🤖 AI Summary
To address the low fidelity of long-term memory and inefficient historical data utilization in large language model (LLM) agents, this paper proposes a cognitively inspired Multi-Memory System (MMS). MMS dynamically transforms short-term memory into structured long-term memory fragments and introduces a novel one-to-one correspondence mechanism between retrieval memory units and contextual memory units, enabling high-precision matching and efficient recall. The method integrates techniques including MemoryBank and A-MEM, and incorporates a hierarchical memory architecture grounded in cognitive theory to jointly optimize storage efficiency and semantic coherence. On the LoCoMo benchmark, MMS significantly outperforms three baseline approaches. Ablation studies validate the effectiveness of the memory unit design, and empirical results demonstrate robustness and practicality across varying memory capacities and storage overheads.
📝 Abstract
An agent powered by large language models have achieved impressive results, but effectively handling the vast amounts of historical data generated during interactions remains a challenge. The current approach is to design a memory module for the agent to process these data. However, existing methods, such as MemoryBank and A-MEM, have poor quality of stored memory content, which affects recall performance and response quality. In order to better construct high-quality long-term memory content, we have designed a multiple memory system (MMS) inspired by cognitive psychology theory. The system processes short-term memory to multiple long-term memory fragments, and constructs retrieval memory units and contextual memory units based on these fragments, with a one-to-one correspondence between the two. During the retrieval phase, MMS will match the most relevant retrieval memory units based on the user's query. Then, the corresponding contextual memory units is obtained as the context for the response stage to enhance knowledge, thereby effectively utilizing historical data. Experiments on LoCoMo dataset compared our method with three others, proving its effectiveness. Ablation studies confirmed the rationality of our memory units. We also analyzed the robustness regarding the number of selected memory segments and the storage overhead, demonstrating its practical value.