CLIP
CLIP and the modality gap: why image and text embeddings sit in separate cones, and when the gap is a choice rather than a bug.
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Untangling the Moons: A Visual History of Contrastive Learning
Eight contrastive losses, twenty years of history, one interactive playground. Watch pair, triplet, InfoNCE, CLIP, SupCon, SigLIP, alignment+uniformity, and cosine→0 organize 2D points, and see which ones know when to stop.
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Not All Infinities Are Equal: The Cross-Entropy Asymmetry Behind Hallucination
The singularity structure of cross-entropy is asymmetric, and that asymmetry explains LLM hallucination, the CLIP modality gap, and why contrastive losses need 32K batches.
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Opposite Is Not Different: The Cosine-Similarity Bug in CLIP and Contrastive Learning
Maximum difference between two unit vectors is orthogonality (cos = 0), not opposition (cos = −1). CLIP, InfoNCE, and SimCLR have been optimizing for the wrong target for years.