Empowering Working Memory for Large Language Model Agents
Jing Guo Nan Li Jianchuan Qi Hang Yang Ruiqiao Li Yuzhen Feng Si Zhang Ming Xu
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.