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/.
686ddd7fcb5725779c60b444
Rui-Jie Zhu
claimed_verified
2025-07-09T08:49:55.890Z
63ff09f24852102d4871c19c
https://cdn-avatars.huggingface.co/v1/production/uploads/63ff09f24852102d4871c19c/lyE3xemtZss3qebK5sEXw.png
Rui-Jie Zhu
user
ridger
686ddd7fcb5725779c60b445
Tianhao Peng
686ddd7fcb5725779c60b446
Tianhao Cheng
686ddd7fcb5725779c60b447
Xingwei Qu
686ddd7fcb5725779c60b448
Jinfa Huang
claimed_verified
2025-07-09T08:49:54.002Z
63f37af60be81bdc5d92eebb
/avatars/b8dfdff4ab36988ec9a8643e82a3d2db.svg
Huang
user
Jinfa
686ddd7fcb5725779c60b449
Dawei Zhu
686ddd7fcb5725779c60b44a
Hao Wang
686ddd7fcb5725779c60b44b
Kaiwen Xue
686ddd7fcb5725779c60b44c
Xuanliang Zhang
686ddd7fcb5725779c60b44d
Yong Shan
claimed_verified
2025-07-15T19:11:20.842Z
64b8cc7ebe76d2ff0703bfb3
/avatars/f1c7ff17fd923f1460d362333d9fbfe3.svg
sh
user
yo37
686ddd7fcb5725779c60b44e
Tianle Cai
686ddd7fcb5725779c60b44f
Taylor Kergan
686ddd7fcb5725779c60b450
Assel Kembay
686ddd7fcb5725779c60b451
Andrew Smith
686ddd7fcb5725779c60b452
Chenghua Lin
686ddd7fcb5725779c60b453
Binh Nguyen
686ddd7fcb5725779c60b454
Yuqi Pan
686ddd7fcb5725779c60b455
Yuhong Chou
686ddd7fcb5725779c60b456
Zefan Cai
686ddd7fcb5725779c60b457
Zhenhe Wu
686ddd7fcb5725779c60b458
Yongchi Zhao
686ddd7fcb5725779c60b459
Tianyu Liu
686ddd7fcb5725779c60b45a
Jian Yang
686ddd7fcb5725779c60b45b
Wangchunshu Zhou
claimed_verified
2025-07-29T12:51:16.465Z
628c8598ef14f971b698107f
/avatars/3a4ad87e6b5f9e836a1160d869df1447.svg
Zhou
user
Wangchunshu
686ddd7fcb5725779c60b45c
Chujie Zheng
claimed_verified
2025-07-09T08:49:58.017Z
610b70452719facd4ea85e28
https://cdn-avatars.huggingface.co/v1/production/uploads/610b70452719facd4ea85e28/S7nMy7D0Rxq0VIVblhYDG.jpeg
Chujie Zheng
user
chujiezheng
686ddd7fcb5725779c60b45d
Chongxuan Li
686ddd7fcb5725779c60b45e
Yuyin Zhou
686ddd7fcb5725779c60b45f
Zhoujun Li
686ddd7fcb5725779c60b460
Zhaoxiang Zhang
686ddd7fcb5725779c60b461
Jiaheng Liu
686ddd7fcb5725779c60b462
Ge Zhang
claimed_verified
2025-07-22T14:00:55.925Z
638efcf4c67af472d316d424
/avatars/97a57859d7d87a3a8f1bb41d32a72bc2.svg
Ge Zhang
user
zhangysk
686ddd7fcb5725779c60b463
Wenhao Huang
686ddd7fcb5725779c60b464
Jason Eshraghian
claimed_verified
2025-07-09T08:49:47.907Z
63047063bad6ce7fc02438c1
/avatars/8729cccbb15da682458d323eb8dc528b.svg
Jason
user
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