StructMem: Structured Memory for Long-Horizon Behavior in LLMs
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915952187146240 |
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| author | Xu, Buqiang Chen, Yijun Fang, Jizhan Zhong, Ruobin Yao, Yunzhi Zhu, Yuqi Du, Lun Deng, Shumin |
| author_facet | Xu, Buqiang Chen, Yijun Fang, Jizhan Zhong, Ruobin Yao, Yunzhi Zhu, Yuqi Du, Lun Deng, Shumin |
| contents | Long-term conversational agents need memory systems that capture relationships between events, not merely isolated facts, to support temporal reasoning and multi-hop question answering. Current approaches face a fundamental trade-off: flat memory is efficient but fails to model relational structure, while graph-based memory enables structured reasoning at the cost of expensive and fragile construction. To address these issues, we propose \textbf{StructMem}, a structure-enriched hierarchical memory framework that preserves event-level bindings and induces cross-event connections. By temporally anchoring dual perspectives and performing periodic semantic consolidation, StructMem improves temporal reasoning and multi-hop performance on \texttt{LoCoMo}, while substantially reducing token usage, API calls, and runtime compared to prior memory systems, see https://github.com/zjunlp/LightMem . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_21748 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | StructMem: Structured Memory for Long-Horizon Behavior in LLMs Xu, Buqiang Chen, Yijun Fang, Jizhan Zhong, Ruobin Yao, Yunzhi Zhu, Yuqi Du, Lun Deng, Shumin Computation and Language Artificial Intelligence Information Retrieval Machine Learning Multiagent Systems Long-term conversational agents need memory systems that capture relationships between events, not merely isolated facts, to support temporal reasoning and multi-hop question answering. Current approaches face a fundamental trade-off: flat memory is efficient but fails to model relational structure, while graph-based memory enables structured reasoning at the cost of expensive and fragile construction. To address these issues, we propose \textbf{StructMem}, a structure-enriched hierarchical memory framework that preserves event-level bindings and induces cross-event connections. By temporally anchoring dual perspectives and performing periodic semantic consolidation, StructMem improves temporal reasoning and multi-hop performance on \texttt{LoCoMo}, while substantially reducing token usage, API calls, and runtime compared to prior memory systems, see https://github.com/zjunlp/LightMem . |
| title | StructMem: Structured Memory for Long-Horizon Behavior in LLMs |
| topic | Computation and Language Artificial Intelligence Information Retrieval Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2604.21748 |