The EpisTwin: A Knowledge Graph-Grounded Neuro-Symbolic Architecture for Personal AI

• URL: http://arxiv.org/abs/2603.06290v1
• Date: Fri, 06 Mar 2026 13:48:23 GMT
• Title: The EpisTwin: A Knowledge Graph-Grounded Neuro-Symbolic Architecture for Personal AI
• Abstract summary: EpisTwin is a neuro-symbolic framework that grounds generative reasoning in a verifiable, user-centric Personal Knowledge Graph.<n>EpisTwin enables complex reasoning over the personal semantic graph via an agentic coordinator.<n>PersonalQA-71-100 is a synthetic benchmark designed to simulate a realistic user's digital footprint and evaluate EpisTwin performance.
• Score: 9.582176201200497
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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