WikiSkill：将智能体经验编译为持久化知识以促进技能进化

摘要

智能体技能包将专业知识和工作流程封装为可复用资源，以扩展AI智能体的能力。近期研究从智能体经验中自动发现此类技能，使智能体能够通过交互逐步适应。然而，指导技能发展的洞见通常散落在优化历史中，限制了其在多次迭代中的系统性复用。我们引入了WikiSkill，一个将智能体技能与持久化知识库（wiki）协同演化的框架。在高层面上，WikiSkill将原始执行经验、累积知识和可执行技能相分离，同时持续将经验整合进知识库中，使后续的技能更新能够在此基础上构建。在多样化的基准测试和模型中，WikiSkill持续优于最先进的技能进化方法，并在大多数模型-基准测试组合中优于无技能基线。我们发现技能进化与模型扩展具有互补性：较大的模型通常从进化技能中获益更多，而具备技能的较小模型可以超越没有技能的大得多的模型。我们还发现进化技能在模型间和模型家族间具有有效的迁移性，且由其他模型进化的技能可以优于自我进化的技能。最后，我们的消融研究证实，知识库中的持久知识积累对于有效的技能进化至关重要。这些结果证明了系统地积累和提炼智能体经验对于开发可复用和可迁移技能的价值。

English

Agent skills package specialized knowledge and workflows into reusable resources that extend AI agent capabilities. Recent work automatically discovers such skills from agent experience, which enables agents to progressively adapt through interaction. However, the insights that guide skill development typically remain scattered across optimization histories, limiting their systematic reuse across iterations. We introduce WikiSkill, a framework that co-evolves agent skills with a persistent knowledge base (wiki). At a high level, WikiSkill separates raw execution experience, accumulated knowledge, and executable skills, while continuously consolidating experience into the wiki, which subsequent skill updates can build on. Across diverse benchmarks and models, WikiSkill consistently outperforms state-of-the-art skill-evolution methods and improves over no-skill baselines in most model-benchmark settings. We find that skill evolution complements model scaling: larger models generally benefit more from evolved skills, while smaller models with skills can outperform substantially larger models without them. We also find that evolved skills transfer effectively across models and model families, and skills evolved by other models can outperform self-evolved skills. Finally, our ablation studies confirm that persistent knowledge accumulation in the wiki is critical for effective skill evolution. These results demonstrate the benefits of systematically accumulating and refining agent experience for developing reusable and transferable skills.