[2604.12285] GAM: Hierarchical Graph-based Agentic Memory for LLM Agents

Computer Science > Artificial Intelligence arXiv:2604.12285 (cs) [Submitted on 14 Apr 2026] Title:GAM: Hierarchical Graph-based Agentic Memory for LLM Agents Authors:Zhaofen Wu, Hanrong Zhang, Fulin Lin, Wujiang Xu, Xinran Xu, Yankai Chen, Henry Peng Zou, Shaowen Chen, Weizhi Zhang, Xue Liu, Philip S. Yu, Hongwei Wang View a PDF of the paper titled GAM: Hierarchical Graph-based Agentic Memory for LLM Agents, by Zhaofen Wu and 11 other authors View PDF HTML (experimental) Abstract:To sustain coherent long-term interactions, Large Language Model (LLM) agents must navigate the tension between acquiring new information and retaining prior knowledge. Current unified stream-based memory systems facilitate context updates but remain vulnerable to interference from transient noise. Conversely, discrete structured memory architectures provide robust knowledge retention but often struggle to adapt to evolving narratives. To address this, we propose GAM, a hierarchical Graph-based Agentic Memory framework that explicitly decouples memory encoding from consolidation to effectively resolve the conflict between rapid context perception and stable knowledge retention. By isolating ongoing dialogue in an event progression graph and integrating it into a topic associative network only upon semantic shifts, our approach minimizes interference while preserving long-term consistency. Additionally, we introduce a graph-guided, multi-factor retrieval strategy to enhance context precision. Exper...