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A Survey of Inductive Reasoning for Large Language Models

Paper · 2026 · Kedi Chen, Dezhao Ruan, Yuhao Dan et al.

Summary

Inductive reasoning moves from particular observations to general rules, and it often admits several valid answers. It has received far less systematic attention for LLMs than deductive tasks such as mathematical proof or program verification. The authors present what they describe as the first comprehensive survey of this area. They organize enhancement techniques into three families: post-training enhancement, test-time exploration, and data augmentation. They also catalogue existing benchmarks and evaluation strategies.

Their main proposal is a unified sandbox-based evaluation scheme. A rule induced by a model is turned into code, a tool, or an LLM-as-a-Judge prompt, and each observation is then checked against it as a unit test. This produces an observation coverage (OC) metric, which gives a finer, per-observation signal than overall accuracy and can guide later rule refinement.

The survey closes with analyses of where inductive ability comes from and why simpler architectures and cleaner data tend to help. Together these give researchers a structured map of the field.

Key points

  • Taxonomy of methods:
    • Post-training enhancement covers synthetic data (LingR, ItD, CodeSeq) and IRL-style optimization (e.g., Prompt-OIRL).
    • Test-time exploration covers hypothesis selection, iteration (ARISE, SSR, IDEA), and evolution (IncSchema, PRIMO).
    • Data augmentation covers human intervention, external knowledge retrieval, and structured signals.
  • Benchmarks reviewed include ARC, List Functions, ILP, DEER, ACRE, SCAN and SyGuS.
  • Observation coverage (OC) is the fraction of observations that pass sandbox unit tests under an induced rule. In the paper's illustrative example, OC equals 0.6.
  • Analysis findings:
    • Induction heads, which are attention heads performing match-and-copy, are linked to in-context learning ability.
    • Parameters, architecture and training data jointly shape inductive bias.
    • Simplicity and clean corpora tend to favor inductive generalization.
  • Paper list: a curated list is maintained at github.com/BDML-lab/llm-inductive-reasoning-survey.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Fudan UniversityShanghai · China167 works · 20,987 citations🔬Shanghai Artificial Intelligence LaboratoryShanghai · China117 works · 11,281 citations🎓East China Normal UniversityShanghai · China50 works · 2,174 citations🎓Xi'an Jiaotong UniversityXi'an · China22 works · 36,046 citations🎓Shanghai Innovation InstituteShanghai · China24 works · 262 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksReasoning · 1,426 works

Cite

Cite as Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang, Siyu Yan, Xuecheng Wu, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Biqing Qi, Linyang Li, Qipeng Guo, Xiaoming Shi and Wei Zhang. 2026. A Survey of Inductive Reasoning for Large Language Models. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1447

@inproceedings{chen2026survey,
  title = {{A Survey of Inductive Reasoning for Large Language Models}},
  author = {Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang, Siyu Yan, Xuecheng Wu, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Biqing Qi, Linyang Li, Qipeng Guo, Xiaoming Shi and Wei Zhang},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1447},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1447}
}

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