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GoldenRetriever replaces ranking with threshold selection for encrypted RAG

Researchers propose a non-interactive homomorphic encryption framework that selects documents by similarity threshold rather than ranking, reducing latency in privacy-preserving retrieval-augmented generation systems.

1 min read

Researchers have published a new approach to privacy-preserving retrieval-augmented generation that replaces computationally expensive ranking with threshold-based document selection. The framework, called GoldenRetriever, uses homomorphic encryption to evaluate document similarity without exposing ...

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Method & sources
Source type
Primary publication (lab/vendor blog) — our analysis + implication
Source link
r/arxiv-compression
Published
UTC
Byline
By the gotcontext.ai team (editorial standards)
Correction?
corrections@gotcontext.ai

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