mlsanityData to 14 Oct 2026

A Survey of Inductive Reasoning for Large Language Models

arXiv 2510.10182 ↗First posted 11 Oct 2025cs.CL cs.AIcited by 8

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Reasoning is an important task for large language models (LLMs). Among all the reasoning paradigms, inductive reasoning is one of the fundamental types, which is characterized by its particular-to-general thinking process and the non-uniqueness of its answers. The inductive mode is crucial for knowledge generalization and aligns better with human cognition, so it is a fundamental mode of learning, hence attracting increasing interest. Despite the importance of inductive reasoning, there is no systematic summary of it. Therefore, this paper presents the first comprehensive survey of inductive reasoning for LLMs. First, methods for improving inductive reasoning are categorized into three main areas: post-training, test-time scaling, and data augmentation. Then, current benchmarks of inductive reasoning are summarized, and a unified sandbox-based evaluation approach with the observation coverage metric is derived. Finally, we offer some analyses regarding the source of inductive ability and how simple model architectures and data help with inductive tasks, providing a solid foundation for future research.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 77 of its 222 references that are in the map. First posted on arXiv 11 Oct 2025.

Builds on

77 of 222 references.

Closest ancestors · 11

Same direction, newest first

  1. 2025From Reasoning to Learning: A Survey on Hypothesis Discovery and Rule Learning with Large Language Models28 May
  2. 2025Qwen3 Technical Report14 May
  3. 2025Code-Driven Inductive Synthesis: Enhancing Reasoning Abilities of Large Language Models with Sequences17 Mar
  4. 2024DeepSeek-V3 Technical Report27 Dec
  5. 2024Evaluating the Robustness of Analogical Reasoning in Large Language Models21 Nov
  6. 2024Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs31 Jul
  7. 2024Multi-Step Reasoning with Large Language Models, a Survey16 Jul
  8. 2024Using Counterfactual Tasks to Evaluate the Generality of Analogical Reasoning in Large Language Models14 Feb
  9. 2023Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement12 Oct
  10. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity8 Feb
  11. 2022Towards Reasoning in Large Language Models: A Survey20 Dec

Other references · 66

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2023A Survey of Large Language Modelscited by 2,576
  4. 2024A Survey on LLM-as-a-Judgecited by 1,423
  5. 2022A Survey on In-context Learningcited by 721
  6. 2025Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Modelscited by 384
  7. 2020Inductive Biases for Deep Learning of Higher-Level Cognitioncited by 275
  8. 2018Meta-Learning: A Surveycited by 256
  9. 2023A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Modelscited by 246
  10. 2024On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Surveycited by 244
  11. 2025A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?cited by 223
  12. 2017Learning Explanatory Rules from Noisy Datacited by 211
38 more
  1. 2023Large Language Models for Generative Information Extraction: A Surveycited by 185
  2. 2025A Survey of Reinforcement Learning for Large Reasoning Modelscited by 180
  3. 2022A Review on Language Models as Knowledge Basescited by 157
  4. 2022Instruction Induction: From Few Examples to Natural Language Task Descriptionscited by 152
  5. 2019On the Measure of Intelligencecited by 144
  6. 2024A Minimaximalist Approach to Reinforcement Learning from Human Feedbackcited by 144
  7. 2021AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processingcited by 131
  8. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 124
  9. 2025Inductive Moment Matchingcited by 113
  10. 2024Towards Conversational Diagnostic AIcited by 79
  11. 2024Comprehensive Exploration of Synthetic Data Generation: A Surveycited by 60
  12. 2025LLM Agents for Education: Advances and Applicationscited by 59
  13. 2023Large Language Models in Education: Vision and Opportunitiescited by 54
  14. 2025A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Futurecited by 53
  15. 2025Intern-S1: A Scientific Multimodal Foundation Modelcited by 53
  16. 2021LIME: Learning Inductive Bias for Primitives of Mathematical Reasoningcited by 46
  17. 2023Large Language Models for Telecom: Forthcoming Impact on the Industrycited by 44
  18. 2024MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Codecited by 39
  19. 2023Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RLcited by 37
  20. 2021Knowledge Enhanced Pretrained Language Models: A Compreshensive Surveycited by 33
  21. 2025GUI-Shepherd: Reliable Process Reward and Verification for Long-Sequence GUI Taskscited by 26
  22. 2022Fast Rates for Noisy Interpolation Require Rethinking the Effects of Inductive Biascited by 22
  23. 2024IDEA: Enhancing the Rule Learning Ability of Large Language Model Agent through Induction, Deduction, and Abductioncited by 22
  24. 2025DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experiencecited by 22
  25. 2025InternBootcamp: Boosting LLM Reasoning with Verifiable Task Scalingcited by 20
  26. 2017Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networkscited by 19
  27. 2025Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Testcited by 18
  28. 2025ToolFuzz -- Automated Agent Tool Testingcited by 18
  29. 2025Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Bettercited by 17
  30. 2025CodeARC: Benchmarking Reasoning Capabilities of LLM Agents for Inductive Program Synthesiscited by 16
  31. 2024DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Modelscited by 15
  32. 2024Understanding the Interplay between Parametric and Contextual Knowledge for Large Language Modelscited by 15
  33. 2025Language Models for Code Optimization: Survey, Challenges and Future Directionscited by 15
  34. 2025Sailing by the Stars: A Survey on Reward Models and Learning Strategies for Learning from Rewardscited by 14
  35. 2025AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learningcited by 14
  36. 2025Graph Neural Networks in Modern AI-aided Drug Discoverycited by 12
  37. 2025Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit Emergencecited by 10
  38. 2024ItD: Large Language Models Can Teach Themselves Induction through Deductioncited by 8

50 most cited of 66 All 66 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 3.

8 papers.

  1. 2025Information Gain-based Policy Optimization: A Simple and Effective Approach for Multi-Turn Search Agentscited by 48
  2. 2025A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networkscited by 4
  3. 2026Beyond Accuracy: Unveiling Inefficiency Patterns in Tool-Integrated Reasoningcited by 3
  4. 2025Code-driven Number Sequence Calculation: Enhancing the inductive Reasoning Abilities of Large Language Modelscited by 2
  5. 2025Facial-R1: Aligning Reasoning and Recognition for Facial Emotion Analysiscited by 2
  6. 2025Dual Attention Guided Defense Against Malicious Editscited by 1
  7. 2026Rethinking Multiple-Choice Questions for RLVR: Unlocking Potential via Distractor Designcited by 1
  8. 2026Dual-Vocabulary Language Model for Cross-Tokenizer Distillationcited by 0

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  5. 2025Do Large Language Models Excel in Complex Logical Reasoning with Formal Language?
  6. 2025Advancing Reasoning in Large Language Models: Promising Methods and Approaches
  7. 2024On the Role of Model Prior in Real-World Inductive Reasoning
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