The paper introduces the Relevance-Aware RipGrep Search Agent (RARG), which repurposes relevance from a document-selection score into an execution prior for corpus interaction. RARG orders documents for sequential ripgrep traversal, initializes promising entry points with query-relevant paragraphs, and reranks grep matches so informative excerpts appear earlier. The abstract reports improvements on the accuracy-efficiency frontier across challenging browse question answering and reasoning-intensive retrieval, but does not provide numerical results in the supplied summary.
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