Paper by Tepper et al. proposing GleanVec, a method to accelerate vector search using minimalist nonlinear dimensionality reduction. Improves efficiency for high-dimensional vector queries.

Open-source comparative benchmarks evaluating vector search performance of engines like Qdrant, Elasticsearch, Milvus, Redis, and Weaviate. Covers single-node upload/search, filtered search across various datasets and configurations, focusing on RPS, latency, precision, and indexing time using affordable hardware.

Research paper presenting OneSparse, a unified system for multi-index vector search. Published at the Companion Proceedings of the ACM on Web Conference 2024, it addresses the challenge of efficient vector search across multiple index structures.

SC'25 Workshop paper characterizing Qdrant vector database performance on high-performance computing platforms, bridging AI and HPC workloads.

Research paper proposing a decoupled storage architecture for vector databases that improves update speed by 10.05x for insertions and 6.89x for deletions through innovative design.

An OpenSearch plugin that expands its capabilities with the custom `knn_vector` data type, enabling storage of embeddings and providing methods for k-NN similarity searches, including Approximate k-NN, Script Score k-NN, and Painless extensions.