面向长期对话智能体的、带有查询感知图增强的以事件为中心的记忆
Event-Centric Memory with Query-Aware Graph Augmentation for Long-Term Conversational Agents
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中文总结 AI 辅助
针对现有对话智能体记忆系统的局限，提出 QGMem 框架，通过以事件为中心的记忆组织与查询感知图增强，在六个基准任务中实现了检索、多跳推理等任务的性能提升。
中文摘要 AI 辅助
对于持久且个性化的对话智能体而言，记忆系统可通过存储过往交互并检索相关信息，使其能够对长期历史进行记忆、更新与推理。现有记忆系统通常遵循两种范式：扁平结构记忆与基于图的记忆。前者轻量，但事件关系与状态更新隐含；后者显式建模记忆结构，但会产生额外的构建成本，并在长期历史中引入无关关系。为解决这些局限，我们提出 QGMem，一种受人类记忆启发的新型记忆构建与激活框架，其中经验被组织为事件，且与查询相关的事件被建模为图以作为工作记忆。QGMem 将长对话历史转换为以事件索引的原子记忆单元，这些单元保留个体经验，并将相关单元整合为动态记忆轨迹，以保留状态轨迹与当前状态。当查询到达时，混合记忆检索收集互补的候选记忆，而查询感知重排序则激活最相关的单元作为紧凑的工作记忆。为揭示工作记忆中的关系依赖并支持感知冲突的推理，QGMem 将工作记忆组织为局部图，随后将其编码为图令牌，并与文本工作记忆一同提供给大语言模型（LLM），以在答案生成期间提升证据利用率。在六个基准上开展的实验验证了该框架，且在检索、多跳证据组合、冲突解决以及使用紧凑上下文与适度推理成本的超长对话推理中，均展现出一致的性能提升。
英文摘要
For persistent and personalized conversational agents, memory systems can enable them to remember, update, and reason over long histories by storing past interactions and retrieving relevant information. Existing memory systems typically follow two paradigms: flat-structured memory and graph-based memory. The former is lightweight but leaves event relations and state updates implicit, while the latter explicitly models memory structure but incurs additional construction cost and introduces irrelevant relations over long histories. To address these limitations, we propose QGMem, a novel memory construction and activation framework motivated by human memory, in which experience is organized into events and query-relevant events are modeled by graph as working memory. QGMem converts long dialogue histories into event-indexed atomic memory units that preserve individual experiences and consolidates related units into dynamic memory traces that retain state trajectories and current states. When a query arrives, hybrid memory retrieval gathers complementary candidate memories, and query-aware reranking activates the most relevant units as a compact working memory. To expose relational dependencies in the working memory and support conflict-aware reasoning, QGMem organizes the working memory as a local graph, which is then encoded as a graph token and provided to the LLM together with the textual working memory to improve evidence utilization during answer generation. Experiments across six benchmarks validate the framework and show consistent gains in retrieval, multi-hop evidence composition, conflict resolution, and ultra-long dialogue reasoning with compact contexts and moderate inference cost.
发表机构
- Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
- University of Chinese Academy of Sciences（中国科学院大学）
- Alibaba Group（阿里巴巴集团）
- PeopleAI Inc.（北京人人智能科技有限公司）
- Renmin University of China（中国人民大学）
- ShanghaiTech University（上海科技大学）
机构由 AI 辅助整理，请以论文原文为准。
