סמנטיקה בזווית: כשדומיננטיות קוסינית עובדת עד שאינה

תקציר מקורי באנגליתarXiv:2504.16318v3 Announce Type: replace Abstract: Cosine similarity is a standard comparison rule for learned representations in information retrieval, natural language processing, computer vision, and multimodal learning. Its popularity is well founded: it removes positive radial scale, is computationally convenient, and often matches objectives that train normalized embeddings. These same properties also delimit what cosine can express. Normalization discards radial variation; anisotropic representations can compress angular contrast; high-dimensional neighborhoods can develop hubs; and a symmetric, uncalibrated score may be mismatched to the relation of interest. This article offers a selective review organized around a simple principle: the usefulness of cosine similarity depends joi