Matryoshka truncation

Assess

Techniques

A vector truncation approach that reduces embedding dimensionality while preserving useful signal.

Why it's here

Placed in Assess: 1 article(s) of evidence from 1 source(s), led by model releases, with 1 in the last 30 days. Confidence 24%. Low accumulated evidence, so it defaults conservatively pending more signal.

Evidence (1)

6The New Stack·9/30/2026model_release
Cohere's Embed 5 fast query model slightly lowers retrieval quality
Cohere released Embed 5, letting teams index data with Embed 5 Pro and query the same vectors with the faster Embed 5 Fast without re-embedding. In Cohere's tests, Fast queries against a Pro index reached 98.4% of the Pro-to-Pro retrieval baseline, while using Fast for both indexing and queries scored 96.6%.