arXiv AI July 21, 2026 By Navnit Shukla, Kamal Pandey, Omsankar Tiwari

TurboVec: A Case Study in Cost-Efficient Private Retrieval for Enterprise RAG via Codebook-Oblivious Quantization

arXiv:2607. 16973v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems increasingly power enterprise LLM applications, yet the vector retrieval layer introduces two underexplored challenges: (1) trained codebook quantizers may expose corpus statistics during index construction, creating a leakage channel in multi-tenant deployments, and (2) post-hoc filtering for tenant isolation degrades recall on selective queries.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 15

Cost-Governed RAG: Unified Per-Tenant Cost Attribution Across Retrieval and Generation in Multi-Tenant LLM Systems

arXiv:2607. 12188v1 Announce Type: new Abstract: Enterprise Retrieval-Augmented Generation (RAG) deployments face a critical governance gap: while LLM generation cost is metered per token, the retrieval layer - vector memory, similarity compute, and embedding API calls - remains an unattributed shared cost, enabling invisible cross-subsidization among tenants.

By Navnit Shukla

llms rag efficiency

arXiv AI

Aug 6

SSTQ:Privacy-Preserving Vector Quantization via Subsampled Stochastic TurboQuant

arXiv:2608. 05127v1 Announce Type: cross Abstract: Achieving local differential privacy in distributed optimization while maintaining low communication cost remains challenging.

By Adel Javanmard, David P. Woodruff, Vahab Mirrokni

efficiency safety

arXiv Machine Learning

Jun 11

Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite

arXiv:2606. 11257v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) pipelines are compute-intensive, combining embedding, retrieval, reranking, and large language model (LLM) generation.

By Zhiyuan Cheng, Longying Lai

llms rag efficiency benchmarks safety

arXiv AI

Aug 3

GPU-Accelerated ANNS: Quantized for Speed, Built for Change

arXiv:2601. 07048v5 Announce Type: replace-cross Abstract: Approximate nearest neighbor search (ANNS) is a core problem in machine learning and information retrieval applications.

By Hunter McCoy, Zikun Wang, Prashant Pandey

efficiency

arXiv Machine Learning

Jul 9

Fractal KV-Cache Archives: Lossless Symbolic Storage with In-Place Retrieval for Long-Context LLM Inference

arXiv:2607. 07144v1 Announce Type: new Abstract: The key-value (KV) cache dominates the memory cost of long-context autoregressive inference, and a growing body of work compresses it through quantization, eviction, or offloading.

By Vladimir Gusev

llms efficiency

arXiv AI

1d ago

Static Pruning Across Sparse Retrieval Regimes: What Transfers, What Breaks, and What Still Helps

arXiv:2608. 16309v1 Announce Type: cross Abstract: Static pruning is widely used to accelerate sparse neural retrieval, yet existing studies each validate their conclusions within a single custom pipeline, leaving it unclear which findings transfer to modern engines with different index organizations and dynamic pruning mechanisms.

By Zirui Song, Yuye Zhu, Yang Yang

efficiency benchmarks

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Cost-Governed RAG: Unified Per-Tenant Cost Attribution Across Retrieval and Generation in Multi-Tenant LLM Systems

SSTQ:Privacy-Preserving Vector Quantization via Subsampled Stochastic TurboQuant

Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite

GPU-Accelerated ANNS: Quantized for Speed, Built for Change

Fractal KV-Cache Archives: Lossless Symbolic Storage with In-Place Retrieval for Long-Context LLM Inference

Static Pruning Across Sparse Retrieval Regimes: What Transfers, What Breaks, and What Still Helps