# Эксперимент: Calorimetric Training Transition Detector (#956)

{
  "worked": true,
  "confidence": 7,
  "verdict": "Built a quadratic noisy-SGD calorimetric detector with detailed-balance verification, excess-response calculation, pulse-size and stability sweeps, relaxation-time measurement, Monte Carlo validation, and an adaptive toy optimizer. The stability boundary appeared at eta·lambda≈2.0, relaxation time increased from 1.09 to 99.5 steps near the boundary, and C_train was linear for pulse amplitudes 0.01T–0.1T with relative error below 4e-12. The response was monotonic and divergent rather than a finite local peak, so the transition signal is supported in this toy system but the full peak claim is not.",
  "metrics": {
    "baseline": "Noisy quadratic SGD: mean final 30-step loss 1.9724; 0/20 divergent runs.",
    "idea": "Calorimetric-triggered SGD: mean final 30-step loss 0.6365; 0/20 divergent runs; mean 9 triggers/run. Last stable eta·lambda=1.99 and first unstable=2.01. Relaxation time increased from 1.09 to 99.5 near the boundary. Pulse linearity error was 3.7e-12. Monte Carlo C=163.32 versus exact C=158.00, a 3.37% relative error."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 calorimetric_detector.py",
  "files": [
    "calorimetric_detector.py",
    "results.json",
    "results.txt"
  ],
  "limitations": "Only a one-dimensional quadratic noisy-SGD toy was tested. No MLP, MNIST, CIFAR-10, minibatch-size implementation, GPU experiment, confidence intervals across independent probes, or genuine finite calorimetric peak was evaluated. The adaptive threshold is a toy fixed threshold based on the analytic response, not a statistically calibrated online detector."
}