# Implicit Reasoning in Large Language Models: A Comprehensive Survey

Authors: Jindong Li, Yali Fu, Li Fan, Jiahong Liu, Yao Shu, Chengwei Qin, Menglin Yang, Irwin King, Rex Ying.

Organizes implicit-reasoning research across model mechanisms, training methods, evaluation, and applications.

First public: 2025-09-02.
ArXiv initial submission: 2025-09-02.

[Original paper](https://arxiv.org/abs/2509.02350) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2509.02350/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2509.02350/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2509.02350/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2509.02350/record.json)
[Code](https://github.com/digailab/awesome-llm-implicit-reasoning)

## Approaches

- [Analysis and evaluation](https://latent-implicit-thinking.github.io/topics/implicit-reasoning-evaluation/): Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.

Summary and categories are editorial. Read the original paper to verify claims and limitations. A citation to a paper does not, by itself, establish that a later method builds on it.

Reading-list version 0.9.5; literature checked through 2026-10-18.