Human-inspired Perspectives: A Survey on AI Long-term Memory

Zihong He; Weizhe Lin; Hao Zheng; Fan Zhang; Matt W. Jones; Laurence Aitchison; Xuhai Xu; Miao Liu; Per Ola Kristensson; Junxiao Shen

arXiv · 2411.00489

Human-inspired Perspectives: A Survey on AI Long-term Memory

Zihong He·Weizhe Lin·Hao Zheng·Fan Zhang·Matt W. Jones·Laurence Aitchison·Xuhai Xu·Miao Liu·Per Ola Kristensson·Junxiao Shen

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Abstract

With the rapid advancement of AI systems, their abilities to store, retrieve, and utilize information over the long term - referred to as long-term memory - have become increasingly significant. These capabilities are crucial for enhancing the performance of AI systems across a wide range of tasks. However, there is currently no comprehensive survey that systematically investigates AI's long-term memory capabilities, formulates a theoretical framework, and inspires the development of next-generation AI long-term memory systems. This paper begins by introducing the mechanisms of human long-term memory, then explores AI long-term memory mechanisms, establishing a mapping between the two. Based on the mapping relationships identified, we extend the current cognitive architectures and propose the Cognitive Architecture of Self-Adaptive Long-term Memory (SALM). SALM provides a theoretical framework for the practice of AI long-term memory and holds potential for guiding the creation of next-generation long-term memory driven AI systems. Finally, we delve into the future directions and application prospects of AI long-term memory.

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BibTeX RIS

Zihong He, Weizhe Lin, Hao Zheng, Fan Zhang, Matt W. Jones, Laurence Aitchison, Xuhai Xu, Miao Liu, Per Ola Kristensson, Junxiao Shen. 2025-01-12. Human-inspired Perspectives: A Survey on AI Long-term Memory. https://arxiv.org/abs/2411.00489

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