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Their 1024 dimension outperforms their 2048 dimension? What am I missing here.

For float and int8, 1024 does indeed outperform 2048. However, for binary, 2048 outperforms 1024.

For our general-purpose embedding model (voyage-3-large), embedding vectors with 2048 dimensions outperform 1024 across the board: https://blog.voyageai.com/2025/01/07/voyage-3-large/

Do you have any insights into this? Perhaps scaling degradation with Matryoshka learning?