RUVA: Personalized Transparent On-Device Graph Reasoning
arXiv:2602.15553v1 Announce Type: new 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://sisinf
arXiv:2602.15553v1 Announce Type: new 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/.
Executive Summary
The article proposes Ruva, a novel 'Glass Box' architecture for Personal AI that leverages Graph Reasoning to address the limitations of current 'Black Box' Retrieval-Augmented Generation systems. Ruva enables users to inspect and correct AI decisions, ensuring accountability, transparency, and true privacy. By shifting from Vector Matching to Graph Reasoning, Ruva ensures the 'Right to be Forgotten' and empowers users to curate their own knowledge graph.
Key Points
- ▸ Ruva is a 'Glass Box' architecture that provides transparency and accountability in Personal AI
- ▸ Ruva uses Graph Reasoning to enable users to inspect and correct AI decisions
- ▸ Ruva ensures the 'Right to be Forgotten' by allowing precise redaction of specific facts
Merits
Enhanced Transparency
Ruva provides users with clear insights into AI decision-making processes, enabling them to understand and correct errors.
Improved Accountability
Ruva's design ensures that AI decisions are grounded in a Personal Knowledge Graph, making it easier to identify and correct errors.
True Privacy
Ruva's ability to precisely redact specific facts ensures that users' sensitive information is protected.
Demerits
Scalability Challenges
As the size of the Personal Knowledge Graph grows, Ruva's performance and efficiency may be affected, requiring further optimization.
User Complexity
Ruva's Human-in-the-Loop Memory Curation approach may require users to have a certain level of technical expertise, potentially limiting its accessibility.
Expert Commentary
The proposed Ruva architecture demonstrates a significant shift in the Personal AI landscape by prioritizing transparency, accountability, and user control. While scalability and user complexity may pose challenges, the potential benefits of Ruva's design, particularly in ensuring true privacy and the 'Right to be Forgotten,' make it an intriguing area of research. As the article concludes, Ruva hands users the pen, empowering them to curate their own knowledge graph and take ownership of their data. This approach has far-reaching implications for data protection regulations and the development of more user-centric AI systems.
Recommendations
- ✓ Further research should focus on optimizing Ruva's performance and efficiency, particularly as the size of the Personal Knowledge Graph grows.
- ✓ Developing user-friendly interfaces and educational resources can help to increase accessibility and adoption of Ruva's Human-in-the-Loop Memory Curation approach.
