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Paper page - Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction

Source: https://huggingface.co/papers/2605.05242 Published on May 3

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Abstract

Direct corpus interaction enables more effective agentic search by allowing agents to query raw text directly, outperforming traditional retrieval methods in complex tasks.

Modernretrieval systems, whether lexical or semantic, expose a corpus through a fixed similarity interface that compresses access into a single top-k retrieval step before reasoning. This abstraction is efficient, but foragentic search, it becomes a bottleneck: exact lexical constraints, sparse clue conjunctions, local context checks, and multi-step hypothesis refinement are difficult to implement by calling a conventional off-the-shelf retriever, and evidence filtered out early cannot be recovered by stronger downstream reasoning. Agentic tasks further exacerbate this limitation because they require agents to orchestrate multiple steps, including discovering intermediate entities, combining weak clues, and revising the plan after observing partial evidence. To tackle the limitation, we studydirect corpus interaction(DCI), where an agent searches the raw corpus directly with general-purposeterminal tools(e.g., grep, file reads, shell commands, lightweight scripts), without any embedding model, vector index, or retrieval API. This approach requires no offline indexing and adapts naturally to evolving local corpora. AcrossIR benchmarksand end-to-endagentic searchtasks, this simple setup substantially outperforms strong sparse, dense, andrerankingbaselines on several BRIGHT andBEIR datasets, and attains strong accuracy onBrowseComp-Plusandmulti-hop QAwithout relying on any conventional semantic retriever. Our results indicate that as language agents become stronger, retrieval quality depends not only on reasoning ability but also on the resolution of the interface through which the model interacts with the corpus, with which DCI opens a broader interface-design space foragentic search.

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Zhuofeng Li (@zhuofengli96475): 🔥 Introducing Direct Corpus Interaction (DCI)! The best retriever for agentic search is no retriever.

🚀 We replaced the entire agentic search pipeline — embedding model, vector index, top-k retrieval — with only grep and bash. 🔧

📄 Paper: https://t.co/h1CWuCtuQz

DCI