Human-Inspired Perspectives: A Survey on AI Long-Term Memory

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 article 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.