Why this matters
Many retrieval agents treat relevance as a static selection signal (top‑k documents) and then hand everything to an LLM, which struggles to localize, compose, or verify dispersed evidence. Direct Corpus Interaction (DCI) enables fine‑grained grep‑style exploration but can wander through low‑value matches and converge slowly. The core insight of this paper is simple but consequential: make relevance an execution prior that actively guides how and where the agent searches the corpus, so globally relevant clues surface earlier and LLM interaction converges faster.
Key Findings
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Relevance-as-prior: Relevance scores are used beyond document ranking — they determine traversal order for sequential 'ripgrep' scans, initialize high‑value entry paragraphs, and rerank grep matches so that informative excerpts reach the LLM earlier. This shifts relevance from a filter to an execution policy.
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Coarse-to-fine guidance: The proposed agent (RARG) applies relevance at multiple granularities (document ordering → paragraph seeding → excerpt reranking), which reduces wasted search and accelerates evidence discovery during interactive corpus exploration.
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Empirical payoff: Across browse question answering and reasoning‑heavy retrieval tasks, RARG improves the accuracy–efficiency tradeoff relative to both retrieval‑first pipelines and relevance‑agnostic DCI agents, yielding faster and more reliable convergence to correct answers.
Who it’s for — and tradeoffs
Great fit if you build or evaluate agentic search systems that need robust evidence localization (e.g., browse QA, multi‑hop reasoning, or tool-using LLM agents). RARG is especially useful when document relevance signals are available and where early exposure of globally relevant clues shortens costly LLM interactions. Look elsewhere if you cannot produce reasonably calibrated document‑level relevance scores, if corpus access latency makes sequential traversal impractical, or if absolute end-to-end throughput (many concurrent queries) is prioritized over per‑query search convergence time.
Where it fits
RARG sits between retrieval‑centric RAG pipelines and pure DCI agents: it retains interactive, grep‑style capabilities for fine‑grained evidence assembly while borrowing relevance structure to guide that interaction more effectively. Methodologically, it is most relevant to systems that combine retrieval, excerpt-level reasoning, and iterative LLM prompting.
Implementation notes (conceptual)
The method requires (1) a document relevance estimator to seed priors, (2) a mechanism to order documents for sequential ripgrep traversal, and (3) a reranking step that scores candidate excerpts by informativeness before exposing them to the LLM. These steps introduce extra orchestration and reranking cost, so benefits should be weighed against added computation and pipeline complexity.