超越语言的推理：潜在思维链推理综合综述
Reasoning Beyond Language: A Comprehensive Survey on Latent Chain-of-Thought Reasoning
AI总结：
本文综述潜在思维链推理这一新兴LLM推理范式，建立系统分类，分析其方法、设计原则、应用与挑战，为该领域发展提供结构化基础。
摘要：
大型语言模型（LLMs）通过思维链（CoT）推理在复杂任务上展现出令人印象深刻的性能，但传统CoT依赖于显式表述的中间步骤，限制了其更广泛的适用性，尤其在超越语言的抽象推理任务中。为解决该问题，越来越多研究关注潜在CoT推理，其推理过程嵌入在隐空间中，通过将推理与显式语言生成解耦，有望提供更丰富的认知表征，并实现更灵活、快速的推理。本文旨在对这一新兴范式进行全面概述并建立系统分类，分析近期方法进展，将其从令牌级水平方法到层级垂直策略进行分类，随后深入讨论这些方法，强调其设计原则、应用及剩余挑战。希望本文的综述为推进LLM推理这一有前景方向提供结构化基础，相关论文将定期更新于该httpsURL。
英文摘要：
Large Language Models (LLMs) have shown impressive performance on complex tasks through Chain-of-Thought (CoT) reasoning. However, conventional CoT relies on explicitly verbalized intermediate steps, which constrains its broader applicability, particularly in abstract reasoning tasks beyond language. To address this, there has been growing research interest in \textit{latent CoT reasoning}, where the reasoning process is embedded within latent spaces. By decoupling reasoning from explicit language generation, latent CoT offers the promise of richer cognitive representations and facilitates more flexible, faster inference. This paper aims to present a comprehensive overview of this emerging paradigm and establish a systematic taxonomy. We analyze recent advances in methods, categorizing them from token-wise horizontal approaches to layer-wise vertical strategies. We then provide in-depth discussions of these methods, highlighting their design principles, applications, and remaining challenges. We hope that our survey provides a structured foundation for advancing this promising direction in LLM reasoning. The relevant papers will be regularly updated at https://github.com/EIT-NLP/Awesome-Latent-CoT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
