HyperMem: Hypergraph Memory for Long-Term Conversations
2026-04-09 • Computation and Language
Computation and LanguageArtificial Intelligence
AI summaryⓘ
The authors propose HyperMem, a new memory system for conversational AI that better remembers long conversations by organizing information in a special way called a hypergraph. Unlike older methods that link pairs of facts, HyperMem connects multiple related pieces at once, grouping them into topics, episodes, and facts. This helps the system find and use relevant information more smoothly over time. Tests show HyperMem improves conversation quality compared to earlier methods.
long-term memoryconversational agentsRetrieval-Augmented Generation (RAG)hypergraphhierarchical memoryhigh-order associationscoarse-to-fine retrievalLoCoMo benchmark
Authors
Juwei Yue, Chuanrui Hu, Jiawei Sheng, Zuyi Zhou, Wenyuan Zhang, Tingwen Liu, Li Guo, Yafeng Deng
Abstract
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 facts via hyperedges, unifying scattered content into coherent units. Leveraging this structure, we design a hybrid lexical-semantic index and a coarse-to-fine retrieval strategy, supporting accurate and efficient retrieval of high-order associations. Experiments on the LoCoMo benchmark show that HyperMem achieves state-of-the-art performance with 92.73% LLM-as-a-judge accuracy, demonstrating the effectiveness of HyperMem for long-term conversations.