让大模型记住对话历史并智能检索，提升个性化聊天体验。
MemORAI: Memory Organization and Retrieval via Adaptive Graph Intelligence for LLM Conversational Agents
- 用双层压缩筛选关键记忆，保留用户个人特征
- 在转述级别追踪事实来源，防止信息失真
- 根据问题动态调整检索权重，更懂上下文
大型语言模型缺乏持久记忆，难以支持长期个性化对话。现有基于图的记忆系统存在信息稀释、溯源缺失和统一检索忽略查询上下文的问题。我们提出 MemORAI（通过自适应图智能实现记忆组织与检索），集成三项创新：基于双层压缩的选别式记忆过滤，保留与用户人格相关的高价值内容；在回合级别追踪事实来源的溯源增强型多关系图；以及基于动态加权PageRank的查询自适应子图检索，实现查询条件下的边权重调节。在 LOCOMO 与 LongMemEval 基准上的评估显示，MemORAI 在记忆检索与个性化响应生成上达到当前最优性能，证明选择性存储、丰富表示与自适应检索对构建连贯、个性化的 LLM 对话代理至关重要。
原文摘要 · Abstract (English)
Large Language Models (LLMs) lack persistent memory for long-term personalized conversations. Existing graph-based memory systems suffer from information dilution, absent provenance tracking, and uniform retrieval that ignores query context. We introduce MemORAI (Memory Organization and Retrieval via Adaptive Graph Intelligence), a framework that integrates three innovations: selective memory filtering with dual-layer compression to retain user-persona-relevant content, a provenance-enriched multi-relational graph tracking factual origins at the turn level, and query-adaptive subgraph retrieval with Dynamic Weighted PageRank that applies query-conditioned edge weighting. Evaluated on LOCOMO and LongMemEval benchmarks, MemORAI achieves state-of-the-art performance in memory retrieval and personalized response generation, demonstrating that selective storage, enriched representation, and adaptive retrieval are essential for coherent, personalized LLM agents.
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