VCE-Skill Mines Public Skill Version Histories as Evolution Priors, Adding 3.20-4.98 Points Over Trajectory-Only Self-Evolution

arXiv 2608.16544·medium signalXBlueskyLinkedInCopy link

Current skill self-evolution methods revise skills using only execution trajectories from the current task, ignoring the accumulated diff history of public skill repositories. A pilot study shows the two sources are complementary — version changes supply reusable evolution priors, trajectories supply task-grounded evidence — so VCE-Skill distills noisy, implementation-specific public changes into structured version-change experience and adaptively fuses it with the base evolver's trajectory proposals. Mean scores rise 3.20-4.98 points, and the evolved skills transfer better across models, pointing at a source of prior knowledge most agent-skill systems currently throw away.