Large language models (LLMs) have achieved impressive linguistic
capabilities. However, a key limitation persists in their lack of human-like
memory faculties. LLMs exhibit constrained memory retention across sequential
interactions, hindering complex reasoning. This paper explores the potential of
applying cognitive psychology's working memory frameworks, to enhance LLM
architecture. The limitations of traditional LLM memory designs are analyzed,
including their isolation of distinct dialog episodes and lack of persistent
memory links. To address this, an innovative model is proposed incorporating a
centralized Working Memory Hub and Episodic Buffer access to retain memories
across episodes. This architecture aims to provide greater continuity for
nuanced contextual reasoning during intricate tasks and collaborative
scenarios. While promising, further research is required into optimizing
episodic memory encoding, storage, prioritization, retrieval, and security.
Overall, this paper provides a strategic blueprint for developing LLM agents
with more sophisticated, human-like memory capabilities, highlighting memory
mechanisms as a vital frontier in artificial general intelligence.

本文通过应用认知心理学的工作记忆框架来增强大型语言模型（LLMs）的架构，以解决其在人类记忆能力方面的限制，并提出了一种创新模型，包括集中的工作记忆中心和情景缓冲区，以提供更高的连续性，以实现复杂任务和合作场景中的细致语境推理。然而，对于情景记忆的优化编码、存储、优先级、检索和安全性仍需进一步研究，以促进发展具有更复杂、类似人类记忆能力的 LLM 代理。这表明记忆机制是人工通用智能领域的重要前沿。