# π-RAG: Oblivious Retrieval via Semantic Quantization and Transcendental Addressing for Large Language Models

arXiv: https://arxiv.org/abs/2606.22153 · HTML: https://mlsanity.com/paper/2606.22153/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 11 references that are in the map.
- First posted on arXiv 20 Jun 2026.

## Builds on (most cited first, 9 of 9)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Differentially Private Fine-tuning of Language Models](https://mlsanity.com/paper/2110.06500/) (2021)
- [Information Leakage in Embedding Models](https://mlsanity.com/paper/2004.00053/) (2020)
- [Unveiling Privacy Risks in LLM Agent Memory](https://mlsanity.com/paper/2502.13172/) (2025)
- [A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems](https://mlsanity.com/paper/2402.18649/) (2024)
- [Privacy-Aware RAG: Secure and Isolated Knowledge Retrieval](https://mlsanity.com/paper/2503.15548/) (2025)
- [LLM Access Shield: Domain-Specific LLM Framework for Privacy Policy Compliance](https://mlsanity.com/paper/2505.17145/) (2025)
- [Optimizing Privacy-Preserving Primitives to Support LLM-Scale Applications](https://mlsanity.com/paper/2509.25072/) (2025)

## Abstract (arXiv)

This paper introduces π-RAG, a novel architecture for oblivious retrieval that decouples Large Language Models (LLMs) from sensitive data storage without sacrificing semantic understanding. Traditional Retrieval-Augmented Generation (RAG) architectures expose raw vector embeddings to potential inversion attacks and nondeterministic retrieval failures. To address this, we utilize the digits of π as a source of transcendental entropy, creating an immutable indirection layer between the LLM and private records. The value π provides immutability, is uneditable and math governs it. The architecture also introduces a Semantic Quantization Layer. This layer projects user inputs onto a pre-computed manifold of Canonical Intent Centroids. RAG performs vector cosine similarity but here it maps the centroids to deterministic offsets via cryptographic salt. The resulting π-key is a pointer to standardized payload from the actual datastore. By replacing direct access to the datastore via LLM with this transcendental layer, π-RAG mathematically guarantees that the inference remains oblivious to the data. This architecture unifies deterministic randomness, auditability, and differential privacy, demonstrating high efficacy for high-compliance sectors such as finance and healthcare.