EVISKILL: Grounding Skill Evolution in Replayable Evidence

Excerpt

arXiv:2610.05030v1 Announce Type: new Abstract: 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. However, existing experience-driven methods can lose the behavioral evidence and task contexts supporting edits. Moreover, a global validatio