A Survey on Latent Reasoning
Rui-Jie Zhu, Tianhao Peng, Tianhao Cheng, Xingwei Qu, Jinfa Huang, Dawei Zhu, Hao Wang, Kaiwen Xue, Xuanliang Zhang, Yong Shan, Tianle Cai, Taylor Kergan, Assel Kembay, Andrew Smith, Chenghua Lin, Binh Nguyen, Yuqi Pan, Yuhong Chou, Zefan Cai, Zhenhe Wu, Yongchi Zhao, Tianyu Liu
2025-07-09
Summary
This paper talks about latent reasoning, a way that large language models think by performing multiple steps of inference inside their hidden states instead of expressing each step in words. It uses continuous, internal processing to improve reasoning without needing detailed step-by-step training data.
What's the problem?
The problem is that traditional methods for AI reasoning rely on models explaining their thinking step-by-step in words, which is inefficient and limits how deep or complex the reasoning can be. These methods also need special training data showing how to break down problems.
What's the solution?
The researchers studied techniques like activation-based recurrence and infinite-depth reasoning with masked diffusion models that let the AI process information repeatedly inside its hidden layers. This approach allows more flexible and powerful reasoning internally before producing an answer.
Why it matters?
This matters because latent reasoning offers a way to make AI models reason more deeply and efficiently, helping them solve complex problems better without extra human guidance, which could lead to smarter AI applications across many fields.
Abstract
Latent reasoning in Large Language Models (LLMs) performs multi-step inference in continuous hidden states, enhancing reasoning capabilities without token-level supervision, and includes methodologies like activation-based recurrence and infinite-depth reasoning via masked diffusion models.