# Эксперимент: Adaptive conformal safety margins (#832)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built and verified an adaptive conformal radius simulator with nonnegative projection, ensemble upper-quantile support, and a shifted crossing safety comparison. The mechanism manifested: stationary exceedance matched target alpha within 0.00009, while post-shift growth and recovery matched inverse-gamma predictions exactly across all sweeps. In the toy crossing scenario, adaptive margins reduced false-safe frames from 80/80 for a small fixed margin to 4/80 while rejecting 20 safe frames versus 120/120 for the conservative fixed margin; this is promising but not evidence of a learned-policy win.",
  "metrics": {
    "baseline": "fixed_0.20: 80 false-safe unsafe frames, 0 safe rejections, accept rate 1.00; fixed_0.80: 0 false-safe frames, 120 safe rejections, accept rate 0.00",
    "idea": "adaptive: 4 false-safe unsafe frames, 20 safe rejections, accept rate 0.52, mean radius 0.4528; exceedance absolute error <= 0.00009; ramp and recovery relative errors 0.0; ensemble quantile Q_0.75([0.2,0.4,0.8,1.0])=0.85"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json"
  ],
  "limitations": "Only a synthetic one-dimensional crossing proxy was tested; no neural predictor, PPO or policy training, multi-agent trajectories, delayed FIFO prediction buffer, computational-cost measurement, formal finite-sample conformal guarantee, or realistic distribution shifts were evaluated."
}