feat: land superpaper v1 notes scaffold
Add the ledger schema, class router, lint codes, ingest/build pipeline, and three work-tree examples: align derivation, superfig delegation, and supertensor delegation.
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# A One-Step Predictor (fixture)
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We predict $\hat y = f_\theta(x)$ in one forward pass. The loss
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$L(\hat y, y)$ is computed after the prediction; it is not a station
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on the forward path.
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The scale $1/\sqrt{d}$ is introduced so that the variance of the
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inner product does not grow with $d$.
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