---
title: "A Survey of Reasoning-Intensive Retrieval: Progress and"
description: "Methods: they propose a taxonomy based on where reasoning enters the retrieval pipeline, and discuss trade-offs and use… 100 references."
authors: ["Yiyang Wei", "Tingyu Song", "Siyue Zhang", "Yilun Zhao"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1949/"
doi: "10.18653/v1/2026.acl-long.1949"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wei-2026-survey-reasoning-intensive-retrieval-progress/"
---
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# A Survey of Reasoning-Intensive Retrieval: Progress and Challenges

**Authors:** Yiyang Wei, [Tingyu Song](https://md.research.elamur.ai/free/en/authors/tingyu-song/), [Siyue Zhang](https://md.research.elamur.ai/free/en/authors/siyue-zhang/) and [Yilun Zhao](https://md.research.elamur.ai/free/en/authors/yilun-zhao/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1949/) · [PDF](https://aclanthology.org/2026.acl-long.1949.pdf) · [DOI 10.18653/v1/2026.acl-long.1949](https://doi.org/10.18653/v1/2026.acl-long.1949) · [OpenAlex](https://openalex.org/W7166852561)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary

This survey maps the emerging field of Reasoning-Intensive Retrieval (RIR). In RIR, a document counts as relevant because of an implicit chain of inference linking it to the query, not because the two share words or meaning. One example: finding text on how dissolved salt behaves when seawater is boiled, in answer to a question about whether boiled seawater is drinkable. The authors argue that RIR work is scattered across incompatible evaluation setups and pipeline stages. To bring order to it, they do three things:

- **Benchmarks:** they catalogue RIR benchmarks by knowledge domain, modality, size and annotation source.
- **Methods:** they propose a taxonomy based on where reasoning enters the retrieval pipeline, and discuss trade-offs and use cases.
- **Open problems:** they identify unresolved issues.

The survey's main contribution is this organizing framework and roadmap, not new experiments. It matters because it gives researchers a common frame for comparing systems built on LLM reasoning, and because it highlights deployment concerns such as cost and generalization that standard metrics overlook.

## Key points

- **Benchmark families:** open-domain (ImpliRet, 9,000; BESPOKE, 150), expert-domain (scientific, code, legal, medical; e.g., MIRB, FreshStack, CoIR ≈162,000, R2MED, CMIRB), multi-domain (BRIGHT and BRIGHT-Plus, 1,384 each; RAR-b) and multimodal (MRMR, MR2-BENCH, ARK, MM-BRIGHT).
- **Method taxonomy, four stages:**
  - Pre-retrieval: query rewriting or decomposition (e.g., ThinkQE, TongSearch-QR) and index enrichment (e.g., LATTICE).
  - Retriever training: covering base model, training data and objective/reward (e.g., ReasonIR, DIVER, O1-Embedder).
  - Reranking: via prompting, SFT/distillation or RL (e.g., Rank1, ReasonRank, Rank-R1).
  - Iterative: loops that alternate retrieval and reasoning.
- **Metric gaps:** nDCG and Recall overlook efficiency and cannot separate models that score alike but retrieve qualitatively different results.
- **Open challenges:**
  - Benchmarks remain far from everyday information needs.
  - Multimodal reasoning lags behind text-only reasoning.
  - Multi-stage pipelines are expensive at inference time; the authors point to latent reasoning, multi-vector models and adaptive routing as remedies.
  - Specialized RIR models often trail strong general-purpose embedders.
- **Scope limitation:** the survey mainly covers methods tested on established RIR benchmarks, so graph-based retrieval and HyDE get little attention.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) — Government body, Beijing, China · 194 works · 23,906 citations
- [University of Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/universities/university-of-chinese-academy-of-sciences/) — University, Beijing, China · 147 works · 8,480 citations

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## Cite

Cite as: Yiyang Wei, Tingyu Song, Siyue Zhang and Yilun Zhao. 2026. A Survey of Reasoning-Intensive Retrieval: Progress and Challenges. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wei2026survey,
  title = {{A Survey of Reasoning-Intensive Retrieval: Progress and Challenges}},
  author = {Yiyang Wei, Tingyu Song, Siyue Zhang and Yilun Zhao},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1949},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1949}
}
```

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*Updated 1 November 2026*