# Эксперимент: Proximal-Mismatch Fine-Tuning (#397)

{
  "worked": true,
  "confidence": 9,
  "verdict": "Built and ran a reproducible scalar PnP proximal-mismatch experiment in experiment.py. All three quantitative predictions were confirmed: proximal coefficient error was 3.3e-16, mismatch scaling had log-log slope 2.000 versus predicted 2, zero-regularization mismatch was 0, and the stability transition bracketed the predicted boundary a=2.0 between 1.99 and 2.01. Proximal matching also beat clean-MSE in this exactly solvable quadratic toy, but this does not establish real-image performance.",
  "metrics": {
    "baseline": "Clean-MSE coefficient=0.68604; final F=0.4410949; final stationarity=0.5887514; MSE to exact optimum=0.0581222; proximal mismatch=0.0129127",
    "idea": "Proximal-match coefficient=0.454545; final F=0.3539115; final stationarity=2.01e-16; MSE to exact optimum=8.12e-33; proximal mismatch=9.81e-34. Mechanism checks: coefficient max error=3.33e-16, mismatch slope=2.000, predicted stability boundary=2.0, observed bracket=[1.99,2.01]."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json"
  ],
  "limitations": "No image denoiser, real BSD/medical data, learned nonlinear network, approximate teacher, noisy inverse operator, SSIM, GPU training, or few-shot generalization was tested. The proximal teacher was analytically known, so the practical gain is evidence only for the isolated quadratic mechanism."
}