# 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/p5_full_grid_bias_crossover.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; tasks are resampled for percentile 95% bootstrap intervals.
- **Figure prototype:** horizontal method comparison, mismatch response curves,
  and horizontal nonideality comparison.
- **Panel map:**
  - (a) nonlinear CLLN final error across clean, raw, calibration,
    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.