Fast and Efficient Approximate Nearest Neighbor Search for High-Dimensional LLM Embeddings
TL;DR
This paper addresses both $k-Nearest Neighbor Graph (kNNG) construction on 1024-dimensional BGE-M3 embeddings and Maximum Inner Product Search (MIPS) on unnormalized Llama-3.2-8B features, and introduces a 1D presorting mechanism via Fast Linear Assignment Sorting (FLAS) prior to graph construction.
Abstract
The annual SISAP Indexing Challenge benchmarks Approximate Nearest Neighbor Search (ANNS) algorithms under rigorous constraints. This paper presents our submissions for the 2026 edition, addressing both $k$-Nearest Neighbor Graph (kNNG) construction on 1024-dimensional BGE-M3 embeddings (Task 1) and Maximum Inner Product Search (MIPS) on unnormalized Llama-3.2-8B features (Task 2). To optimize construction speed, we utilize Equi-Voronoi Polytopes (EVP) for efficient quantization, supplemented by targeted reranking strategies to maintain high recall. For MIPS, we transform the asymmetric inner product problem into a Euclidean search space via dimensionality augmentation. To reduce query latency and optimize memory access, we introduce a 1D presorting mechanism via Fast Linear Assignment Sorting (FLAS) prior to graph construction. This significantly improves spatial locality and cache hit rates during subsequent graph traversal. Source Code: https://github.com/Visual-Computing/sisap26-deglib