AI RESEARCH
When Softmax Fails at the Top: Extreme Value Corrections for InfoNCE
arXiv CS.AI
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ArXi:2606.00262v1 Announce Type: cross InfoNCE is the standard contrastive learning objective, but its softmax form is not only a computational convenience: it also encodes a statistical assumption about how the top-scoring example is selected. Using extreme value theory, we show that this assumption is often misaligned with the normalized embedding setting used in modern contrastive learning.