Membox: Weaving Topic Continuity into Long-Range Memory for LLM Agents

• URL: http://arxiv.org/abs/2601.03785v1
• Date: Wed, 07 Jan 2026 10:36:29 GMT
• Title: Membox: Weaving Topic Continuity into Long-Range Memory for LLM Agents
• Authors: Dehao Tao, Guoliang Ma, Yongfeng Huang, Minghu Jiang,
• Abstract summary: We introduce membox, a hierarchical memory architecture centered on a Topic Loom.<n>Membox monitors dialogue in a sliding-window fashion, grouping consecutive same-topic turns into coherent "memory boxes" at storage time.<n>Experiments on LoCoMo demonstrate that Membox achieves up to 68% F1 improvement on temporal reasoning tasks.
• Score: 14.666607208502185
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human-agent dialogues often exhibit topic continuity-a stable thematic frame that evolves through temporally adjacent exchanges-yet most large language model (LLM) agent memory systems fail to preserve it. Existing designs follow a fragmentation-compensation paradigm: they first break dialogue streams into isolated utterances for storage, then attempt to restore coherence via embedding-based retrieval. This process irreversibly damages narrative and causal flow, while biasing retrieval towards lexical similarity. We introduce membox, a hierarchical memory architecture centered on a Topic Loom that continuously monitors dialogue in a sliding-window fashion, grouping consecutive same-topic turns into coherent "memory boxes" at storage time. Sealed boxes are then linked by a Trace Weaver into long-range event-timeline traces, recovering macro-topic recurrences across discontinuities. Experiments on LoCoMo demonstrate that Membox achieves up to 68% F1 improvement on temporal reasoning tasks, outperforming competitive baselines (e.g., Mem0, A-MEM). Notably, Membox attains these gains while using only a fraction of the context tokens required by existing methods, highlighting a superior balance between efficiency and effectiveness. By explicitly modeling topic continuity, Membox offers a cognitively motivated mechanism for enhancing both coherence and efficiency in LLM agents.

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