EvoUndo: Recoverability-Constrained Self-Evolution for LLM Agent Harnesses

EvoUndo is a framework that enables large language model agents to self‑evolve—modifying prompts, tools, and execution harnesses—while ensuring that these changes can be reliably reversed across different states. The study evaluates EvoUndo on 600 unseen one‑shot self‑evolution tasks, finding that 197 capability‑improving mutations fail recoverability checks. By extending the recovery language and adding exact state‑address diagnostics, the framework recovers up to 191 out of 197 failures, demonstrating that robust self‑evolution requires co‑designing verification, grounding, witness semantics, and recovery expressivity.

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• llms
• agents