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Pulls the training-set-size series, snapshot-location sweep, stress
grid, scalar-gain comparison, oracle diagnostic, and derivative-probe
rows from the experiment machine, and adds three generated fragments
(train size, snapshot location, stress grid) used by the appendix.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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nested SEs
finite_time_supplement.py computes 95% cluster bootstrap intervals for
the predictor metrics (resampling forward initializations within each
cell) and renders the six-panel supplementary figure. The table
generator gains the teacher-task nested-SE fragment, computed from the
raw paired rows pulled from the experiment machine.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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generate_paper_tables.py reads the row-level CSVs and emits the four
appendix tables (initialization calibration rows, finite-time cells,
CNN calibration rows, learning-rate regimes) with the paper's nested
uncertainty convention. The fragments are included by the manuscript
via \input; no table value is transcribed manually.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Pulled from the original experiment machine. These are the authoritative
source rows behind the Appendix H tables (regime_summary matches the
paper values; the depth-3 compensated gap rounds to 0.16262).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Panel 1a: rename to Backward rules; DFA drawn as direct side-to-side
arrows from the error to each hidden layer. Panel 1b: replace the dual
y-axis with stacked guarantee/cost sub-axes. Figure 2: reader-facing
panel titles; legend renames (linear extrapolation, relinearization).
Track the regenerated figure-source CSVs (verified bit-exact against
the frozen paper numbers) so figures can be rebuilt without rerunning
experiments.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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