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Explain causal Attention through one query and a weighted sum - #4
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Adds /learn/attention/ with one authored causal attention row: scaled QK, future mask before Softmax, exponent normalization, weighted V and sum. Independent query/future-value experiments distinguish changing weights from masked information and token sampling. Existing mechanical sources unchanged.
Validation: npm run check including golden attention probabilities/results, masking isolation, normalization, convex bounds and reverse sampling. Managed Chrome desktop and 390×844 iframe: complete 48s playback, pause/resume/seek, keyboard steps, replay/reset, parameters, prediction quiz and reduced-motion toggle. Controlled visibility event harness paused/no auto-resume; real OS tab suspension, physical phones and measured learner gains are not claimed. Sources and evidence in SCENE_SPEC/LESSON. Catalog remains reviewed until production receipt.