Microsoft proves multi-vector embeddings exponentially more compact for retrieval ranking
_reachsumit · x · 2026-08-25
Microsoft researchers formally prove that multi-vector embeddings can be exponentially more compact than single-vector ones for ranking documents. They construct the first explicit family of query-document sets where single-vector embeddings require exponential size, while polynomial-size multi-vector embeddings suffice. They introduce the ANDOR benchmark, showing SOTA single-vector models perform poorly even after fine-tuning, while multi-vector models improve substantially, aligning with theory.
Related event: Microsoft paper proves multivector retrieval exponentially more efficient(3 posts)→
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