AI搜索新思路：用相关性给文件搜索排优先级
现在的AI搜索工具（比如RAG）通常只靠相关性挑出最相关的几个文件，但遇到复杂问题（比如需要从多个文件里拼凑答案、验证事实）就抓瞎了。这篇论文提出一个叫RARG的方法，它把相关性当成搜索的“导航仪”：先按相关性给文件排序，优先搜那些可能更有用的文件；搜的时候，还会把文件里最相关的段落提前展示给AI看，而不是让AI自己从头翻。结果在需要推理的问答任务上，RARG比传统方法更快更准。它不是你明天就能用的功能，但说明了一个趋势：AI搜索的下一个进化方向，不是找更多文件，而是更聪明地“翻文件”。
📄 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.