RUVA: Personalized Transparent On-Device Graph Reasoning
This addresses privacy and control issues for users of personal AI, offering a novel solution rather than an incremental improvement.
The paper tackles the lack of accountability and privacy in personal AI systems by proposing Ruva, a 'Glass Box' architecture that replaces vector matching with graph reasoning, enabling users to inspect and precisely redact facts in a personal knowledge graph.
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/.