AI RESEARCH
StructMem: Structured Memory for Long-Horizon Behavior in LLMs
arXiv CS.LG
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ArXi:2604.21748v1 Announce Type: cross Long-term conversational agents need memory systems that capture relationships between events, not merely isolated facts, to 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.