发表机构
Nanjing University; Jiutian Research（南京大学; 中移九天）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对设备端个性化智能体记忆路由与检索难题，提出混合图记忆框架HGP，利用轻量分类器与图结构记忆，在PAL-Set上S分数达35.58，显著优于基线。
AI 中文摘要
基于LLM的智能体在个性化交互任务中面临挑战，原因在于异构、多类型且隐含约束的长期痕迹。现有记忆机制在准确路由和检索方面存在困难，尤其是在个性化至关重要的设备端。大多数方法使用单向量表示，模糊了类型区分和关系结构。我们提出HGP，一种混合图记忆框架。HGP采用轻量级自增强分类器进行个性化记忆路由，并将情景记忆、语义记忆和程序记忆构建为图。它还将工作记忆提取为状态轨迹，以捕获当前状态和隐含约束，确保可靠的决策。该分类器减少了大型模型的调用，使得设备端部署成为可能，而图存储则实现了准确检索和增量用户画像细化。在两个基准上的实验表明，在PAL-Set解决方案选择上，HGP的S分数达到35.58，比最强基线高出近7分。代码和数据位于此https URL。
英文摘要
LLM-based agents face challenges in personalized interactive tasks due to heterogeneous, multi-typed, and implicitly constrained long-term traces. Existing memory mechanisms struggle with accurate routing and retrieval, especially on-device where personalization is critical. Most methods use single-vector representations, blurring type distinctions and relational structure. We propose HGP, a hybrid graph memory framework. HGP employs a lightweight self-enhancement classifier for personalized memory routing and constructs episodic, semantic, and procedural memories as graphs. It also extracts working memory as a state trajectory to capture current state and implicit constraints, ensuring reliable decision-making. The classifier reduces large-model calls, enabling on-device deployment, while graph storage enables accurate retrieval and incremental user profile refinement. Experiments on two benchmarks show that on PAL-Set solution selection, HGP achieves an S-score of 35.58, nearly 7 points above the strongest baseline. Code and data are at https://github.com/Ouan6/HGP-.git.
