# Эксперимент: Sign-Reset PI Optimizer (#379)

{
  "worked": true,
  "confidence": 9,
  "verdict": "Built a sign-reset PI optimizer and reproducible toy/MLP experiments in experiment.py. The no-reset Jury stability boundary matched prediction exactly at eta=2.5, and the inverse-curvature boundary sweep matched eta*=4/[lambda(2Kp-Ki)] within 0.05%. Sign reversals reset the integral state as specified; resets improved settling in parts of the stable regime and the PI model outperformed SGD on the tiny synthetic MLP, but resets destabilized near-boundary cases and were not a universal stability win.",
  "metrics": {
    "baseline": "Toy no-reset PI: stable at eta=2.4 with settling step 80, but diverged above the predicted boundary; MLP SGD final loss 0.3270, accuracy 85.35%.",
    "idea": "Toy reset PI: integral changed from 1.0 before the first reversal to 0.0 at reset; at eta=1.8 settling step 7 versus no-reset step 9; MLP PI-reset final loss 0.0121, accuracy 99.80% with 0 resets in this run. Near eta=2.4, reset diverged while no-reset settled, demonstrating the limitation."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json",
    "results.txt"
  ],
  "limitations": "The neural experiment used a small synthetic two-moons dataset rather than MNIST, tested only global sign detection, and did not measure matched FLOPs, wall-clock speed, or larger-scale generalization. The MLP run had no sign reversals, so its improvement is PI accumulation rather than evidence of reset-specific benefit."
}