SYNAPSE: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation

• URL: http://arxiv.org/abs/2601.02744v1
• Date: Tue, 06 Jan 2026 06:19:58 GMT
• Title: SYNAPSE: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation
• Authors: Hanqi Jiang, Junhao Chen, Yi Pan, Ling Chen, Weihang You, Yifan Zhou, Ruidong Zhang, Yohannes Abate, Tianming Liu,
• Abstract summary: We introduce Synapse, a unified memory architecture that transcends static rather than pre-computed links.<n>We show that Synapse significantly outperforms state-of-the-art methods in complex temporal and multi-hop reasoning tasks.<n>Our code and data will be made publicly available upon acceptance.
• Score: 29.545442480332515
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While Large Language Models (LLMs) excel at generalized reasoning, standard retrieval-augmented approaches fail to address the disconnected nature of long-term agentic memory. To bridge this gap, we introduce Synapse (Synergistic Associative Processing Semantic Encoding), a unified memory architecture that transcends static vector similarity. Drawing from cognitive science, Synapse models memory as a dynamic graph where relevance emerges from spreading activation rather than pre-computed links. By integrating lateral inhibition and temporal decay, the system dynamically highlights relevant sub-graphs while filtering interference. We implement a Triple Hybrid Retrieval strategy that fuses geometric embeddings with activation-based graph traversal. Comprehensive evaluations on the LoCoMo benchmark show that Synapse significantly outperforms state-of-the-art methods in complex temporal and multi-hop reasoning tasks, offering a robust solution to the "Contextual Tunneling" problem. Our code and data will be made publicly available upon acceptance.

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