Tutorial on Reasoning for IR & IR for Reasoning
Mohanna Hoveyda, Panagiotis Efstratiadis, Arjen de Vries, Maarten de Rijke

TL;DR
This tutorial reviews diverse reasoning approaches in information retrieval, providing a unified framework to help IR researchers leverage cross-disciplinary advances for more structured and verifiable inference.
Contribution
It introduces a unified analytical framework for reasoning in IR, mapping recent approaches onto core components to clarify trade-offs and opportunities for integration.
Findings
Mapping approaches reveals their trade-offs and complementarities.
IR can benefit from cross-disciplinary reasoning advances.
Retrieval processes can play a central role in reasoning systems.
Abstract
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…
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Taxonomy
TopicsInformation Retrieval and Search Behavior · Biomedical Text Mining and Ontologies · Text and Document Classification Technologies
