# Эксперимент: Ultra-Local Neural Safety Shield (#926)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built an online ultra-local estimator with filtered finite-difference updates, residual uncertainty envelope, actuator clipping, and robust one-dimensional barrier projection. The mechanism checks passed: SGD reduced parameter error from 2.156 to 2.13e-5, the measured uncertainty boundary shift matched Delta/kc with 1.85e-16 maximum relative error, and the rejection transition matched the predicted Delta threshold of 0.10. In the toy controller, robust shielding reduced violation rates to 0.0 at disturbance amplitudes 0.4, 0.8, and 1.2, versus 0.266, 0.264, and 0.242 for the nominal shield; this is a promising toy result, not evidence of general neural-policy performance.",
  "metrics": {
    "baseline": "Unshielded violation rates for disturbance amplitudes 0.0/0.4/0.8/1.2: 0.800/0.754/0.724/0.704; nominal shield: 0.000/0.266/0.264/0.242.",
    "idea": "Robust shield violation rates: 0.000/0.000/0.000/0.000 for amplitudes 0.0/0.4/0.8/1.2; intervention rates: 0.896/0.806/0.668/0.562; mean estimated envelopes: 0.115/0.366/0.714/1.107."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 shield_experiment.py",
  "files": [
    "shield_experiment.py",
    "results.json"
  ],
  "limitations": "Only a deterministic scalar toy simulation was tested; no neural network, PPO/SAC training, multidimensional navigation, observation noise sweep, actuator infeasibility stress test, or comparison of computational overhead was evaluated. The uncertainty envelope uses a deliberately simple recent-residual heuristic and its conditional safety guarantee was not formally validated under estimator lag or envelope under-coverage."
}