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# Visual contract: digital CLLN scaling pilot
- **Artifact:** three-panel quantitative result figure.
- **Target venue / format:** ICLR two-column paper, full-width figure.
- **Core claim:** local innovation subtraction preserves digital coupled
learning under fixed component imperfection as the lattice grows.
- **Reviewer question:** does SDIL retain a task-level advantage at large
system size, and is the result stronger than static calibration or matched
zero-mean noise?
- **Evidence layer:** main scaling result, currently labelled exploratory.
- **Source data:**
`results/coupled_ladder/p1_imperfection_pilot.json` and
`results/coupled_ladder/p1_bias_baseline_pilot.json`.
- **Statistics / uncertainty:** component draws are averaged within task;
tasks are the bootstrap unit. The pilot has five task clusters and one
component draw per task and size. Error bars are percentile 95% intervals.
- **Figure prototype:** coordinated small-multiple lines.
- **Panel map:**
- (a) final classification error against learnable edge count;
- (b) classification-error area over the full training trajectory against
learnable edge count;
- (c) fraction of runs that fail to reach zero error and remain there through
the training horizon against learnable edge count.
- **Exact label inventory:** Clean, same-RMS noise, raw imperfection, static
calibration, SDIL, learnable edges, classification error, classification-
error AUC, stable failure fraction.
- **Caption role:** state the paired protocol, distinguish fixed bias from
matched noise, and label the evidence as a five-task pilot.
- **Manuscript placement:** Part 2, immediately after the digital CLLN and
component-imperfection definitions.
- **Output formats:** editable SVG, vector PDF, PNG preview, analysis JSON,
and source CSV.
- **Traceability:** every plotted aggregate is regenerated by
`experiments/analyze_coupled_ladder_scaling.py` from the two JSON files.
- **Constraint:** the figure reports an error-growth slope comparison only
when the static-calibration slope is positive. Raw imperfection is allowed
to show a high non-monotonic floor rather than a forced power law.
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