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智能评分 60
SkillWeaver: 面向LLM智能体的组合技能路由
AI 推荐理由
与现有单技能路由方法不同，该工作首次系统性地处理技能组合问题，并提供了专门的多技能基准，值得阅读原文以了解具体分解与规划实现。核心解读
该论文提出组合技能路由方法SkillWeaver，将复杂查询分解为原子子任务，为每个子任务检索合适技能并组合成可执行计划。系统采用LLM分解器、bi-encoder FAISS检索器和依赖感知DAG规划器，并配套发布CompSkillBench基准（300个组合查询，覆盖2209个真实技能），以直接评估多技能场景下的智能体性能。
全文
Cool paper on Skill routing for LLM agents.
Real tasks rarely map to a single skill. They need several composed together, but most skill routing still treats the problem as picking one tool from a library.
This work formalizes Compositional Skill Routing, decomposes a complex query into atomic sub-tasks, retrieves the right skill for each, and then composes an executable plan.
The system, SkillWeaver, pairs an LLM decomposer with a bi-encoder FAISS retriever and a dependency-aware DAG planner.
It comes with CompSkillBench, 300 compositional queries over 2,209 real skills, so the multi-skill case gets measured directly.
Why does it matter?
As skill libraries grow, single-skill retrieval quietly caps what an agent can do. The DAG planner turns retrieved skills into an ordered, dependency-respecting plan.
Paper: https://t.co/OvyScHiis1
Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX
