MEND — Model Editor Networks using Gradient Decomposition

Exploits the fact that a per-example fine-tuning gradient of a dense layer's weight matrix is itself a rank-one outer product (upstream-gradient vector ⊗ input-activation vector), and learns small per-layer hypernetworks that transform each of these two rank-one factors independently, producing a new rank-one weight update — an outer-product/direction mechanism preserved end-to-end, without any claim about pre-existing linear geometry in the edited model.