{
  "id": 19180,
  "url": "https://arxiv.org/abs/2608.13120v1",
  "title": "SkillEvo: Self-Renewing Evolution Gradients from Multi-Turn Interaction Feedback",
  "summary": "Agent Skills are today either hand-authored or produced in a single LLM generation pass, and consequently possess no closed loop through which they might improve from the interaction failures they actually cause. Recent work does close this loop, but derives its feedback from single-turn question-answering evaluation. The consequence is a sharp asymmetry: once the first round has patched the gaps that a single exchange can reveal, the evolution gradient decays, the defects that surface only acro",
  "authors": "Qianxi Yan, Chunrong Chen, Jiuzhou Zhao, Min Zhang, Yongzhou Xu, Xiaochuan Xu",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-13T11:49:02.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/19180",
  "original_url": "https://arxiv.org/abs/2608.13120v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}