Google paper shows single-vector embeddings need MUVERA-level dimensions to match multivector similarity
_reachsumit · x · 2026-07-23
Researchers at Google prove a near-optimal lower bound for single-vector embeddings when approximating maximum inner product similarity. Their result shows that a single-vector representation needs a dimension as large as MUVERA’s known upper bound to match multivector similarity, tightening the theoretical gap around this approximation problem.
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