Trajectory-Guided Fault Localization for Agent Skill Evolution
Quick summary
arXiv:2610.11858v1 Announce Type: cross Abstract: Agent skills provide reusable guidance for code agents, but incomplete or unsuitable guidance can impair task execution. To reduce the manual effort of skill refinement, recent approaches use LLMs to generate revisions from execution feedback. However, grounding these revisions in explicit behavioral evidence remains challenging. To address this gap, we propose SkillMorph, a skill-evolution approach based on trajectory-guided fault localization in agent skills. Its core idea is to link execution evidence to specific skill contents before genera
Key takeaways
- arXiv:2610.11858v1 Announce Type: cross Abstract: Agent skills provide reusable guidance for code agents, but incomplete or unsuitable guidance can impair task execution.
- To reduce the manual effort of skill refinement, recent approaches use LLMs to generate revisions from execution feedback.
- However, grounding these revisions in explicit behavioral evidence remains challenging.
Why it matters
“Trajectory-Guided Fault Localization for Agent Skill Evolution” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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