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EVISKILL: Grounding Skill Evolution in Replayable Evidence

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Briefings

EVISKILL is an evidence-driven framework for LLM agents that organizes interaction observations into Replayable Evidence Cards to track skill evolution. Unlike existing methods that discard behavioral evidence, EVISKILL links edits to their supporting contexts and uses targeted replay to verify changes. Experiments on three interactive benchmarks across six LLM backbones show the approach's effectiveness in preserving evidence and refining procedural knowledge.

Key points

• EVISKILL organizes execution observations into Replayable Evidence Cards to track skill evolution without updating model parameters.
• The framework links edits to their supporting contexts and uses targeted replay to verify changes before incorporation.

Context

LLM agents can accumulate procedural knowledge from experience, but existing methods lose behavioral evidence and task contexts. EVISKILL addresses this by organizing observations into evidence cards and using targeted replay to verify edits.

Limitations

Experiments were conducted on three interactive benchmarks across six LLM backbones. The framework's effectiveness depends on determining what to change, why changes are justified, and when they should become persistent guidance.

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