# A New Role for Relevance: Guiding Corpus Interaction in Agentic Search

arXiv: https://arxiv.org/abs/2607.24223 · HTML: https://mlsanity.com/paper/2607.24223/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 23 of its 40 references that are in the map.
- First posted on arXiv 27 Jul 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 23 of 23)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning](https://mlsanity.com/paper/2503.09516/) (2025)
- [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://mlsanity.com/paper/2212.10509/) (2022)
- [WebThinker: Empowering Large Reasoning Models with Deep Research Capability](https://mlsanity.com/paper/2504.21776/) (2025)
- [Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG](https://mlsanity.com/paper/2501.09136/) (2025)
- [BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agent](https://mlsanity.com/paper/2508.06600/) (2025)
- [BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval](https://mlsanity.com/paper/2407.12883/) (2024)
- [Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction](https://mlsanity.com/paper/2605.05242/) (2026)
- [ExpSeek: Self-Triggered Experience Seeking for Web Agents](https://mlsanity.com/paper/2601.08605/) (2026)
- [Rethinking Agentic Search with Pi-Serini: Is Lexical Retrieval Sufficient?](https://mlsanity.com/paper/2605.10848/) (2026)
- [GrepSeek: Training Search Agents for Direct Corpus Interaction](https://mlsanity.com/paper/2605.29307/) (2026)
- [Selection-p: Self-Supervised Task-Agnostic Prompt Compression for Faithfulness and Transferability](https://mlsanity.com/paper/2410.11786/) (2024)
- [PRELUDE: A Benchmark Designed to Require Global Comprehension and Reasoning over Long Contexts](https://mlsanity.com/paper/2508.09848/) (2025)
- [Towards Retrieving Interaction Spaces for Agentic Search](https://mlsanity.com/paper/2606.06880/) (2026)
- [Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understanding](https://mlsanity.com/paper/2512.17220/) (2025)
- [Dr-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion](https://mlsanity.com/paper/2606.14885/) (2026)
- [HGMEM: Hypergraph-based Working Memory to Improve Multi-step RAG for Long-Context Complex Relational Modeling](https://mlsanity.com/paper/2512.23959/) (2025)
- [Many-Shot CoT-ICL: Making In-Context Learning Truly Learn](https://mlsanity.com/paper/2605.13511/) (2026)
- [Rethinking Agentic RAG: Toward LLM-Driven Logical Retrieval Beyond Embeddings](https://mlsanity.com/paper/2605.27123/) (2026)
- [MiA-Signature: Approximating Global Activation for Long-Context Understanding](https://mlsanity.com/paper/2605.06416/) (2026)

## Built on by (most cited first, 3 of 3)

- [Projecting BrowseComp-Plus onto ClimbMix: Toward More Realistic Corpora for Agentic Search](https://mlsanity.com/paper/2608.20317/) (2026)
- [Evidence Blindness in Direct Corpus Interaction: Persistent Navigation with AtlasNav](https://mlsanity.com/paper/2608.24764/) (2026)
- [RunningTab: Direct Workspace Interaction with Environment-Side Tabs](https://mlsanity.com/paper/2610.10444/) (2026)

## Abstract (arXiv)

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.