# Эксперимент: Martingale Response Control Variate (#998)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built a reproducible NumPy MVP of the martingale residual controller with exact response-weighted scaling, budget clipping, and a scalar stochastic optimization comparison. The mechanism manifested: response power scaled as response^2 (predicted slope 2.0, observed 2.002), the budget law matched to 2.2e-13 absolute error, and the preserved-drift stability boundary matched the predicted eta=2.0 transition (last finite 1.9, first divergent 2.05). The controller reduced mean late loss from 0.0398 to 0.00305, but this toy cannot demonstrate a shifted deterministic stability boundary; measured sample correlation between q and squared response residual was only 0.428, below the proposed >0.7 target.",
  "metrics": {
    "baseline": "SGD: mean late-1000 loss 0.0397659; final loss mean 0.0336985; response-power variance 1.1854",
    "idea": "Response control: mean late-1000 loss 0.00305033; final loss mean 0.00303116; controlled response power follows mean(min(q,B)) exactly; 13.1x lower late loss"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 martingale_response.py",
  "files": [
    "martingale_response.py",
    "results.json",
    "run_output.txt"
  ],
  "limitations": "This is an exact-information scalar toy, not a GRU, recurrent sequence, RL, Adam, or global clipping experiment. R and covariance are supplied analytically rather than estimated from rollouts, compute/FLOP overhead and virtual future-loss response estimation were not measured, and the proposed >0.7 empirical variance-correlation target was not achieved."
}