A Survey of Reasoning-Intensive Retrieval: Progress and Challenges

A comprehensive survey systematizes Reasoning-Intensive Retrieval (RIR), a rapidly emerging field that integrates Large Language Model reasoning capabilities into information retrieval systems. The study provides the first structured framework organizing RIR benchmarks, methods, and taxonomies to guide future research in this fragmented but high-growth area.

This survey addresses a critical gap in academic infrastructure by formally systematizing Reasoning-Intensive Retrieval, a field that has grown organically around LLM capabilities but lacked coherent organization. RIR represents a paradigm shift in information retrieval from pure semantic matching to reasoning-based relevance determination, where systems must infer connections between queries and evidence through multiple logical steps. This matters because traditional IR systems struggle with complex queries requiring multi-hop reasoning, while modern LLMs excel at inferential tasks, creating an intersection ripe for methodological innovation.

The academic landscape has fragmented as researchers independently developed benchmarks, retrievers, and reranking approaches without common frameworks. This survey consolidates these efforts by categorizing benchmarks across knowledge domains and modalities while introducing a taxonomy that maps where reasoning integrates into retrieval pipelines. Understanding these trade-offs helps developers select appropriate approaches for specific applications, from question-answering to scientific literature discovery.

For the broader AI development community, this systematization accelerates progress by reducing redundancy and clarifying frontier challenges. Practitioners building retrieval systems can now reference established benchmarks and understand performance characteristics of different reasoning integration strategies. The survey's identification of open challenges—including computational efficiency, reasoning transparency, and domain adaptation—establishes research priorities that will shape next-generation IR systems.

Looking forward, the field will likely see increased focus on efficient reasoning mechanisms that balance performance with computational cost, standardized evaluation metrics across domains, and hybrid approaches combining dense retrieval with reasoning modules. This survey provides essential orientation as RIR matures from experimental work into production-grade infrastructure.

• →RIR represents a fundamental shift from semantic similarity to inferential reasoning in information retrieval systems.
• →The survey establishes the first systematic taxonomy organizing RIR methods, benchmarks, and evaluation approaches.
• →Computational efficiency and reasoning transparency emerge as critical challenges for practical RIR deployment.
• →LLM integration into retrieval pipelines spans multiple stages with distinct trade-offs between accuracy and cost.
• →Standardized benchmarks across knowledge domains are essential for comparing and advancing RIR methods.