$π$-RAG is a new architecture designed to enhance privacy in retrieval-augmented generation (RAG) systems for large language models (LLMs). It addresses security concerns by decoupling LLMs from sensitive data storage, preventing direct access to raw vector embeddings. The system uses the digits of $π$ to create an immutable indirection layer, ensuring that the LLM remains oblivious to the data it retrieves. This approach aims to provide deterministic randomness, auditability, and differential privacy, making it suitable for high-compliance industries like finance and healthcare. AI
IMPACT Introduces a novel privacy-preserving technique for LLM retrieval, potentially enabling wider adoption in sensitive sectors.
RANK_REASON The item describes a novel architecture presented in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]
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- Canonical Intent Centroids
- large-language models
- retrieval-augmented generation
- Semantic Quantization Layer
- $π$-RAG
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