# Visual contract: hardware-realistic CLLN evidence

- **Artifact:** three-panel quantitative result figure.
- **Target venue / format:** ICLR two-column paper, full-width figure.
- **Core claim:** local innovation subtraction restores nonlinear CLLN
  learning under published component imperfections and remains effective with
  nonideal local sampling.
- **Reviewer question:** does the method survive component mismatch, sampler
  mismatch, noise, stale refresh, and their combination at the task endpoint?
- **Evidence layer:** hardware-realistic simulation; no fabricated-chip claim.
- **Source data:**
  `results/physical_bias/p11_nonlinear_hardware_confirmation.json`,
  `results/physical_bias/p9_grid_correlated_autozero.json`, and
  `results/physical_bias/p8_spice_autozero_primitive.json`.
- **Statistics / uncertainty:** four device draws are averaged within each of
  40 tasks in the sampler sweep, while three new device draws are averaged in
  the nonlinear confirmation. Tasks are resampled for percentile 95%
  bootstrap intervals.
- **Figure prototype:** horizontal method comparison, mismatch response curves,
  and horizontal nonideality comparison.
- **Panel map:**
  - (a) untouched nonlinear CLLN confirmation across clean, raw, calibration,
    clean overclamping, imperfect overclamping, SDIL, and their combination;
  - (b) final error against measured local sampling-error RMSE for pedestal
    and gain mismatch sweeps;
  - (c) final error under common pedestal, sampling noise, stale neutral
    refresh, and combined nonidealities.
- **Exact label inventory:** nonlinear CLLN, component errors, sample-path
  mismatch, residual sampling error, nonideal local sampling, final
  classification error.
- **Caption role:** identify the published component-error scale, the local
  correlated-double-sampling operation, task/device counts, and the boundary
  between simulation and fabricated hardware.
- **Manuscript placement:** Part 3, after the digital scaling result.
- **Output formats:** editable SVG, vector PDF, PNG preview, analysis JSON, and
  source CSV.
- **Traceability:** every plotted aggregate is regenerated by
  `experiments/plot_physical_hardware_evidence.py`; the SPICE primitive is
  carried in the analysis manifest and reserved for supplementary evidence.
- **Constraint:** circuit-solver failures remain classification failures.
  Sampler RMSE excludes the missing diagnostic value on failed solves, while
  the task endpoint includes every trial.