让个人AI可解释可编辑，用户能自由删改知识细节
RUVA: Personalized Transparent On-Device Graph Reasoning
- 用知识图谱替代向量检索，实现可追溯的推理
- 支持精确删除特定事实，真正实现隐私清除
- 适合关注个人数据控制权的开发者与普通用户
当前个人AI主要依赖“黑箱”式检索增强生成，基于向量数据库的统计匹配缺乏可审计性：当AI产生幻觉或泄露敏感信息时，用户无法追溯原因或修正错误。更严重的是，从向量空间中‘删除’概念在数学上不精确，残留概率性‘幽灵’，违背真实隐私。我们提出Ruva，首个面向人机协同记忆管理的‘透明盒子’架构。它将个人AI建立在个人知识图谱之上，让用户可查看AI所知内容，并精准删改具体事实。通过从向量匹配转向图推理，确保‘被遗忘的权利’。用户成为自身记忆的编辑者，Ruva赋予他们执笔之权。项目与演示视频见 http://sisinf00.poliba.it/ruva/。
原文摘要 · Abstract (English)
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/.
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