# A Survey of Reasoning-Intensive Retrieval: Progress and Challenges

arXiv: https://arxiv.org/abs/2605.00063 · HTML: https://mlsanity.com/paper/2605.00063/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 32 of its 100 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Builds on (most cited first, 30 of 32)

- [Gecko: Versatile Text Embeddings Distilled from Large Language Models](https://mlsanity.com/paper/2403.20327/) (2024)
- [ReasonIR: Training Retrievers for Reasoning Tasks](https://mlsanity.com/paper/2504.20595/) (2025)
- [Rank1: Test-Time Compute for Reranking in Information Retrieval](https://mlsanity.com/paper/2502.18418/) (2025)
- [Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning](https://mlsanity.com/paper/2503.06034/) (2025)
- [ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability](https://mlsanity.com/paper/2508.07050/) (2025)
- [UME-R1: Exploring Reasoning-Driven Generative Multimodal Embeddings](https://mlsanity.com/paper/2511.00405/) (2025)
- [DIVER: A Multi-Stage Approach for Reasoning-intensive Information Retrieval](https://mlsanity.com/paper/2508.07995/) (2025)
- [RAR-b: Reasoning as Retrieval Benchmark](https://mlsanity.com/paper/2404.06347/) (2024)
- [JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking](https://mlsanity.com/paper/2411.00142/) (2024)
- [Rank-K: Test-Time Reasoning for Listwise Reranking](https://mlsanity.com/paper/2505.14432/) (2025)
- [R2MED: A Benchmark for Reasoning-Driven Medical Retrieval](https://mlsanity.com/paper/2505.14558/) (2025)
- [ReasonEmbed: Enhanced Text Embeddings for Reasoning-Intensive Document Retrieval](https://mlsanity.com/paper/2510.08252/) (2025)
- [O1 Embedder: Let Retrievers Think Before Action](https://mlsanity.com/paper/2502.07555/) (2025)
- [EnrichIndex: Using LLMs to Enrich Retrieval Indices Offline](https://mlsanity.com/paper/2504.03598/) (2025)
- [AgentIR: Reasoning-Aware Retrieval for Deep Research Agents](https://mlsanity.com/paper/2603.04384/) (2026)
- [Large Reasoning Embedding Models: Towards Next-Generation Dense Retrieval Paradigm](https://mlsanity.com/paper/2510.14321/) (2025)
- [BESPOKE: Benchmark for Search-Augmented Large Language Model Personalization via Diagnostic Feedback](https://mlsanity.com/paper/2509.21106/) (2025)
- [MR2-Bench: Going Beyond Matching to Reasoning in Multimodal Retrieval](https://mlsanity.com/paper/2509.26378/) (2025)
- [CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval](https://mlsanity.com/paper/2506.11066/) (2025)
- [ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking](https://mlsanity.com/paper/2509.00520/) (2025)
- [Your Dense Retriever is Secretly an Expeditious Reasoner](https://mlsanity.com/paper/2510.21727/) (2025)
- [Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense Retrieval](https://mlsanity.com/paper/2503.23033/) (2025)
- [TFRank: Think-Free Reasoning Enables Practical Pointwise LLM Ranking](https://mlsanity.com/paper/2508.09539/) (2025)
- [Beyond Sequential Reranking: Reranker-Guided Search Improves Reasoning Intensive Retrieval](https://mlsanity.com/paper/2509.07163/) (2025)
- [Think Before You Retrieve: Learning Test-Time Adaptive Search with Small Language Models](https://mlsanity.com/paper/2511.07581/) (2025)
- [InsertRank: LLMs can reason over BM25 scores to Improve Listwise Reranking](https://mlsanity.com/paper/2506.14086/) (2025)
- [Reason to Retrieve: Enhancing Query Understanding through Decomposition and Interpretation](https://mlsanity.com/paper/2509.06544/) (2025)
- [LLM-guided Hierarchical Search for End-to-end Reasoning Intensive Retrieval](https://mlsanity.com/paper/2510.13217/) (2025)
- [GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs](https://mlsanity.com/paper/2511.11653/) (2025)
- [BRIGHT+: Upgrading the BRIGHT Benchmark with MARCUS, a Multi-Agent RAG Clean-Up Suite](https://mlsanity.com/paper/2506.07116/) (2025)

## Built on by (most cited first, 1 of 1)

- [Beyond Polarization: The Generative Constraint of Chain-of-Thought in Pointwise Reranking](https://mlsanity.com/paper/2608.30398/) (2026)

## Abstract (arXiv)

Reasoning-Intensive Retrieval (RIR) targets retrieval settings where relevance is mediated by latent inferential links between a query and supporting evidence, rather than semantic similarity. Motivated by the emergent reasoning abilities of Large Language Models (LLMs), recent work integrates these capabilities into the IR field, spanning the entire pipeline from benchmarks to retrievers and rerankers. Despite this progress, the field lacks a systematic framework to organize current efforts and articulate a clear path forward. To provide a clear roadmap for this rapidly growing yet fragmented area, this survey (1) systematizes existing RIR benchmarks by knowledge domains and modalities, providing a detailed analysis of the current landscape; (2) introduces a structured taxonomy that categorizes methods based on where and how reasoning is integrated into the retrieval pipeline, alongside an analysis of their trade-offs and practical applications; and (3) summarizes challenges and future directions to guide research in this evolving field.