StructMem: Structured Memory for Long-Horizon Behavior in LLMs

2026-04-23

AI Summary

This work addresses the challenge in existing long-term conversational agents where memory systems struggle to balance reasoning efficiency with effective relational modeling—flat memories lack structure, while graph-based approaches incur high construction overhead. To this end, we propose StructMem, a structure-enhanced hierarchical memory framework that leverages temporal anchoring, dual-perspective modeling, and periodic semantic integration to preserve event-level bindings while establishing cross-event associations. By avoiding the explicit construction of costly knowledge graphs, StructMem enables efficient relational reasoning without sacrificing scalability. Experimental results on the LoCoMo dataset demonstrate that our approach significantly improves performance in temporal reasoning and multi-hop question answering, while substantially reducing token consumption, API calls, and runtime.

Technology Category

• Machine Learning: Structured Learning
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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 .

Problem

• structured memory
• long-horizon behavior
• temporal reasoning
• multi-hop question answering
• relational structure

Innovation

• structured memory
• hierarchical memory
• temporal reasoning
• multi-hop question answering
• LLM memory