ml-primitive-decoder

Decomposes any ML construct (attention, layer norm, softmax, convolution, dropout, contrastive loss, diffusion step, gradient descent update, cross-entropy) into the small set of linear-algebra primitives it's built from, then explains why those primitives produce the observed behavior. Includes an ablation thought experiment that asks "what would break if you removed this piece?". Use when the user asks "why does X work?", "explain attention/conv/norm intuitively", "what's actually happening in this layer", or when reading an architecture paper and a particular block doesn't make sense yet.