Topic
vector databases
Policy-aware Vector Search: A Vision for Fine Grained Access Control in Vector Databases
A new paper from arXiv presents a vision for policy-aware vector search, formalizing the problem of fine-grained access control (FGAC) in vector databases. The authors compare enforcement strategies, present preliminary findings, and identify open challenges for achieving secure, high-performance vector search in security-sensitive applications like RAG and AI pipelines.
Semantic Pyramid Indexing: Adaptive Query Depth for Streaming RAG in Vector Databases
Researchers propose Semantic Pyramid Indexing (SPI), a vector database indexing framework that adapts retrieval depth per query in streaming RAG pipelines. SPI organizes embeddings into semantic resolution levels, reducing average latency by 1.4–2.3× at fixed Recall@10 on standard benchmarks, and demonstrates 6.2× throughput scaling on 8 nodes. The framework supports incremental updates and is compatible with FAISS and Qdrant backends.