@inproceedings{wei-etal-2026-survey,
title = "A Survey of Reasoning-Intensive Retrieval: Progress and Challenges",
author = "Wei, Yiyang and
Song, Tingyu and
Zhang, Siyue and
Zhao, Yilun",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1949/",
pages = "42101--42122",
ISBN = "979-8-89176-390-6",
abstract = "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."
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%0 Conference Proceedings
%T A Survey of Reasoning-Intensive Retrieval: Progress and Challenges
%A Wei, Yiyang
%A Song, Tingyu
%A Zhang, Siyue
%A Zhao, Yilun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F wei-etal-2026-survey
%X 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.
%U https://aclanthology.org/2026.acl-long.1949/
%P 42101-42122
Markdown (Informal)
[A Survey of Reasoning-Intensive Retrieval: Progress and Challenges](https://aclanthology.org/2026.acl-long.1949/) (Wei et al., ACL 2026)
ACL
- Yiyang Wei, Tingyu Song, Siyue Zhang, and Yilun Zhao. 2026. A Survey of Reasoning-Intensive Retrieval: Progress and Challenges. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 42101–42122, San Diego, California, United States. Association for Computational Linguistics.