个人人工智能：面向个性化LLM智能体的知识图谱存储与检索方法系统比较

摘要

在开发自适应AI系统的过程中，如何通过有效整合用户交互历史实现语言模型个性化仍是核心挑战。尽管大语言模型（LLM）与检索增强生成（RAG）技术结合提升了事实准确性，但它们往往缺乏结构化记忆机制，难以适应复杂长期交互场景。为此，我们提出基于知识图谱的柔性外部记忆框架，该框架由LLM自动构建并动态更新。在AriGraph架构基础上，我们创新性地引入支持标准边与两种超边的混合图设计，实现丰富动态的语义与时序表征。该框架还支持A*算法、WaterCircles遍历、束搜索及混合方法等多重检索机制，可适配不同数据集与LLM能力。通过在TriviaQA、HotpotQA和DiaASQ基准测试上的评估，我们证明不同记忆与检索配置能针对特定任务实现最优性能。此外，我们为DiaASQ基准扩展了时序标注和内部矛盾陈述，验证了系统在管理时序依赖和上下文感知推理方面具有持续鲁棒性与有效性。

English

Personalizing language models by effectively incorporating user interaction history remains a central challenge in the development of adaptive AI systems. While large language models (LLMs), combined with Retrieval-Augmented Generation (RAG), have improved factual accuracy, they often lack structured memory and fail to scale in complex, long-term interactions. To address this, we propose a flexible external memory framework based on a knowledge graph that is constructed and updated automatically by the LLM. Building upon the AriGraph architecture, we introduce a novel hybrid graph design that supports both standard edges and two types of hyper-edges, enabling rich and dynamic semantic and temporal representations. Our framework also supports diverse retrieval mechanisms, including A*, WaterCircles traversal, beam search, and hybrid methods, making it adaptable to different datasets and LLM capacities. We evaluate our system on TriviaQA, HotpotQA, DiaASQ benchmarks and demonstrate that different memory and retrieval configurations yield optimal performance depending on the task. Additionally, we extend the DiaASQ benchmark with temporal annotations and internally contradictory statements, showing that our system remains robust and effective in managing temporal dependencies and context-aware reasoning

个人人工智能：面向个性化LLM智能体的知识图谱存储与检索方法系统比较

PersonalAI: A Systematic Comparison of Knowledge Graph Storage and Retrieval Approaches for Personalized LLM agents

摘要

Support