Monte Carlo Tree Search for Vector Indexing

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.

A 2026 research paper presenting graph-based algorithms for diverse similarity search, where results must be both similar to the query and diverse from each other. This addresses the common problem of redundant results in traditional similarity search.

A 2026 research paper on streaming approximate nearest neighbor search with in-place graph index updates. The approach enables real-time index modifications without expensive rebuilds, crucial for dynamic datasets.

Joint Attribute Graphs for Filtered Nearest Neighbor Search, a research paper that addresses the challenge of combining vector similarity search with attribute filtering. JAG presents a novel index structure that efficiently handles filtered ANN queries common in real-world applications.

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 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.