梳理推理密集型检索的进展与挑战，帮研究者理清方向。
A Survey of Reasoning-Intensive Retrieval: Progress and Challenges
- 按知识领域和模态分类现有评测数据集
- 提出方法集成位置与方式的系统分类框架
- 指出现有难题与未来研究方向，适合该领域新手入门
推理密集型检索（RIR）关注的是查询与支持证据之间通过隐含推理关系而非语义相似性来判定相关性的检索场景。受大语言模型（LLMs）涌现推理能力的启发，近期工作将这些能力融入信息检索全流程，涵盖评测基准、检索器与重排序器。尽管取得进展，该领域仍缺乏系统框架以整合现有成果并明确发展路径。本综述（1）基于知识领域与模态对现有RIR评测基准进行系统分类与分析；（2）提出结构化分类体系，按推理在检索流程中的集成位置与方式对方法分类，并分析其权衡与实际应用；（3）总结当前挑战与未来方向，为该快速发展的领域提供清晰研究路线图。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。