d-HNSW

Transwarp Hippo is an enterprise-grade, cloud-native distributed vector database designed for scalable vector operations, including similarity search and clustering, targeting massive datasets and real-time recommendation systems.

An efficient indexing approach for multi-tenant vector databases that handles low-selectivity filters effectively. Curator addresses the challenge of maintaining high performance when serving multiple tenants with filtered vector search queries.

A 2022 research paper presenting algorithms for faster MIPS (Maximum Inner Product Search) in high-dimensional spaces. MIPS is crucial for recommendation systems, neural networks, and various machine learning applications.

Research on using Monte Carlo Tree Search algorithms for optimizing vector index construction and search strategies. Explores adaptive decision-making during graph building and query routing.

A unified I/O orchestration framework for skewed out-of-core vector search that addresses the challenge of billion-scale ANN search when the dataset exceeds available memory. OrchANN optimizes I/O operations for graph-based indexes stored on disk.

An advanced vector compression technique for approximate nearest neighbor search that improves upon traditional product quantization by using a hierarchical pyramid structure. Published in 2026, it achieves better compression ratios while maintaining search accuracy.