Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents — part 6/6

Cleaned-text code points [78230, 80220). Bibliography omitted here; source.md includes it.

Evolving Generalist Agent. We believe some degree of task-specific adaptation is indeed expected and even desirable, since fundamentally different types of tasks (e.g., in our case, multi-file Python repository edits vs. primarily single-file, multi-language implementations) naturally require distinct scaffolding components. Crucially, this very property highlights a unique advantage of self-improving systems like the DGM: it replaces laborious manual efforts to design specialized agents for diverse tasks with a fully automated evolutionary process. This motivates an exciting future direction of running the DGM on a large, diverse set of tasks to evolve a true generalist agent. Also, currently we only evaluated DGM on two coding benchmarks. While we believe these two benchmarks differ substantially in task structure (multi-file Python repository edits vs. primarily single-file, multi-language implementations), providing strong evidence of generality, additional benchmarks would further strengthen evaluations.

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