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EvoUndo: Recoverability-Constrained Self-Evolution for LLM Agent Harnesses [R]

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AI 摘要

LLM agents can modify their own prompts, tools, and execution harnesses at runtime, a process called self-evolution. While this can enhance capabilities, successful mutations may create persistent effects that are difficult to reverse safely. A protocol-locked 2x2 grounding-by-expressivity intervention significantly improves recovery, increasing successful recovery from 0/48 to 38/48 (79.2%) when the original language is sufficient. Extending the recovery language further enables recovery on 142/143 (99.3%) failures in the oracle-defined S1 stratum.