---
title: "A Survey of Inductive Reasoning for Large Language Models"
description: "The authors present what they describe as the first comprehensive survey of this area. 223 references, cited by 8 papers here."
authors: ["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", "Wei Zhang"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1447/"
doi: "10.18653/v1/2026.acl-long.1447"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/chen-2026-survey-inductive-reasoning-large-language/"
---
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# A Survey of Inductive Reasoning for Large Language Models

**Authors:** Kedi Chen, Dezhao Ruan, [Yuhao Dan](https://md.research.elamur.ai/free/en/authors/yuhao-dan/), Yaoting Wang, Siyu Yan, [Xuecheng Wu](https://md.research.elamur.ai/free/en/authors/xuecheng-wu/), Yinqi Zhang, [Qin Chen](https://md.research.elamur.ai/free/en/authors/qin-chen/), [Jie Zhou](https://md.research.elamur.ai/free/en/authors/jie-zhou/), [Liang He](https://md.research.elamur.ai/free/en/authors/liang-he/), [Biqing Qi](https://md.research.elamur.ai/free/en/authors/biqing-qi/), [Linyang Li](https://md.research.elamur.ai/free/en/authors/linyang-li/), [Qipeng Guo](https://md.research.elamur.ai/free/en/authors/qipeng-guo/), [Xiaoming Shi](https://md.research.elamur.ai/free/en/authors/xiaoming-shi/) and [Wei Zhang](https://md.research.elamur.ai/free/en/authors/wei-zhang/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1447/) · [PDF](https://aclanthology.org/2026.acl-long.1447.pdf) · [DOI 10.18653/v1/2026.acl-long.1447](https://doi.org/10.18653/v1/2026.acl-long.1447) · [OpenAlex](https://openalex.org/W4417100126)

**Citations:** 2 citations (OpenAlex, 2026-10-14) · [8](https://md.research.elamur.ai/free/en/papers/chen-2026-survey-inductive-reasoning-large-language/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## 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 University](https://md.research.elamur.ai/free/en/universities/fudan-university/) — University, Shanghai, China · 167 works · 20,987 citations
- [Shanghai Artificial Intelligence Laboratory](https://md.research.elamur.ai/free/en/labs/shanghai-artificial-intelligence-laboratory/) — Research institute, Shanghai, China · 117 works · 11,281 citations
- [East China Normal University](https://md.research.elamur.ai/free/en/universities/east-china-normal-university/) — University, Shanghai, China · 50 works · 2,174 citations
- [Xi'an Jiaotong University](https://md.research.elamur.ai/free/en/universities/xi-an-jiaotong-university/) — University, Xi'an, China · 22 works · 36,046 citations
- [Shanghai Innovation Institute](https://md.research.elamur.ai/free/en/universities/shanghai-innovation-institute/) — University, Shanghai, China · 24 works · 262 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works

🧭 Topics (2): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,320 works · [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 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.

```bibtex
@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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## References

223 references, 32 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*