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Claude
7e25f39bc5
The original upload never called a model, its autonomous actions only logged that they had happened, and its benchmark was rigged. This replaces it with: - museengine.engine: analyze -> map -> generate -> adversarial critique -> Pareto selection, each step an LLM call with a JSON contract. Offline mode returns labelled keyword suggestions and no fake scores. - museengine.memory: plain-SQLite episodes, notes, state and audit log. - museengine.governance + agent: an autonomous loop behind an allowlist, step budget, kill switch and human approval for self-chosen goals, with every decision and real effect audited. - museengine.evaluation: blinded, order-swapped A/B eval with an exact sign test, replacing the keyword scorer and fudge constants. - Optional Anthropic adapter; the core accepts any (str) -> str callable. - pyproject, CLI, 24 offline tests, CI workflow. The original files are kept unchanged in legacy/ (minus a duplicate). Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01YbuVfbQEpaitM5TMjBMzkF
1.4 KiB
1.4 KiB
Legacy upload
These are the original files, kept unchanged for reference (apart from removing a byte-identical duplicate of the eval harness). They are not imported by the museengine package.
The rewrite in v0.2 addressed the following problems:
- No model in the loop.
llm_callablewas stored and never called. Every "phase" was keyword counting plus f-string templates, so "How do I bake sourdough?" got a slime-mold network design. - Fake actions. In
Autonomic Cognitive Os.py, "build" and "falsify" only adjusted floats and logged sentences such as "verified conservation law". The "compiled skills" pointed to a function that doesn't exist. Goals rotated through 4 hard-coded strings. Only 4 of the advertised 5 agents ever bid. - Rigged evaluation. In
Muse Eval Harness.py:- The scorer rewarded the same jargon the templates emit.
- The baselines were hand-written strawmen.
- The DMN "lift" added a literal
+ 38.5. - The ablation didn't ablate, because
seed_traces=[]fell back to the default traces. - The simulations gave the engine a hard-coded capacity advantage.
- Not runnable as uploaded. Filenames contain spaces while the modules import snake_case names, and the code prepends a path from the original authoring sandbox.
- Persistence. It rewrote the whole SQLite file on every tick with no temp file and no rename, while its docs called this "atomic".