2/18/2026 | USA | technology | ✓ Verified - arxiv.org

RUVA: Personalized Transparent On-Device Graph Reasoning

#RUVA #Personalized Transparent On-Device Graph Reasoning #Personal AI #Retrieval‑Augmented Generation #Vector databases #AI hallucination #Data accountability #Probabilistic ghosts #Transparent reasoning

📌 Key Takeaways

RUVA proposes a new method for personalized on‑device reasoning that enhances transparency in personal AI systems.
The paper highlights the dominance of black‑box Retrieval‑Augmented Generation and the lack of accountability in standard vector databases.
It emphasizes that users cannot inspect the cause of AI hallucinations or sensitive data retrieval in these systems.
It points out that mathematically removing concepts from vector spaces leaves behind probabilistic ghosts that violate trust in AI.
The motivation is to provide a way for users to understand and correct errors, ensuring personal data remains truly private and controllable.

📖 Full Retelling

The new research paper titled *RUVA: Personalized Transparent On-Device Graph Reasoning* was released on arXiv on February 15, 2026 by an unspecified research team. The authors introduce a novel approach called RUVA that aims to bring accountability and transparency to the Personal AI ecosystem, which is presently dominated by black‑box Retrieval‑Augmented Generation models. The paper is available online at arXiv:2602.15553v1 and focuses on the shortcomings of conventional statistical vector databases—particularly their inability to allow users to inspect why an AI might hallucinate or retrieve sensitive information, and the problematic nature of attempting to delete concepts from a vector space, which can leave lingering probabilistic “ghosts.” The motivation for the study is clear: to develop a method that enables users to understand and correct errors in personal AI systems and to ensure that sensitive data does not unintentionally persist.

🏷️ Themes

Personal AI, Accountability, Transparency, On‑device reasoning, Vector databases, AI hallucination, Data privacy, Concept deletion

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Deep Analysis

Why It Matters

RUVA introduces a transparent on-device graph reasoning system that allows users to inspect and correct AI errors, addressing accountability issues in black-box models. By enabling direct manipulation of knowledge graphs, it reduces hallucinations and eliminates residual ghost concepts from vector spaces.

Context & Background

Black-box retrieval-augmented generation lacks transparency
Vector databases cannot precisely delete concepts
On-device graph reasoning offers inspectable reasoning paths

What Happens Next

Future work will focus on integrating RUVA with mainstream AI platforms and expanding its support for multilingual knowledge graphs. The approach may also inspire new regulatory standards for AI accountability.

Frequently Asked Questions

How does RUVA differ from traditional vector databases?

It uses explicit graph structures that can be inspected and edited on the device, unlike opaque vector embeddings.

Can RUVA run on low-power devices?

Yes, the on-device design is optimized for efficiency, making it suitable for smartphones and edge hardware.

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Original Source

              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 tr
            

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Source

arxiv.org