When Softmax Fails at the Top: Extreme Value Corrections for InfoNCE 事件

When Softmax Fails at the Top: Extreme Value Corrections for InfoNCE arXiv:2606.00262v1 Announce Type: cross Abstract: 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. Motivated by this mism