SkillOpt: Executive Strategy for Self-Evolving Agent Skills
The paper introduces SkillOpt, a novel approach for optimizing agent skills in artificial intelligence. Unlike traditional methods, SkillOpt employs a systematic controllable text-space optimizer that enhances skill performance through structured edits. The results demonstrate significant improvements across various benchmarks and models, establishing SkillOpt as a leading method in the field.
- ▪SkillOpt is the first systematic controllable text-space optimizer for agent skills.
- ▪It improves skill performance by applying edits only when they enhance validation scores.
- ▪Across six benchmarks and seven target models, SkillOpt outperforms all competitors.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23904 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
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| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.23904 (cs) [Submitted on 22 May 2026] Title:SkillOpt: Executive Strategy for Self-Evolving Agent Skills Authors:Yifan Yang, Ziyang Gong, Weiquan Huang, Qihao Yang, Ziwei Zhou, Zisu Huang, Yan Li, Xuemei Gao, Qi Dai, Bei Liu, Kai Qiu, Yuqing Yang, Dongdong Chen, Xue Yang, Chong Luo View a PDF of the paper titled SkillOpt: Executive Strategy for Self-Evolving Agent Skills, by Yifan Yang and 13 other authors View PDF HTML (experimental) Abstract:Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill, and none of which reliably improves over its starting point under feedback.
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