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        <title>H-Mem: Hybrid Multi-Dimensional Memory Management for Long-Context Conversational Agents</title>
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    <abstract>Long-context conversational agents require robust memory, but existing frameworks struggle to organize information effectively across dimensions like time and topic, leading to poor retrieval. To address this, we introduce H-Mem, a novel Hybrid Multi-Dimensional Memory architecture. H-Mem stores conversational facts in two parallel, hierarchical data structures: a temporal tree that organizes information chronologically and a semantic tree that organizes it conceptually. This dual-tree design enables a hybrid retrieval mechanism managed by an intelligent Mode Controller. Based on the query, the controller dynamically chooses between a sequential search using semantic anchors and an intersective search combining both hierarchies. Our experiments on long-context QA datasets demonstrate that H-Mem provides a more flexible approach to memory management, leading to significant improvements of over 8.4% compared to other state-of-the-art systems.</abstract>
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%0 Conference Proceedings
%T H-Mem: Hybrid Multi-Dimensional Memory Management for Long-Context Conversational Agents
%A Ye, Zihe
%A Huang, Jingyuan
%A Chen, Weixin
%A Zhang, Yongfeng
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F ye-etal-2026-h
%X Long-context conversational agents require robust memory, but existing frameworks struggle to organize information effectively across dimensions like time and topic, leading to poor retrieval. To address this, we introduce H-Mem, a novel Hybrid Multi-Dimensional Memory architecture. H-Mem stores conversational facts in two parallel, hierarchical data structures: a temporal tree that organizes information chronologically and a semantic tree that organizes it conceptually. This dual-tree design enables a hybrid retrieval mechanism managed by an intelligent Mode Controller. Based on the query, the controller dynamically chooses between a sequential search using semantic anchors and an intersective search combining both hierarchies. Our experiments on long-context QA datasets demonstrate that H-Mem provides a more flexible approach to memory management, leading to significant improvements of over 8.4% compared to other state-of-the-art systems.
%U https://aclanthology.org/2026.eacl-long.363/
%P 7756-7775

Markdown (Informal)

[H-Mem: Hybrid Multi-Dimensional Memory Management for Long-Context Conversational Agents](https://aclanthology.org/2026.eacl-long.363/) (Ye et al., EACL 2026)

• H-Mem: Hybrid Multi-Dimensional Memory Management for Long-Context Conversational Agents (Ye et al., EACL 2026)

ACL

• Zihe Ye, Jingyuan Huang, Weixin Chen, and Yongfeng Zhang. 2026. H-Mem: Hybrid Multi-Dimensional Memory Management for Long-Context Conversational Agents. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7756–7775, Rabat, Morocco. Association for Computational Linguistics.