Recent advances in large language models (LLMs) have popularized the chain-of-thought (CoT) paradigm, in which models produce explicit reasoning steps in natural language. Although this approach improves interpretability and facilitates external auditing, it may not represent the most computationally efficient method for internal reasoning. In contrast, human cognition relies on implicit mental representations that recall past sensory and episodic information without requiring complete verbalization. In this paper, we propose a framework that integrates implicit mental representations into the internal reasoning processes of LLMs. Preliminary experiments indicate that incorporating an Implicit Memory Module (IMM) into a simple GPT model yields a reduction of between 35% and 57% in final training loss compared to a regular GPT baseline. The addition of an explicit interpretability channel (e.g., a chain-of-thought decoder) is straightforward to implement within this approach. We outline theoretical foundations, propose technical mechanisms to scale the memory module, and discuss how these ideas may lead to more efficient and robust reasoning, with optional future extensions for explicit auditability.

翻译：近年来，大语言模型（LLMs）的进展使得思维链（CoT）范式得到广泛采用，该范式要求模型生成自然语言形式的显式推理步骤。尽管这种方法提升了可解释性并便于外部审计，但其可能并非计算效率最高的内部推理方式。相比之下，人类认知依赖于隐式心理表征，能够在不需完整言语化的条件下回忆过往的感知与情景信息。本文提出一种将隐式心理表征整合到大语言模型内部推理过程的框架。初步实验表明，在基础GPT模型中引入隐式记忆模块（IMM）后，相较于标准GPT基线，最终训练损失降低了35%至57%。在此框架中，添加显式可解释性通道（例如思维链解码器）的实现方式较为直接。我们阐述了理论基础，提出了扩展记忆模块的技术机制，并探讨了这些理念如何导向更高效、更稳健的推理能力，同时为未来实现可选的显式审计功能提供了扩展方向。