AI搜索不再只翻前10条，而是像程序员一样grep全文
现在的AI搜索（比如RAG）通常只翻出最相关的10个文档片段，但复杂问题需要的证据可能藏在文档深处，或者需要跨文档拼凑。这篇论文让AI像程序员用grep命令一样，在全文里逐行搜索，但又不盲目——它先用相关性排序文档，优先搜索可能藏答案的文档，再在文档内把匹配行按信息量重新排序，让AI先看到最有用的片段。在需要多步推理的问答任务上，这种“先粗筛再细搜”的方法比传统检索快且准。它不是你明天能用上的，但揭示了下一代搜索代理的方向：从“翻书”变成“侦探式排查”。
📄 原文摘要(英文)
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.