SelfEvoSkill: Paired Execution Audits for Skill Revision without External Outcome Supervision
Agent skills provide LLM agents with reusable procedures, but a given skill may be incomplete, ineffective, or misleading for the current task. Existing methods for revising skills typically rely on external outcome supervision, such as task rewards, performance on a separate validation set, or feedback from deployment, to decide which updates to retain. However, this supervision may be costly or unavailable when adapting a skill to a new task. Using execution behavior as an alternative signal is challenging: a single run reflects the combined effects of the agent, task, and workspace, making the skill's contribution difficult to isolate. Comparing executions with and without the skill, w...
| Authors | Haowen Gao, Haoran Chen, Can Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 12 June 2026 |
| Last updated at source | 3 October 2026 |
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