color{gray}2.87 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/song/v/1`	$$34.9 \mathrm{ms} \pm 338 \mathrm{μs}\left({\color{gray}0.195 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1`	$$34.6 \mathrm{ms} \pm 354 \mathrm{μs}\left({\color{gray}2.00 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: `bf5a9ef5-dc3b-43cf-a291-6210c0321eba`	$$8.57 \mathrm{ms} \pm 56.3 \mathrm{μs}\left({\color{gray}1.63 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$93.5 \mathrm{ms} \pm 551 \mathrm{μs}\left({\color{gray}2.03 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$148 \mathrm{ms} \pm 598 \mathrm{μs}\left({\color{gray}1.71 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$100 \mathrm{ms} \pm 625 \mathrm{μs}\left({\color{gray}2.48 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$111 \mathrm{ms} \pm 512 \mathrm{μs}\left({\color{gray}1.31 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$118 \mathrm{ms} \pm 552 \mathrm{μs}\left({\color{gray}1.84 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$127 \mathrm{ms} \pm 537 \mathrm{μs}\left({\color{gray}1.71 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$103 \mathrm{ms} \pm 414 \mathrm{μs}\left({\color{gray}0.367 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$133 \mathrm{ms} \pm 448 \mathrm{μs}\left({\color{gray}1.33 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$111 \mathrm{ms} \pm 665 \mathrm{μs}\left({\color{gray}1.67 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$119 \mathrm{ms} \pm 539 \mathrm{μs}\left({\color{gray}0.575 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$122 \mathrm{ms} \pm 679 \mathrm{μs}\left({\color{gray}0.956 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$121 \mathrm{ms} \pm 443 \mathrm{μs}\left({\color{gray}0.350 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$189 \mathrm{ms} \pm 533 \mathrm{μs}\left({\color{red}21.5 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$169 \mathrm{ms} \pm 2.41 \mathrm{ms}\left({\color{red}13.0 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$40.3 \mathrm{ms} \pm 178 \mathrm{μs}\left({\color{gray}0.122 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$557 \mathrm{ms} \pm 927 \mathrm{μs}\left({\color{gray}2.37 \mathrm{\%}}\right) $$	Flame Graph

+    const count = marking?.count ?? 0;
+    const dimensions = getPlaceDimensions(place, sdcpn);
+
+    placeStates[placeId] = {


+  // Initialize transition states
+  const transitionStates: EngineFrameSnapshot["transitions"] = {};
+  for (const transition of sdcpn.transitions) {
+    transitionStates[transition.id] = {


cursor · 2026-05-12T23:20:16Z

PR Summary

High Risk
High risk because it changes @hashintel/petrinaut core simulation/playback public types and initial-marking/scenario compilation formats, which can break consumers and alter runtime behavior.

Overview
Adds a first-pass Monte Carlo experiment workflow to @hashintel/petrinaut/core, exporting new Monte Carlo simulator/worker/experiment factories and distribution metrics.

Refactors core simulation internals and public API around SimulationFrameReader/binary frame handling, makes InitialMarking JSON-serializable, and updates scenario compilation to produce numeric counts or per-token records for colored places (with expanded validation and tests).

Tightens playback backpressure typing by separating compute modes (ComputePlayMode) from viewOnly, updates docs/architecture notes, removes the old core/react/ui split RFC docs, and includes small config updates (new spin keyframe, ignore libs/analysis, changeset).

^{Reviewed by Cursor Bugbot for commit 9798ca1. Bugbot is set up for automated code reviews on this repo. Configure here.}

augmentcode · 2026-05-12T23:20:19Z

This pull request is abnormally large and would use a significant amount of tokens to review. If you still wish to review it, comment "augment review" and we will review it.

cursor

Cursor Bugbot has reviewed your changes and found 1 potential issue.

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cursor · 2026-05-15T23:48:15Z

+    run.nextFrame = run.currentFrame;
+    run.currentFrame = workingFrame;
+    run.frameNumber++;
+    run.currentTime += run.simulation.dt;


Floating-point time drift in Monte Carlo simulation loop

Medium Severity

Time is accumulated via repeated += dt additions in both advanceRun (run.currentTime += run.simulation.dt) and MonteCarloSimulatorImpl.advanceAll (this.#time += dt). Over many frames, floating-point rounding errors compound. Since run.currentTime is used for the maxTime completion check, accumulated drift can cause a run to end one frame early or late. Using run.frameNumber * dt for the comparison (or for the metric context time) would avoid this drift.

Additional Locations (1)

libs/@hashintel/petrinaut/src/core/simulation/monte-carlo/monte-carlo-simulator.ts#L89-L90

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Comment thread libs/@hashintel/petrinaut/src/core/simulation/monte-carlo/run-state.ts Fixed

Comment thread libs/@hashintel/petrinaut/src/core/simulation/monte-carlo/worker/monte-carlo.worker.ts

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Comment thread libs/@hashintel/petrinaut/src/cli/app.tsx Outdated

Comment thread libs/@hashintel/petrinaut/src/core/simulation/monte-carlo/runtime/experiment.ts

graphite-app Bot assigned kube May 13, 2026

kube added 18 commits May 15, 2026 20:49

Precompile differential equation adapters

6175ac4

Optimize transition execution metadata

a6bc0bf

Add Monte Carlo simulator

e22194d

Delete MonteCarloSimulator.md

37a0565

Add Monte Carlo experiments UI

e324de4

Add Petrinaut Monte Carlo CLI

c8ea637

Remove local Monte Carlo stress test

fc8b4ef

Add Petrinaut Monte Carlo changeset

10c22b0

Remove Petrinaut CLI

4a1444e

Update .gitignore to exclude analysis folder

0743696

Fix Petrinaut simulation pre-merge issues

fbfba88

Remove Petrinaut analysis artifacts

ce45d69

Update smooth-lizards-run.md

0058222

Fix empty colored token row validation

80550d2

Adjust experiment drawer body spacing

c50ad20

Normalize drawer body spacing

d928651

Avoid cloning Monte Carlo initial marking

045c1df

Resolve timeline icon and metric imports

d3127af

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Comment thread libs/@hashintel/petrinaut/src/core/simulation/monte-carlo/runtime/experiment.ts

Remove unused drawer style imports

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Clean up Monte Carlo transport on init failure

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kube commented May 10, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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🌟 What is the purpose of this PR?

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Pre-Merge Checklist 🚀

🚢 Has this modified a publishable library?

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codspeed-hq Bot commented May 10, 2026

Merging this PR will not alter performance

Footnotes

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github-actions Bot commented May 10, 2026

Benchmark results

@rust/hash-graph-benches – Integrations

policy_resolution_large

policy_resolution_medium

policy_resolution_none

policy_resolution_small

read_scaling_complete

read_scaling_linkless

representative_read_entity

representative_read_entity_type

representative_read_multiple_entities

scenarios

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PR Summary

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cursor Bot May 15, 2026

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Floating-point time drift in Monte Carlo simulation loop

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kube commented May 10, 2026 •

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