用知识图谱让个人AI理解跨应用数据，实现可信推理。
The EpisTwin: A Knowledge Graph-Grounded Neuro-Symbolic Architecture for Personal AI
- 将多源异构数据转为语义三元组，构建用户专属知识图谱。
- 通过动态视觉精炼，在推理时保持符号实体与原始上下文一致。
- 专设评测集模拟真实数字足迹，适合可信个人AI研究者使用。
个人人工智能受限于用户数据在孤立系统间的碎片化。尽管检索增强生成提供部分解决方案，但其依赖向量相似性无法捕捉潜在语义拓扑与时间依赖关系。我们提出EpisTwin，一种基于可验证、以用户为中心的个人知识图谱的神经符号框架。该框架利用多模态语言模型将跨应用的异构数据转化为语义三元组。推理时，通过代理协调器结合图检索增强生成与在线深度视觉精炼，动态将符号实体重新锚定至原始视觉上下文。我们还构建了PersonalQA-71-100合成基准，用于模拟真实用户的数字足迹并评估性能。实验表明，该框架在多个前沿判别模型下表现稳健，为可信个人AI提供了可行方向。
原文摘要 · Abstract (English)
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.
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