A Survey on Latent Reasoning
Large Language Models (LLMs) have demonstrated impressive reasoning
capabilities, especially when guided by explicit chain-of-thought (CoT)
reasoning that verbalizes intermediate steps. While CoT improves both
interpretability and accuracy, its dependence on natural language reasoning
limits the model's expressive bandwidth. Latent reasoning tackles this
bottleneck by performing multi-step inference entirely in the model's
continuous hidden state, eliminating token-level supervision. To advance latent
reasoning research, this survey provides a comprehensive overview of the
emerging field of latent reasoning. We begin by examining the foundational role
of neural network layers as the computational substrate for reasoning,
highlighting how hierarchical representations support complex transformations.
Next, we explore diverse latent reasoning methodologies, including
activation-based recurrence, hidden state propagation, and fine-tuning
strategies that compress or internalize explicit reasoning traces. Finally, we
discuss advanced paradigms such as infinite-depth latent reasoning via masked
diffusion models, which enable globally consistent and reversible reasoning
processes. By unifying these perspectives, we aim to clarify the conceptual
landscape of latent reasoning and chart future directions for research at the
frontier of LLM cognition. An associated GitHub repository collecting the
latest papers and repos is available at:
https://github.com/multimodal-art-projection/LatentCoT-Horizon/.
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Rui-Jie Zhu
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2025-07-09T08:49:55.890Z
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Rui-Jie Zhu
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ridger
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Tianhao Peng
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Tianhao Cheng
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Xingwei Qu
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Jinfa Huang
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2025-07-09T08:49:54.002Z
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Huang
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Jinfa
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Dawei Zhu
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Hao Wang
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Kaiwen Xue
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Xuanliang Zhang
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Yong Shan
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2025-07-15T19:11:20.842Z
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yong
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yo37
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Tianle Cai
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Taylor Kergan
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Assel Kembay
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Andrew Smith
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Chenghua Lin
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Binh Nguyen
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Yuqi Pan
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Yuhong Chou
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Zefan Cai
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Zhenhe Wu
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Yongchi Zhao
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Tianyu Liu
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Jian Yang
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Wangchunshu Zhou
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2025-07-29T12:51:16.465Z
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Zhou
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Chujie Zheng
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2025-07-09T08:49:58.017Z
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Chujie Zheng
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Chongxuan Li
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Yuyin Zhou
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Zhoujun Li
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Zhaoxiang Zhang
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Jiaheng Liu
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Ge Zhang
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2025-07-22T14:00:55.925Z
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Ge Zhang
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Wenhao Huang
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Jason Eshraghian
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2025-07-09T08:49:47.907Z
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Jason
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jeshragh
A Survey on Latent Reasoning
Large Language Models (LLMs) have demonstrated impressive reasoning
capabilities, especially when guided by explicit chain-of-thought (CoT)
reasoning that verbalizes intermediate steps. While CoT improves both
interpretability and accuracy, its dependence on natural language reasoning
limits the model's expressive bandwidth. Latent reasoning tackles this
bottleneck by performing multi-step inference entirely in the model's
continuous hidden state, eliminating token-level supervision. To advance latent
reasoning research, this survey provides a comprehensive overview of the
emerging field of latent reasoning. We begin by examining the foundational role
of neural network layers as the computational substrate for reasoning,
highlighting how hierarchical representations support complex transformations.
Next, we explore diverse latent reasoning methodologies, including
activation-based recurrence, hidden state propagation, and fine-tuning
strategies that compress or internalize explicit reasoning traces. Finally, we
discuss advanced paradigms such as infinite-depth latent reasoning via masked
diffusion models, which enable globally consistent and reversible reasoning
processes. By unifying these perspectives, we aim to clarify the conceptual
landscape of latent reasoning and chart future directions for research at the
frontier of LLM cognition. An associated GitHub repository collecting the
latest papers and repos is available at:
https://github.com/multimodal-art-projection/LatentCoT-Horizon/.
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.
chain-of-thought (CoT) latent reasoning neural network layers hierarchical representations activation-based recurrence hidden state propagation fine-tuning strategies infinite-depth latent reasoning masked diffusion models