# Эксперимент: Uniform Stochastic Barrier Critic (#1071)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built a stochastic 1D reach-avoid barrier MVP with an exact Bellman LP oracle, Monte Carlo expectation estimation, rollout validation, and a CUDA neural-critic comparison. The mechanism manifested: the required initial barrier increased approximately linearly with slack (slope 15.96) until a feasibility cliff between delta=0.03 and 0.04, while Monte Carlo error scaled as M^-0.511 versus the predicted M^-0.5. The neural idea improved boundary fitting and reduced initial-set values versus the baseline, but its maximum drift residual remained positive (0.217), so it was not a valid finite-sample certificate and did not demonstrate a reliable safety win.",
  "metrics": {
    "baseline": "Neural task/boundary fit: loss 0.3060, max initial B 0.2222, boundary RMSE 0.1438, max drift residual 0.2871",
    "idea": "Margin/drift critic: loss 8.0592, max initial B 0.00948, boundary RMSE 0.000114, max drift residual 0.2173; exact LP margin slope 15.96 and MC error exponent -0.511"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 barrier_experiment.py",
  "files": [
    "barrier_experiment.py",
    "results.json"
  ],
  "limitations": "Only a 1D additive-noise system and grid/quadrature transition model were tested; no learned dynamics, policy optimization, adversarial state search, high-dimensional neural barriers, held-out disturbance certificate, or statistically powered distribution-shift evaluation was performed."
}