VCE-Skill: Enhancing Skill Self-Evolution with Version-Change Experience

Abstract:Agents increasingly rely on reusable skills to encode task knowledge, tool-use procedures, and validation rules. Existing skill self-evolution methods primarily revise skills using execution trajectories collected from current tasks, leaving the evolution knowledge accumulated in public skill version histories largely untapped. Our pilot study reveals a clear complementarity between the two sources: public skill changes provide reusable evolution priors, whereas trajectories provide evidence grounded in the current task. Motivated by this, we propose VCE-Skill, which distills noisy and implementation-specific public skill changes into reusable, structured version-change experience and adaptively fuses it with trajectory-derived proposals from the base evolver, thereby exploiting external experience while retaining task-specific evidence. Extensive experiments demonstrate that VCE-Skill improves skill self-evolution, increasing mean scores by 3.20--4.98 points; transfer experiments further show that the resulting skills achieve stronger cross-model transfer performance. Our work highlights public skill version changes as a previously underexplored yet effective source of prior knowledge and advances trajectory-driven skill self-evolution.

Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)

arXiv:2608.16544 [cs.MA]

(or arXiv:2608.16544v1 [cs.MA] for this version)

https://doi.org/10.48550/arXiv.2608.16544 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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