NeurIPS 2023 paper proposing SOAR, a method for improved indexing in approximate nearest neighbor search, focusing on better space partitioning and search optimization.

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.

An ultra-scalable graph-based nearest neighbor indexing algorithm that builds state-of-the-art indexes up to 11.6× faster than Vamana (DiskANN) and 12.9× faster than HNSW. PiPNN uses HashPrune, a novel online pruning algorithm that enables efficient billion-scale index construction on a single machine.

A research paper on accuracy-preserving index construction for distributed vector search systems. Published in 2025, it addresses the challenge of maintaining search quality while distributing vector indexes across multiple nodes.

Research on maintaining balanced, high-quality graph indexes while streaming data arrives continuously. Addresses the challenge of index degradation over time with incremental updates.

Indexing strategy where vector indexes are stored in the same partitions as corresponding table rows, ensuring data locality and operational advantages in distributed databases.