Anchor Divergence for Semantic Geometry in Contrastive Learning

Excerpt

arXiv:2610.06919v1 Announce Type: cross Abstract: This paper concerns how semantic context determines geometry in learned vector representations. Similarity is typically measured using cosine similarity, which provides a single fixed geometry. Semantic similarity, however, is inherently context dependent: two images may be similar because they depict the same object, share a visual style, or are relevant to the same clinical finding. We show that contrastive representations naturally encompass a