arXiv Computer Vision
SkillCycle: Co-Evolving Agent Policies and Skill Banks
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SkillMaster: Toward Autonomous Skill Mastery in LLM Agents
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arXiv AI
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SkillAudit: Ground-Truth-Free Skill Evolution via Paired Trajectory Auditing
arXiv:2606. 14239v1 Announce Type: new Abstract: Agent skills are structured procedural packages that guide frozen LLM agents in specialized workflows.
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MMG2Skill: Can Agents Distill In-the-Wild Guides into Self-Evolving Skills?
arXiv:2606. 01993v1 Announce Type: cross Abstract: Abundant procedural knowledge on the Web holds great potential for helping agents solve long-horizon tasks.
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EASE: Behavior-Adaptive Skill Curation for Self-Evolving Agents
arXiv:2609.36746v1 Announce Type: new Abstract: Agent skills provide a lightweight mechanism for self-evolving agents to accumulate reusable procedural knowledge without updating model parameters. Ho...
arXiv Computation and Language
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Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution
The paper introduces SGUID, a method that selects a compact subset of skills for distillation in large language models. It shows that fewer than 25% of retrieved skills are useful for on‑policy distillation and that distilling only the selected skills can match or surpass full‑bank distillation while using up to 11× fewer skills. SGUID also enables stable model‑skill co‑evolution by iteratively curating new skill banks from updated model rollouts.
arXiv AI
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Harnessing LLM Agents with Skill Programs
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