mlsanityData to 7 Oct 2026

Tutorial on Reasoning for IR & IR for Reasoning

arXiv 2602.03640 ↗First posted 3 Feb 2026cs.IR cs.AI cs.CLcited by 0

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 39 of its 60 references that are in the map. First posted on arXiv 3 Feb 2026.

Builds on

39 of 60 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning12 Mar
  2. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions20 Dec

Other references · 37

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  4. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  5. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  6. 2025Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?cited by 948
  7. 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 743
  8. 2022Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimizationcited by 244
  9. 2024Chain-of-Thought Reasoning Without Promptingcited by 208
  10. 2025BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agentcited by 183
  11. 2023LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Proverscited by 161
  12. 2024BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrievalcited by 153
25 more
  1. 2023From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thoughtcited by 118
  2. 2025On the Theoretical Limitations of Embedding-Based Retrievalcited by 98
  3. 2023Training Chain-of-Thought via Latent-Variable Inferencecited by 46
  4. 2024Efficiently Learning at Test-Time: Active Fine-Tuning of LLMscited by 42
  5. 2025Implicit Reasoning in Large Language Models: A Comprehensive Surveycited by 42
  6. 2024Procedural Knowledge in Pretraining Drives Reasoning in Large Language Modelscited by 41
  7. 2024Learning Iterative Reasoning through Energy Diffusioncited by 36
  8. 2025Bayesian Teaching Enables Probabilistic Reasoning in Large Language Modelscited by 36
  9. 2023QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operationscited by 30
  10. 2025Energy-Based Transformers are Scalable Learners and Thinkerscited by 30
  11. 2023NevIR: Negation in Neural Information Retrievalcited by 28
  12. 2024What Are the Odds? Language Models Are Capable of Probabilistic Reasoningcited by 25
  13. 2024Fundamental Problems With Model Editing: How Should Rational Belief Revision Work in LLMs?cited by 24
  14. 2024Hyperbolic Fine-Tuning for Large Language Modelscited by 24
  15. 2025Hyperbolic Deep Learning for Foundation Models: A Surveycited by 20
  16. 2024BIRD: A Trustworthy Bayesian Inference Framework for Large Language Modelscited by 18
  17. 2025Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Modelscited by 14
  18. 2021Box Embeddings: An open-source library for representation learning using geometric structurescited by 9
  19. 2025Reproducing NevIR: Negation in Neural Information Retrievalcited by 7
  20. 2025Benchmarking Information Retrieval Models on Complex Retrieval Taskscited by 6
  21. 2024Hitchhiker's guide on the relation of Energy-Based Models with other generative models, sampling and statistical physics: a comprehensive reviewcited by 5
  22. 2024SetBERT: Enhancing Retrieval Performance for Boolean Logic and Set Operation Queriescited by 5
  23. 2025Constructing Set-Compositional and Negated Representations for First-Stage Rankingcited by 5
  24. 2025A Comprehensive Taxonomy of Negation for NLP and Neural Retrieverscited by 4
  25. 2024ExcluIR: Exclusionary Neural Information Retrievalcited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026A Survey of Reasoning-Intensive Retrieval: Progress and Challenges
  2. 2025DIVER: A Multi-Stage Approach for Reasoning-intensive Information Retrieval
  3. 2026OrLog: Resolving Complex Queries with LLMs and Probabilistic Reasoning
  4. 2026When to Retrieve During Reasoning: Adaptive Retrieval for Large Reasoning Models
  5. 2026Multi-Step Semantic Reasoning in Generative Retrieval
  6. 2025Enhancing Retrieval Systems with Inference-Time Logical Reasoning
  7. 2024RAR-b: Reasoning as Retrieval Benchmark
  8. 2026The Magnitude Mirage: Rethinking Confidence for Reasoning-Intensive Retrieval