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

TL;DR

Membox introduces a hierarchical memory system for LLM agents that captures topic continuity, improving long-range coherence and temporal reasoning while reducing context token usage.

Contribution

It presents a novel Topic Loom and Trace Weaver architecture that maintains topic continuity in dialogue memory, outperforming existing methods in coherence and efficiency.

Findings

Up to 68% F1 improvement on temporal reasoning tasks

Reduces context token usage compared to existing methods

Enhances coherence and efficiency in LLM dialogue memory

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.