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NaN poisoning: inject NaN where info must not come from (future/test/labels), run the real pipeline, assert past outputs stay finite. Documents false negatives (pandas skipna, nanmean) and false positives (softmax rows, batch stats). Backprop-to-input is its gradient dual for inside the model; quote already frozen in docs/evidence/karpathy_recipe_training_nn_2019.md. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>