# 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.