Lineage-Aware Retrieval-Augmented Generation (v5): Prisca-GraphRAG with Sparse-Autoencoder Feature Interpretability
Abstract
Paper v5 of The Ouroboros Thesis. Presents Prisca-GraphRAG (lineage-aware knowledge retrieval), VOTE-RAG (Omega-weighted Borda-count ensemble), Federated Prisca Privacy (Bekenstein-calibrated differential privacy), and Tawa SAE (ceque-radial indexed sparse autoencoder interpretability). Reference implementation in TypeScript. Part of the SZL Holdings governed AI platform.Part of the Ouroboros Thesis chain by Stephen P. Lutar Jr. (ORCID 0009-0001-0110-4173, SZL Holdings). Reference implementation: github.com/szl-holdings/ouroboros. Source repository: github.com/szl-holdings/ouroboros-thesis.
Fields of Study & Subjects
Advanced Graph Neural Networks
Privacy-Preserving Technologies in Data
Machine Learning in Healthcare
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 4 May 2026
- Language
- EN