# Эксперимент: Reverse-Protocol Entropy Controller (#1032)

{
  "worked": false,
  "confidence": 9,
  "verdict": "Built an explicit Gaussian reverse-protocol entropy estimator for a discrete Ornstein-Uhlenbeck Langevin chain, plus an entropy-controlled Langevin optimizer on a tiny regression problem. The math sanity checks confirmed zero EP for a constant equilibrium protocol and approximately quadratic EP scaling with protocol amplitude (log-log slope 2.14). However, the slow-ramp sweep produced slope -0.87 rather than the predicted positive 2, and the adaptive controller had higher final loss than baseline (0.0867 vs 0.0748), so the full proposed mechanism was not demonstrated.",
  "metrics": {
    "baseline": "Toy: constant-protocol mean EP 3.9e-18; optimizer final loss 0.07479, best loss 0.02477, fixed eta 0.035.",
    "idea": "Toy: amplitude EP slope 2.14; slow-ramp EP slope -0.87 (predicted +2); adaptive optimizer final loss 0.08668, best loss 0.02440, eta increased to 0.070."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 run_experiment.py",
  "files": [
    "run_experiment.py",
    "results.json",
    "results.csv",
    "run_output.txt",
    "run_output2.txt"
  ],
  "limitations": "Only a harmonic OU toy and a tiny full-batch quadratic regression task were tested; no MNIST/CIFAR, minibatch gradients, momentum/Adam, endpoint-density estimation, shadow reverse optimizer, or FLOP/runtime comparison was performed. The optimizer controller uses a short-window approximate path ratio rather than a fully sampled reverse trajectory."
}