# Эксперимент: Displacement-Huber distribution pooling (#883)

{
  "worked": true,
  "confidence": 9,
  "verdict": "Built a NumPy displacement-Huber quantile pooling module with a safeguarded Newton/bisection solver and numerical checks. All three mechanism predictions were confirmed: the [0,0,T] saturation transition occurred at T=1.5 as predicted, maximum curve error was 5.6e-17, inlier averaging error was 0, and outlier score influence was capped at 1/3. In the toy classifier, Huber pooling did not improve corruption accuracy (47.75% vs 47.875% for mean pooling), while costing about 30.5x more, so the mechanism works but this MVP showed no practical task-level win.",
  "metrics": {
    "baseline": "Arithmetic pooling: clean accuracy 1.0000, 50% corruption accuracy 0.47875, degradation 0.52125, 0.03035 ms/sample",
    "idea": "Displacement-Huber pooling: clean accuracy 1.0000, 50% corruption accuracy 0.47750, degradation 0.52250, 0.92541 ms/sample, 30.49x arithmetic-pooling overhead; mechanism maximum saturation error 5.55e-17"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json"
  ],
  "limitations": "Only a synthetic nearest-centroid classification task was tested; no CIFAR/text model, learned attention weights, gradient norms, GPU implementation, or ordinary Wasserstein baseline was evaluated. The safeguarded solver uses 12-20 iterations rather than the proposed fixed five Newton steps because the raw update diverges when all residuals are in the Huber linear region."
}