Some snippets:

> Leverages concentration of measure phenomena

> Uses anisotropic vector quantization to optimize inner product accuracy by penalizing errors in directions that impact high inner products, achieving superior recall and speed.

I only skimmed the article, but the 2 words I emphasized seems to imply they apply a quadratic metric for distance, i.e. they assume the data coordinates are with respect to non-orthogonal basis vectors, resulting in off-diagonal distance metric terms.