相关性的新角色：指导智能体搜索中的语料库交互

摘要

相关性是一种查询相关的估计，用于判断文档或片段是否包含有用证据。现有检索代理利用相关性选择top-k内容，但仅凭文档相关性无法定位、组合或验证复杂问题所需证据。直接语料库交互（DCI）通过grep式探索实现此类细粒度操作，但其忽略相关性的搜索可能较晚暴露有用线索，从而延迟收敛。近期进展利用相关性将语料库缩小为交互的工作空间。然而，一旦交互开始，相关性仍无法直接指导grep优先搜索哪些文档，或从大量匹配结果中区分出信息性片段，使大语言模型优先看到它们。我们提出相关性感知的RipGrep搜索代理（RARG），将相关性转化为语料库交互的执行先验。RARG提供由粗到细的相关性引导：它对文档进行排序以进行顺序的“ripgrep”遍历，从而更早暴露全局相关线索；用查询相关段落初始化有前景的入口点；并重新排序grep匹配结果，以凸显文档级排序可能掩盖的信息性片段。在具有挑战性的浏览问答和推理密集型检索中，RARG相较于基于检索和直接交互的代理提升了准确-效率边界。这些结果表明，相关性感知的交互能够实现更快、更可靠的搜索收敛。

English

Relevance is a query-dependent estimate of whether a document or excerpt contains useful evidence. Existing retrieval agents use relevance to select top-k content, but document relevance alone cannot localize, compose, or verify the evidence required by complex questions. Direct Corpus Interaction (DCI) enables such fine-grained operations through grep-style exploration, but its relevance-agnostic search can expose useful clues late and delay convergence. Recent advances use relevance to narrow the corpus into a working space for interaction. Once interaction begins, however, relevance still does not directly guide which documents grep searches first or distinguish informative excerpts from a broad set of matches to let LLMs see them first. We introduce the Relevance-Aware RipGrep Search Agent (RARG), which turns relevance into an execution prior for corpus interaction. RARG provides coarse-to-fine relevance guidance: it orders documents for sequential 'ripgrep' traversal to expose globally relevant clues earlier, initializes promising entry points with query-relevant paragraphs, and reranks grep matches to surface informative excerpts that document-level ranking may otherwise obscure. Across challenging browse question answering and reasoning-intensive retrieval, RARG improves the accuracy--efficiency frontier over retrieval-based and direct-interaction agents. These results demonstrate that relevance-aware interaction enables faster and more reliable search convergence.