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The paper identifies a misalignment between the softmax-based InfoNCE loss and the normalized embedding setting in modern contrastive learning. It proposes WEINCE, a simple modification that blends softmax logits with an endpoint shortfall correction using extreme value theory, yielding consistent improvements across vision benchmarks.