Large Language Models have demonstrated remarkable abilities across various tasks, with Chain-of-Thought (CoT) prompting emerging as a key technique to enhance reasoning capabilities. However, existing research primarily focuses on improving performance, lacking a comprehensive framework to explain and understand the fundamental factors behind CoT's success. To bridge this gap, we introduce a novel perspective grounded in the Hopfieldian view of cognition in cognitive neuroscience. We establish a connection between CoT reasoning and key cognitive elements such as stimuli, actions, neural populations, and representation spaces. From our view, we can understand the reasoning process as the movement between these representation spaces. Building on this insight, we develop a method for localizing reasoning errors in the response of CoTs. Moreover, we propose the Representation-of-Thought (RoT) framework, which leverages the robustness of low-dimensional representation spaces to enhance the robustness of the reasoning process in CoTs. Experimental results demonstrate that RoT improves the robustness and interpretability of CoT reasoning while offering fine-grained control over the reasoning process.

翻译：大型语言模型在各种任务中展现出卓越能力，其中思维链（CoT）提示已成为增强推理能力的关键技术。然而，现有研究主要聚焦于性能提升，缺乏能够解释和理解CoT成功背后根本因素的综合框架。为弥补这一空白，我们引入基于认知神经科学中霍普菲尔德认知观的新视角。我们建立了CoT推理与关键认知要素（如刺激、动作、神经群体和表征空间）之间的关联。从这一视角出发，我们可以将推理过程理解为这些表征空间之间的动态迁移。基于此洞见，我们开发了一种在CoT响应中定位推理错误的方法。此外，我们提出了思维表征（RoT）框架，该框架利用低维表征空间的鲁棒性来增强CoT推理过程的稳健性。实验结果表明，RoT在提升CoT推理鲁棒性与可解释性的同时，还能实现对推理过程的细粒度控制。