AI RESEARCH
HyperMem: Hypergraph Memory for Long-Term Conversations
arXiv CS.CL
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ArXi:2604.08256v1 Announce Type: new 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.