EpisTwin：一种基于知识图谱的神经符号架构用于个人AI
The EpisTwin: A Knowledge Graph-Grounded Neuro-Symbolic Architecture for Personal AI
AI总结：
EpisTwin通过构建个人知识图谱，结合多模态语言模型和代理协调器，实现对用户数据的语义理解和复杂推理，提升个人AI的可信度和整体认知能力。
摘要：
个人人工智能目前受到用户数据碎片化存储在孤立数据仓库中的阻碍。虽然检索增强生成提供了一种部分解决方案，但其对无结构向量相似性的依赖无法捕捉到对于整体认知至关重要的潜在语义拓扑和时间依赖性。我们引入了EpisTwin，一种将生成推理扎根于可验证、以用户为中心的个人知识图谱的神经符号框架。EpisTwin利用多模态语言模型将异构、跨应用的数据提升为语义三元组。在推理过程中，EpisTwin通过结合图检索增强生成与在线深度视觉细化的代理协调器，能够在个人语义图上执行复杂推理，动态地将符号实体重新定位到其原始视觉上下文中。我们还引入了PersonalQA-71-100，一个合成基准，旨在模拟现实用户的数字足迹并评估EpisTwin的性能。我们的框架在一系列最先进的判断模型上展示了稳健的结果，为可信的个人AI提供了有前途的方向。
英文摘要：
Personal Artificial Intelligence is currently hindered by the fragmentation of user data across isolated silos. While Retrieval-Augmented Generation offers a partial remedy, its reliance on unstructured vector similarity fails to capture the latent semantic topology and temporal dependencies essential for holistic sensemaking. We introduce EpisTwin, a neuro-symbolic framework that grounds generative reasoning in a verifiable, user-centric Personal Knowledge Graph. EpisTwin leverages Multimodal Language Models to lift heterogeneous, cross-application data into semantic triples. At inference, EpisTwin enables complex reasoning over the personal semantic graph via an agentic coordinator that combines Graph Retrieval-Augmented Generation with Online Deep Visual Refinement, dynamically re-grounding symbolic entities in their raw visual context. We also introduce PersonalQA-71-100, a synthetic benchmark designed to simulate a realistic user's digital footprint and evaluate EpisTwin performance. Our framework demonstrates robust results across a suite of state-of-the-art judge models, offering a promising direction for trustworthy Personal AI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
