SkillClaw: 让技能与代理进化者共同进化
SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
AI总结：
SkillClaw通过聚合多用户交互数据，实现技能的集体进化，提升代理在现实场景中的性能。
摘要：
大型语言模型（LLM）代理如OpenClaw依赖可重用技能执行复杂任务，但这些技能在部署后大多静态。为解决此问题，本文提出SkillClaw框架，通过聚合多用户和跨时间的交互数据，持续改进技能。SkillClaw利用自主进化器识别行为模式，并通过优化现有技能或扩展新能力来更新技能集。实验表明，SkillClaw在WildClawBench上显著提升了Qwen3-Max在现实场景中的性能。
英文摘要：
Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after deployment. As a result, similar workflows, tool usage patterns, and failure modes are repeatedly rediscovered across users, preventing the system from improving with experience. While interactions from different users provide complementary signals about when a skill works or fails, existing systems lack a mechanism to convert such heterogeneous experiences into reliable skill updates. To address these issues, we present SkillClaw, a framework for collective skill evolution in multi-user agent ecosystems, which treats cross-user and over-time interactions as the primary signal for improving skills. SkillClaw continuously aggregates trajectories generated during use and processes them with an autonomous evolver, which identifies recurring behavioral patterns and translates them into updates to the skill set by refining existing skills or extending them with new capabilities. The resulting skills are maintained in a shared repository and synchronized across users, allowing improvements discovered in one context to propagate system-wide while requiring no additional effort from users. By integrating multi-user experience into ongoing skill updates, SkillClaw enables cross-user knowledge transfer and cumulative capability improvement, and experiments on WildClawBench show that limited interaction and feedback, it significantly improves the performance of Qwen3-Max in real-world agent scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
