Information retrieval has long focused on ranking documents by semantic relatedness. Yet many real-world information needs demand more: enforcement of logical constraints, multi-step inference, and synthesis of multiple pieces of evidence. Addressing these requirements is, at its core, a problem of reasoning. Across AI communities, researchers are developing diverse solutions for the problem of reasoning, from inference-time strategies and post-training of LLMs, to neuro-symbolic systems, Bayesian and probabilistic frameworks, geometric representations, and energy-based models. These efforts target the same problem: to move beyond pattern-matching systems toward structured, verifiable inference. However, they remain scattered across disciplines, making it difficult for IR researchers to identify the most relevant ideas and opportunities. To help navigate the fragmented landscape of research in reasoning, this tutorial first articulates a working definition of reasoning within the context of information retrieval and derives from it a unified analytical framework. The framework maps existing approaches along axes that reflect the core components of the definition. By providing a comprehensive overview of recent approaches and mapping current methods onto the defined axes, we expose their trade-offs and complementarities, highlight where IR can benefit from cross-disciplinary advances, and illustrate how retrieval process itself can play a central role in broader reasoning systems. The tutorial will equip participants with both a conceptual framework and practical guidance for enhancing reasoning-capable IR systems, while situating IR as a domain that both benefits and contributes to the broader development of reasoning methodologies.

翻译：长期以来，信息检索主要关注依据语义相关性对文档进行排序。然而，许多现实世界的信息需求要求更高：逻辑约束的执行、多步推理以及多重证据的综合。从根本上说，满足这些需求是一个推理问题。在各个AI研究社区中，研究人员正在为推理问题开发多样化的解决方案，从LLM的推理时策略与后训练，到神经符号系统、贝叶斯与概率框架、几何表示以及基于能量的模型。这些努力针对同一个问题：超越模式匹配系统，迈向结构化、可验证的推理。然而，这些研究分散在各个学科中，使得信息检索研究人员难以识别最相关的思路和机遇。为了帮助梳理推理研究中这一碎片化的格局，本教程首先在信息检索的语境下阐明一个可操作的推理定义，并由此推导出一个统一的分析框架。该框架依据定义的核心维度，对现有方法进行映射。通过全面概述近期方法并将现有技术映射到定义的维度上，我们揭示了它们之间的权衡与互补性，强调了信息检索可以从跨学科进展中获益的领域，并阐释了检索过程本身如何在更广泛的推理系统中发挥核心作用。本教程将为参与者提供概念框架和实践指导，以增强具备推理能力的信息检索系统，同时将信息检索定位为一个既受益于、又能贡献于更广泛推理方法发展的领域。