Membox: Weaving Topic Continuity into Long-Range Memory for LLM Agents
This work addresses the challenge that existing large language model agents struggle to maintain thematic continuity in dialogue, often resulting in fragmented narratives and broken causal chains. To overcome this, the authors propose Membox, a novel hierarchical memory architecture that introduces thematic continuity modeling at the storage stage. Membox employs Topic Loom to aggregate thematically related dialogue segments into coherent “memory boxes” and utilizes Trace Weaver to construct long-range event timelines across conversational discontinuities, enabling cognitively inspired, efficient memory organization. Departing from the conventional “fragmented storage–retrieval reconstruction” paradigm, Membox achieves up to a 68% relative improvement in temporal reasoning F1 score on the LoCoMo benchmark, significantly outperforming baselines such as Mem0 and A-MEM while using fewer context tokens, thereby achieving both higher efficiency and superior performance.