AI 中文总结
SkillVine将技能进化建模为图搜索，通过分支探索与主干-分支协作机制平衡探索与利用，在多个基准上超越线性进化方法。
AI 中文摘要
智能体技能封装了可复用的程序性知识，使大语言模型（LLM）智能体能够执行任务，并且可以利用与环境交互的轨迹自动改进这些技能。这是经典的技能进化问题。现有方法主要遵循线性进化范式，即按顺序将更新应用于最新版本的技能库。因此，它们不可避免地陷入局部最优，留下许多有前景的进化路径未被探索。我们提出SkillVine，一个自动技能进化框架，将技能进化表述为图搜索问题，并采用分支探索策略。凭借主干-分支协作搜索机制、智能父节点选择器和自适应粒度更新规则，SkillVine在探索与利用之间取得了平衡。我们在5个基准上使用两种LLM评估了SkillVine。结果表明，SkillVine在分支上发现的技能库版本优于线性主干上的版本，并在十个基准-模型组合中的九个上取得了最佳测试性能。
英文摘要
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 collaborative searching mechanism, an intelligent parent-node selector, and an adaptive-granularity update rule, SkillVine achieves a balance between exploration and exploitation. We evaluate SkillVine on 5 benchmarks with two LLMs. Results show that SkillVine discovers better skill-library versions along branches than along the linear trunk and achieves the best test performance in nine of ten benchmark-model combinations.
