arXiv explained breakdown of this paper 👇 https://arxivexplained.com/papers/a-survey-on-latent-reasoning
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Reasoning","submittedOnDailyBy":{"_id":"63ff09f24852102d4871c19c","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63ff09f24852102d4871c19c/lyE3xemtZss3qebK5sEXw.png","isPro":false,"fullname":"Rui-Jie Zhu","user":"ridger","type":"user"},"summary":"Large Language Models (LLMs) have demonstrated impressive reasoning\ncapabilities, especially when guided by explicit chain-of-thought (CoT)\nreasoning that verbalizes intermediate steps. While CoT improves both\ninterpretability and accuracy, its dependence on natural language reasoning\nlimits the model's expressive bandwidth. Latent reasoning tackles this\nbottleneck by performing multi-step inference entirely in the model's\ncontinuous hidden state, eliminating token-level supervision. To advance latent\nreasoning research, this survey provides a comprehensive overview of the\nemerging field of latent reasoning. We begin by examining the foundational role\nof neural network layers as the computational substrate for reasoning,\nhighlighting how hierarchical representations support complex transformations.\nNext, we explore diverse latent reasoning methodologies, including\nactivation-based recurrence, hidden state propagation, and fine-tuning\nstrategies that compress or internalize explicit reasoning traces. Finally, we\ndiscuss advanced paradigms such as infinite-depth latent reasoning via masked\ndiffusion models, which enable globally consistent and reversible reasoning\nprocesses. By unifying these perspectives, we aim to clarify the conceptual\nlandscape of latent reasoning and chart future directions for research at the\nfrontier of LLM cognition. An associated GitHub repository collecting the\nlatest papers and repos is available at:\nhttps://github.com/multimodal-art-projection/LatentCoT-Horizon/.","upvotes":91,"discussionId":"686ddd7fcb5725779c60b465","githubRepo":"https://github.com/multimodal-art-projection/LatentCoT-Horizon/","ai_summary":"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.","ai_keywords":["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"],"githubStars":213},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"63ff09f24852102d4871c19c","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63ff09f24852102d4871c19c/lyE3xemtZss3qebK5sEXw.png","isPro":false,"fullname":"Rui-Jie Zhu","user":"ridger","type":"user"},{"_id":"63c9725ebedad7e2bf160bdc","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63c9725ebedad7e2bf160bdc/wzPuyhOXCYBNGwZDshbnL.jpeg","isPro":false,"fullname":"Mostafa Elhoushi","user":"melhoushi","type":"user"},{"_id":"646350107e9025b09bd62bab","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/646350107e9025b09bd62bab/TEOf1dZnZLE-4_-I6Eh-n.jpeg","isPro":false,"fullname":"momo","user":"wzc991222","type":"user"},{"_id":"638efcf4c67af472d316d424","avatarUrl":"/avatars/97a57859d7d87a3a8f1bb41d32a72bc2.svg","isPro":false,"fullname":"Ge Zhang","user":"zhangysk","type":"user"},{"_id":"65377c30e48353201e6fdda0","avatarUrl":"/avatars/a8f803b6f2e598eaee9c52c0d2ddfc16.svg","isPro":false,"fullname":"Jiaheng Liu","user":"CheeryLJH","type":"user"},{"_id":"64de37ee5e192985054be575","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64de37ee5e192985054be575/fVV7JQMtp_J3uFqszJJHH.jpeg","isPro":false,"fullname":"Yuansheng Ni","user":"yuanshengni","type":"user"},{"_id":"64ba096e760936217a3ad2e2","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64ba096e760936217a3ad2e2/aNQK83Jg5PsBkY0UDg-RA.jpeg","isPro":false,"fullname":"Linzheng Chai","user":"Challenging666","type":"user"},{"_id":"610b70452719facd4ea85e28","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/610b70452719facd4ea85e28/S7nMy7D0Rxq0VIVblhYDG.jpeg","isPro":false,"fullname":"Chujie Zheng","user":"chujiezheng","type":"user"},{"_id":"64d5eedc2fe2c11264080830","avatarUrl":"/avatars/80d2ba75038c59be6ab5dd703ce235c9.svg","isPro":false,"fullname":"Anonymous","user":"Tianhao-Peng","type":"user"},{"_id":"64b8cc7ebe76d2ff0703bfb3","avatarUrl":"/avatars/f1c7ff17fd923f1460d362333d9fbfe3.svg","isPro":false,"fullname":"yong","user":"yo37","type":"user"},{"_id":"64ab99dcb76bfd863eba64c1","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64ab99dcb76bfd863eba64c1/UBXwDPx17X-gl-SzBPvrc.jpeg","isPro":false,"fullname":"TY.Zheng","user":"aaabiao","type":"user"},{"_id":"658406044f6ed39dee01e2ce","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/658406044f6ed39dee01e2ce/k4s6qsG2VJ3_FITjWaT1M.jpeg","isPro":false,"fullname":"Jiawei Guo","user":"KerwinJob","type":"user"}],"acceptLanguages":["*"],"dailyPaperRank":2}">
Papers
arxiv:2507.06203
A Survey on Latent Reasoning
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.
AI-generated summary
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/.
Community
We've all seen LLMs "think out loud" with Chain-of-Thought, but what if they could reason without being limited to words? Our paper explores how models can perform complex, multi-step inference directly in their continuous hidden states, unlocking enormous expressive potential. In this work, we've synthesized the rapidly growing body of research to create the first clear taxonomy of the field. We dive into how models can be trained to "think deeper" (vertical recurrence) or "think longer" (horizontal recurrence) and explore how futuristic paradigms like text diffusion models enable globally consistent, infinite-step refinement.
arXiv explained breakdown of this paper 👇 https://arxivexplained.com/papers/a-survey-on-latent-reasoning
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