From Soft Alignment to Queries, Keys, and Values: Deriving the Transformer's Attention
The Q/K/V abstraction derived from first principles — why Bahdanau's feedforward alignment collapses to a dot product, how the Transformer formalizes queries, keys, and values as separate projections, why we scale by √d_k (with a complete variance proof), what multi-head attention adds, and a full 3-token self-attention numerical walkthrough