# Эксперимент: Lipschitz-Free Metric Pooling (#1310)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built a reproducible Lipschitz-free probe pooling MVP with finite-metric LP verification and a matched-parameter toy classification experiment. The exact distance-to-anchor probe was valid in all 30 trials and matched the exact dual free norm; across five seeds, probe pooling improved clean accuracy from 0.497 to 0.691 and retained 0.644 accuracy after removing 50% of items versus 0.496 for mean pooling. This is a clear signal on the constructed synthetic task, but not evidence of broad superiority.",
  "metrics": {
    "baseline": "Mean pooling: 42 parameters, clean accuracy 0.4969±0.0245, 50% removal accuracy 0.4957±0.0193",
    "idea": "Lipschitz probe pooling: 42 parameters, clean accuracy 0.6910±0.0496, 50% removal accuracy 0.6438±0.0403; exact math check valid-probe fraction 1.0 and probe/exact norm ratio 1.0"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.txt"
  ],
  "limitations": "Only a small synthetic 2D radial/angular classification task was tested. The experiment did not test ModelNet40, retrieval, nonlinear metric distortions, learned attention/router weights, explicit pairwise Lipschitz penalties, or comparisons against DeepSets and attention pooling."
}