Lineage-Aware Retrieval-Augmented Generation (v5): Prisca-GraphRAG with Sparse-Autoencoder Feature Interpretability

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Authors: Stephen Paul Jr. Lutar
Cited by: 3

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