RUVA: Personalized Transparent On-Device Graph Reasoning

TL;DR

Ruva introduces a transparent, graph-based AI architecture that allows users to inspect and precisely edit personal knowledge, enhancing privacy and control over AI-generated information.

Contribution

It presents the first human-in-the-loop graph reasoning system for personal AI, enabling transparent knowledge inspection and exact redaction, addressing privacy and accountability issues.

Findings

Enables inspection of AI knowledge base by users.

Allows precise redaction of specific facts.

Supports human-in-the-loop knowledge management.

Abstract

The Personal AI landscape is currently dominated by "Black Box" Retrieval-Augmented Generation. While standard vector databases offer statistical matching, they suffer from a fundamental lack of accountability: when an AI hallucinates or retrieves sensitive data, the user cannot inspect the cause nor correct the error. Worse, "deleting" a concept from a vector space is mathematically imprecise, leaving behind probabilistic "ghosts" that violate true privacy. We propose Ruva, the first "Glass Box" architecture designed for Human-in-the-Loop Memory Curation. Ruva grounds Personal AI in a Personal Knowledge Graph, enabling users to inspect what the AI knows and to perform precise redaction of specific facts. By shifting the paradigm from Vector Matching to Graph Reasoning, Ruva ensures the "Right to be Forgotten." Users are the editors of their own lives; Ruva hands them the pen. The project and the demo video are available at http://sisinf00.poliba.it/ruva/.