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

TL;DR

EpisTwin is a neuro-symbolic framework that enhances personal AI by grounding generative reasoning in a verifiable personal knowledge graph, integrating multimodal data and complex reasoning for trustworthy, holistic sensemaking.

Contribution

The paper introduces EpisTwin, a novel neuro-symbolic architecture that combines knowledge graphs, multimodal models, and reasoning techniques for improved personal AI capabilities.

Findings

Robust performance across state-of-the-art judge models

Effective grounding of heterogeneous data into semantic triples

Enhanced reasoning with dynamic re-grounding in visual context

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.