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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.02744 |
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| _version_ | 1866915800254775296 |
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| author | Jiang, Hanqi Chen, Junhao Pan, Yi Chen, Ling You, Weihang Zhou, Yifan Zhang, Ruidong Sikora, Andrea Zhao, Lin Abate, Yohannes Liu, Tianming |
| author_facet | Jiang, Hanqi Chen, Junhao Pan, Yi Chen, Ling You, Weihang Zhou, Yifan Zhang, Ruidong Sikora, Andrea Zhao, Lin Abate, Yohannes Liu, Tianming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02744 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SYNAPSE: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation Jiang, Hanqi Chen, Junhao Pan, Yi Chen, Ling You, Weihang Zhou, Yifan Zhang, Ruidong Sikora, Andrea Zhao, Lin Abate, Yohannes Liu, Tianming Computation and Language 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. |
| title | SYNAPSE: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.02744 |