Beyond Cosine: When Order Beats Angle in Embedding Similarity
Opening — Why this matters now Cosine similarity has enjoyed an unusually long reign. From TF‑IDF vectors to transformer embeddings, it remains the default lens through which we judge “semantic closeness.” Yet the more expressive our embedding models become, the more uncomfortable this default starts to feel. If modern representations are nonlinear, anisotropic, and structurally rich, why are we still evaluating them with a metric that only understands angles? ...