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Integrating Temporal Representations for Dynamic Memory Retrieval and Management in Large Language Models

Conventional dialogue agents often struggle with effective memory recall, leading to redundant retrieval and inadequate management of unique user associations. To address this, we propose SynapticRAG, a novel approach integrating synaptic dynamics into Retrieval-Augmented Generation (RAG). SynapticRAG integrates temporal representations into memory vectors, mimicking biological synapses by differentiating events based on occurrence times and dynamically updating memory significance. This model employs temporal scoring for memory connections and a synaptic-inspired propagation control mechanism. Experiments across English, Japanese, and Chinese datasets demonstrate SynapticRAG's superiority over existing methods, including traditional RAG, with up to 14.66\% improvement in memory retrieval accuracy. Our approach advances context-aware dialogue AI systems by enhancing long-term context maintenance and specific information extraction from conversations.

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