不要检索，导航：将企业知识转化为可导航的代理技能用于问答和RAG
Corpus2Skill: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG
AI总结：
本文提出Corpus2Skill，通过将文档库转化为层次化技能目录，使LLM代理在服务时能导航该目录，提升问答质量和 grounding，但指出导航并非所有场景下的最佳替代方案。
摘要：
Retrieval-Augmented Generation (RAG) 使LLM响应基于外部证据，但将模型视为被动搜索结果消费者，无法理解语料库的组织方式或未见过的内容。我们提出了Corpus2Skill，将文档语料库离线转化为层次化技能目录，让LLM代理在服务时导航该目录，从宏观视角逐步细化到文档，必要时回溯无效分支。在企业客户支持基准上，Corpus2Skill在单次密集、混合、分层检索和代理RAG基线中提升了回答质量和 grounding，以适度的成本权衡。进一步的十组泛化研究显示，导航并非通用替代检索：它在具有可恢复专题分类的单域语料库中表现一致，但在开放域事实池或同质表格语料库中，扁平检索仍更优。我们界定了此范围差异，并将其作为知识导向系统的設計指南。代码可在https://github.com/dukesun99/Corpus2Skill获取。
英文摘要：
Retrieval-Augmented Generation (RAG) grounds LLM responses in external evidence but treats the model as a passive consumer of search results, with no view of how the corpus is organized or what it has not yet seen. We present Corpus2Skill, a system-level retrieval architecture for bounded, structurally coherent corpora such as enterprise knowledge bases: an offline compiler distills the corpus into a hierarchical skill directory, and at serve time an LLM agent navigates it, drilling from a bird's-eye view through progressively finer summaries down to documents and backtracking when a branch is unproductive. On an enterprise customer-support benchmark, Corpus2Skill improves both answer quality and grounding over single-shot dense, hybrid, hierarchical-retrieval, and agentic RAG baselines at a moderate cost tradeoff, and the lead persists under encoder-matched controls and paired significance tests. An eleven-dataset study shows that corpus navigation is not a universal replacement for retrieval: it significantly wins on five datasets, ties on three, and loses on three. It helps on single-domain corpora with a recoverable topical taxonomy, but flat retrieval remains preferable on open-domain factoid pools or homogeneous-tabular corpora that defeat top-level clustering. We characterize this scope distinction as a design guideline for knowledge-grounded systems. Code is available at https://github.com/dukesun99/Corpus2Skill.
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。
