GEM is the first native graph-based index for multi-vector (ColBERT-style) retrieval, achieving up to 16x speedups over existing single-vector index adaptations.
March 24, 2026
Original Paper
GEM: A Native Graph-based Index for Multi-Vector Retrieval
arXiv · 2603.20336
The Takeaway
Multi-vector retrieval is highly accurate but computationally expensive to index. GEM builds proximity graphs directly on vector sets and uses quantized distance estimation, making high-fidelity multi-vector retrieval practical for large-scale, low-latency applications.
From the abstract
In multi-vector retrieval, both queries and data are represented as sets of high-dimensional vectors, enabling finer-grained semantic matching and improving retrieval quality over single-vector approaches. However, its practical adoption is held back by the lack of effective indexing algorithms. Existing work, attempting to reuse standard single-vector indexes, often fails to preserve multi-vector semantics or remains slow. In this work, we present GEM, a native indexing framework for multi-vect