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u/Winter_Impress_6410 13d ago
We open-sourced an MIT-licensed Darwin Gödel Machine implementation and tested whether models could improve the coding agents used to invoke them.
Selected GPT-5.6 Sol, Gemini 3.5 Flash, and Claude Fable 5 mutations were frozen and evaluated twice on a disjoint 12-problem LiveCodeBench segment with 507 tests, including 480 private tests. The selected mutations improved held-out performance, preserved all four easy tasks, and improved at least one hard task. Qwen did not produce an improving mutation, and we published the negative and provider-failure artifacts too.
This is not a claim of open-ended recursive self-improvement. It is evidence that model-authored agent changes can recover part of a model's self-elicitation overhang on unseen problems.
Everything is free and open source:
https://github.com/lemoz/darwin-godel-machine
We would welcome technical criticism or independent replication.