HyperMem: Hypergraph Memory for Long-Term Conversations
Long-term memory is essential for conversational agents to maintain coherence, track persistent tasks, and provide personalized interactions across extended dialogues. However, existing approaches as Retrieval-Augmented Generation (RAG) and graph-based memory mostly rely on pairwise relations, which can hardly capture high-order associations, i.e., joint dependencies among multiple elements, causing fragmented retrieval. To this end, we propose HyperMem, a hypergraph-based hierarchical memory architecture that explicitly models such associations using hyperedges. Particularly, HyperMem structures memory into three levels: topics, episodes, and facts, and groups related episodes and their ...
| Authors | Juwei Yue, Chuanrui Hu, Jiawei Sheng et al. |
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| Primary category | cs.CL |
| Published | 9 April 2026 |
| Last updated at source | 10 April 2026 |
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