EVISKILL: Grounding Skill Evolution in Replayable Evidence
EVISKILL uses replayable evidence so LLM agents can refine skills without updating model parameters.
EVISKILL organizes agent execution observations into Replayable Evidence Cards and links proposed skill edits to their supporting contexts. Targeted replay re-executes edits for verification, while provisional retention preserves locally supported changes until global validation decides persistence. Experiments on three interactive benchmarks and six LLM backbones support continual skill refinement without updating model parameters.
- Stores execution context in Replayable Evidence Cards.
- Targeted replay verifies proposed skill edits by re-execution.
- Global validation decides which edits become persistent skills.
- Evaluated on three benchmarks across six LLM backbones.
From the original article
1 min read at huggingface.co
Continual skill evolution enables LLM agents to accumulate and refine reusable procedural knowledge from interaction experience without updating model parameters. Its effectiveness depends on determining not only what to change, but also why a change is justified and when it should become persistent guidance.
By Yan Zhou, Yili Wang, Yiwei Dai · huggingface.co
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