CoEvoSkills: 通过共进化验证实现自进化代理技能
CoEvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification
AI总结：
本文提出CoEvoSkills框架，使代理能自主生成复杂技能包，通过共进化验证提供反馈，实现在SkillsBench上最高通过率和强泛化能力。
摘要：
Anthropic提出LLM代理的技能概念以应对多步骤专业任务，其中工具是单一自包含函数，而技能是结构化的多文件依赖包。当前技能生成不仅标签密集且可能因人机认知错位导致性能下降。为此，本文提出CoEvoSkills框架，通过技能生成器和代理验证器的共进化，使代理能自主构建复杂技能包，在SkillsBench上实现最高通过率，并在六个额外LLM上表现出强泛化能力。
英文摘要：
Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks that simple tool invocations cannot address. A tool is a single, self-contained function, whereas a skill is a structured bundle of interdependent multi-file artifacts. Currently, skill generation is not only label-intensive due to manual authoring, but also may suffer from human--machine cognitive misalignment, which can lead to degraded agent performance, as evidenced by evaluations on SkillsBench. Therefore, we aim to enable agents to autonomously generate skills. However, existing self-evolving methods designed for tools cannot be directly applied to skills due to their increased complexity. To address these issues, we propose CoEvoSkills, a self-evolving skills framework that enables agents to autonomously construct complex, multi-file skill packages. Specifically, CoEvoSkills couples a Skill Generator that iteratively refines skills with a Surrogate Verifier that co-evolves to provide informative and actionable feedback without access to ground-truth test content. On SkillsBench, CoEvoSkills outperforms five baselines on both Claude Code and Codex, and generalizes strongly to six additional LLMs. The code is publicly available at https://github.com/Zhang-Henry/CoEvoSkills.
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。
