S ynaptic RAG : Enhancing Temporal Memory Retrieval in Large Language Models through Synaptic Mechanisms (2025)

Abstract: Existing retrieval methods in Large Language Models show degradation in accuracy when handling temporally distributed conversations, primarily due to their reliance on simple similarity-based retrieval. Unlike existing memory retrieval methods that rely solely on semantic similarity, we propose SynapticRAG, which uniquely combines temporal association triggers with biologically-inspired synaptic propagation mechanisms. Our approach uses temporal association triggers and synaptic-like stimulus propagation to identify relevant dialogue histories. A dynamic leaky integrate-and-fire mechanism then selects the most contextually appropriate memories. Experiments on four datasets of English, Chinese and Japanese show that compared to state-of-the-art memory retrieval methods, SynapticRAG achieves consistent improvements across multiple metrics up to 14.66% points. This work bridges the gap between cognitive science and language model development, providing a new framework for memory management in conversational systems.

## introduction as ai systems become increasingly integrated into daily communications, the ability to maintain natural conversations has become crucial @xcite @xcite . while large language models (llms) have achieved remarkable progress in dialogue generation, they face a fundamental challenge: maintaining coherent long-term memory @xcite @xcite . this limitation becomes particularly evident in their struggle to retrieve and integrate contextually relevant information from temporally distributed dialogue histories @xcite @xcite . for instance, current state-of-the-art memory retrieval methods show a 17% degradation in response relevance when conversation context spans multiple sessions, significantly impacting user experience in real-world applications. inspired by cognitive science research, which shows that human memory retrieval relies heavily on temporal triggers @xcite and synaptic connections between memory nodes @xcite @xcite , rather than pure memory strength. in contrast, current methods primarily use simple similarity-based retrieval, failing to capture these temporal-associative aspects. inspired by these cognitive principles, we propose synapticrag, a neuroscience-inspired memory retrieval framework. our approach integrates temporal association triggers, synaptic-like memory connections, and dynamic activation control to achieve more human-like memory retrieval in dialogue systems. this approach is theoretically motivated by the proven effectiveness of biological memory systems in handling temporal dependencies and context integration. our work makes three key contributions: (1) a novel biologicallyinspired memory retrieval mechanism, (2) integration of temporal and associative memory aspects in memory retrieval systems, and (3) empirical validation across three languages and datasets, showing consistent improvements up to 14.66% points over state-of-the-art memory retrieval methods on english, chinese and japanese benchmarks. ## approach our approach enhances dialogue memory recall by integrating temporal dynamics into retrieval mechanisms. the model consists of four components, as described in figure 1 . ## conclusion in this paper, we introduced synapticrag, a biologically-inspired memory retrieval model that enhances temporal memory management in dialogue systems. by integrating temporal association triggers, synaptic-like stimulus propagation, and dynamic memory activation mechanisms, our approach effectively addresses the limitations of simple similarity-based retrieval in handling temporally distributed conversations. our experiments demonstrate while rag excels in simpler straightforward retrieval tasks, synapticrag demonstrates superior performance in temporal-aware dialogue contexts where understanding dialogue history is critical, achieving consistent improvements across multiple languages and dialogue scenarios.