用结构化图谱和动态检索，让对话系统记住长期信息更准更稳。
APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI
- 构建实体为中心的时序事件图谱，自动组织对话内容
- 在LOCOMO任务中达88.88%准确率，优于现有方法
- 适合需要长期记忆的客服、助手类应用
大语言模型在长时对话记忆方面仍存在可靠性问题：单纯扩大上下文窗口或使用简单检索常引入噪声并导致回复不稳定。我们提出APEX-MEM，一种对话记忆系统，包含三项核心创新：(1) 使用领域无关本体的属性图，将对话结构化为以实体为中心、具有时间锚定的事件；(2) 采用追加式存储，完整保留信息随时间演变的过程；(3) 多工具检索代理在查询时理解并解决冲突或变化的信息，生成简洁且上下文相关的记忆摘要。该查询时解析机制在保留完整交互历史的同时，抑制了无关细节。APEX-MEM在LOCOMO的问答任务上达到88.88%准确率，在LongMemEval上达86.2%，优于当前最先进的会话感知方法，证明结构化属性图能实现更连贯的长期对话推理。
原文摘要 · Abstract (English)
Large language models still struggle with reliable long-term conversational memory: simply enlarging context windows or applying naive retrieval often introduces noise and destabilizes responses. We present APEX-MEM, a conversational memory system that combines three key innovations: (1) a property graph which uses domain-agnostic ontology to structure conversations as temporally grounded events in an entity-centric framework, (2) append-only storage that preserves the full temporal evolution of information, and (3) a multi-tool retrieval agent that understands and resolves conflicting or evolving information at query time, producing a compact and contextually relevant memory summary. This retrieval-time resolution preserves the full interaction history while suppressing irrelevant details. APEX-MEM achieves 88.88% accuracy on LOCOMO's Question Answering task and 86.2% on LongMemEval, outperforming state-of-the-art session-aware approaches and demonstrating that structured property graphs enable more temporally coherent long-term conversational reasoning.
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