# PersonalAI：个性化LLM智能体知识图谱存储与检索方法的系统比较

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-12 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmocrb56j0592slsjbwh3n8i6
- 原文链接：https://arxiv.org/abs/2506.17001

## AI 摘要

本文提出PersonalAI，一种基于知识图谱的灵活外部记忆框架，由LLM自动构建和更新。该框架在AriGraph基础上引入混合图设计，支持标准边与两种超边，实现丰富的语义和时间表示。系统集成A*、WaterCircles遍历、束搜索等多种检索机制，在TriviaQA、HotpotQA及扩展版DiaASQ基准测试中验证表明：不同任务需配置不同记忆与检索策略。研究还扩展DiaASQ数据集，添加时间注释和矛盾陈述，证明系统在时间依赖管理和上下文感知推理中的鲁棒性。

## 正文

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