# Towards Retrieving Interaction Spaces for Agentic Search

arXiv: https://arxiv.org/abs/2606.06880 · HTML: https://mlsanity.com/paper/2606.06880/

## Key facts

- Cited by 7 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 17 of its 47 references that are in the map.
- First posted on arXiv 5 Jun 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, 17 of 17)

- [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)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning](https://mlsanity.com/paper/2503.09516/) (2025)
- [The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale](https://mlsanity.com/paper/2406.17557/) (2024)
- [BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents](https://mlsanity.com/paper/2504.12516/) (2025)
- [WebThinker: Empowering Large Reasoning Models with Deep Research Capability](https://mlsanity.com/paper/2504.21776/) (2025)
- [BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agent](https://mlsanity.com/paper/2508.06600/) (2025)
- [BM25S: Orders of magnitude faster lexical search via eager sparse scoring](https://mlsanity.com/paper/2407.03618/) (2024)
- [Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction](https://mlsanity.com/paper/2605.05242/) (2026)
- [Is Grep All You Need? How Agent Harnesses Reshape Agentic Search](https://mlsanity.com/paper/2605.15184/) (2026)
- [AgentIR: Reasoning-Aware Retrieval for Deep Research Agents](https://mlsanity.com/paper/2603.04384/) (2026)
- [Coding Agents are Effective Long-Context Processors](https://mlsanity.com/paper/2603.20432/) (2026)
- [Agentic-R: Learning to Retrieve for Agentic Search](https://mlsanity.com/paper/2601.11888/) (2026)
- [Rethinking Agentic Search with Pi-Serini: Is Lexical Retrieval Sufficient?](https://mlsanity.com/paper/2605.10848/) (2026)
- [SAGE: Benchmarking and Improving Retrieval for Deep Research Agents](https://mlsanity.com/paper/2602.05975/) (2026)
- [Revisiting Text Ranking in Deep Research](https://mlsanity.com/paper/2602.21456/) (2026)

## Built on by (most cited first, 7 of 7)

- [A New Role for Relevance: Guiding Corpus Interaction in Agentic Search](https://mlsanity.com/paper/2607.24223/) (2026)
- [ITER: Interaction-Aware Retrieval for Agentic Search](https://mlsanity.com/paper/2608.27912/) (2026)
- [Why Git Is the Memory Solution for the Agentic Development Lifecycle](https://mlsanity.com/paper/2607.14390/) (2026)
- [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)
- [JevGPT: Thinking Fast and Slow in Autonomous Research](https://mlsanity.com/paper/2610.18272/) (2026)

## Abstract (arXiv)

Retrieval for search agents is still inherited from non-agentic information retrieval: a retriever ranks the corpus and the agent reads a small set of returned documents. Recent direct corpus interaction (DCI) work shows that agents can instead interact with the raw corpus through shell tools such as grep and file reads. But unbounded interaction does not scale: every broad shell command is a scan over the whole corpus, and latency degrades sharply as the corpus grows. We argue that the role of retrieval for agentic search is not just to select documents that fit in the LLM context window, but to construct an interaction space: a bounded subset of the corpus the agent can explore with associated tools. Two design consequences follow. The space needs a boundary supplied by retrieval, and the objects within it should be processed for interaction. As a proof of concept, we propose RISE (Retrieving Interaction SpacE): we use BM25 to construct the interaction space; meanwhile, its documents are processed during indexing for shell-style navigation. On BrowseComp-Plus, RISE matches the pure-shell DCI baseline at 78% accuracy with gpt-5.4-mini at roughly one quarter of the per-query cost. At 1M documents, RISE-BM25 reaches 81% on gpt-5.4-mini, whereas DCI on gpt-5.4-nano degrades to 60% with 33 of 100 wall-clock failures.