arXiv Computer Vision
3d ago

Skill-V: Verifiable Self-Evolving Skill Library for Interactive Agents

Skill‑V is a verifiable self‑evolving skill library for interactive agents that represents skills as versioned, falsifiable contracts linking semantic intent to observable behavior. The system uses task failures to add new skills and contract‑outcome disagreements to revise existing skill boundaries, ensuring revisions preserve semantic constraints and pass non‑regression tests on historical evidence. An applicability‑aware filter excludes confidently inapplicable skill candidates, leading to high success rates on ALFWorld (95.3%) and WebShop (85.9%) while keeping the library more compact than growth‑only baselines.

By Jie Ma, Zhipeng Qian, Yufei Ma, Zihan Liang, Jiayi Ji, Qingpeng Cai, Ben Chen, Peng Jiang, Xiaoshuai Sun
arXiv AI
Sep 7

From Interaction Traces to Persistent Skills: Online Evolution for Computer-Use Agents

The paper introduces an online skill‑evolution framework that transforms interaction traces and evaluator feedback into a persistent, versioned library of reusable procedures for computer‑use agents. By executing each iteration against a frozen library snapshot, the system updates skills without altering the underlying model parameters. Experiments across four OSWorld domains show that the evolving library consistently outperforms an empty‑library baseline, with gains ranging from 5.7 to 18.6 percentage points, while also revealing domain‑specific temporal stability and challenges in skill retrieval and revision.

By Longtao Hu, Xiao Liang, Linchao Zhu
arXiv Machine Learning
Aug 10

SkillAligner: Treating Retrieved Skills as Adaptable Drafts at Execution Time

arXiv:2608. 06880v1 Announce Type: new Abstract: General-purpose skills promise reusable procedural knowledge for language agents, yet semantic relevance does not guarantee execution utility: a retrieved skill may encode assumptions that conflict with the current task, execution environment, or other retrieved skills.

By Qinfeng Li, Dalin He, Yuntai Bao, Ying Yang, Ruoxi Chen, Xinyan Yu, Lizhou Liang, Ge Su, Wenqi Zhang, Xuhong Zhang