# Эксперимент: Frozen-threshold Adam controller (#1009)

{
  "worked": true,
  "confidence": 9,
  "verdict": "Built a frozen-threshold Adam controller with exact frozen-dynamics verification and a matched small MLP experiment. The mechanism manifested: measured stability boundaries were within 0.05% of the predicted w=2 boundary, all 12 curvature/second-moment scaling tests had measured/predicted ratios of 1.0, and supercritical controller updates mapped w=2, 3, and 8 exactly to the 1.8 safety target. In the MLP, w stayed subcritical with maximum 0.0824, so the controller never reduced the learning rate and produced identical results to baseline; this confirms the toy mechanism but not a practical training win.",
  "metrics": {
    "baseline": "MLP: final loss 0.1510, mean last-50 loss 0.1396, accuracy 96.92%, loss maximum 6.2950, fixed learning rate 0.08",
    "idea": "Toy: frozen-boundary relative error 0.05% at all tested settings; scaling ratios 1.0 across 12 tests; controller maps supercritical w to target 1.8. MLP: final loss 0.1510, mean last-50 loss 0.1396, accuracy 96.92%, maximum logged w 0.0824, final rates unchanged at 0.08"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json",
    "run_output.txt"
  ],
  "limitations": "Only one fixed-seed, 300-step synthetic MLP run was tested; no CIFAR-10, multiple seeds, AdamW weight decay, global clipping comparison, FLOP-matched larger-learning-rate sweep, or divergence-rate study. The neural sharpness estimate used one random Hessian-vector probe per check, and the controller remained inactive because the observed diagnostic was far below threshold."
}