SkillVine: Agent Skill Evolution via Branching Exploration
Agent skills encapsulate reusable procedural knowledge that enables LLM agents to perform tasks, and they can be improved automatically using trajectories from interactions with the environment. This is the classic problem of skill evolution. Existing approaches predominately follow a linear evolution paradigm, in which updates are sequentially applied to the latest skill-library version. As a result, they inevitably fall into local optima, leaving many promising evolution paths unexplored. We propose SkillVine, an automatic skill-evolution framework that formulates skill evolution as a graph search problem and employs a branching exploration strategy. Equipped with a trunk-branch collabo...
| Authors | Kaiwei Liu, Jiqian Dong, Liran Dong et al. |
|---|---|
| Primary category | cs.AI |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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