A Survey of Reasoning-Intensive Retrieval: Progress and Challenges
For researchers in information retrieval and NLP, this survey offers a structured overview of a fragmented field, but it is a survey and thus incremental in nature.
This survey organizes the emerging field of Reasoning-Intensive Retrieval (RIR), which focuses on retrieval tasks requiring inferential links beyond semantic similarity, by providing a taxonomy of methods and analysis of benchmarks. It aims to provide a roadmap for future research in this area.
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.