# Эксперимент: Online Effective-Ridge Correction (#1260)

{
  "worked": false,
  "confidence": 8,
  "verdict": "Built an online effective-ridge controller with covariance and cross-moment EMAs, logarithmic ridge-path fitting, smoothing, gating, and head-gradient correction. The math check recovered the planted lambda 0.37 as 0.3736 with 0.0036 absolute error, confirming the estimator when the iterate is at a ridge fixed point. However, on the five-seed adversarial Gaussian regression test, controller robust loss was 0.047963 versus 0.047970 for adversarial SGD, while robust-loss seed variance was slightly worse (0.000648 versus 0.000635); this is effectively no win.",
  "metrics": {
    "baseline": "Adversarial SGD: mean clean loss 0.011746, robust loss 0.047970, robust-loss std 0.000635, weight norm 0.9636. Fixed weight decay: clean loss 0.012527, robust loss 0.048304, robust-loss std 0.000573.",
    "idea": "Online controller: mean clean loss 0.011751, robust loss 0.047963, robust-loss std 0.000648, weight norm 0.9633; final fitted lambda values across seeds were approximately 0.0288-0.0375."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 online_ridge.py",
  "files": [
    "online_ridge.py",
    "results.json",
    "report.md"
  ],
  "limitations": "Only a small linear Gaussian regression benchmark was tested, not CIFAR-10 or a two-layer MLP. The experiment used one adversarial radius, one controller hyperparameter setting, five seeds, 500 optimization steps, and did not measure late robust-loss increases, matched clean accuracy, or computational overhead. The planted-ridge sanity check assumes the iterate is exactly at a ridge solution, so it does not validate fixed-point approximation error during realistic transient training."
}