# Эксперимент: Truncated Volterra Stabilizer for Recurrent Blocks (#1244)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built a scalar causal ordered-lag Volterra compensator supporting summed quadratic and cubic kernels, plus a deterministic nonlinear stable-plant experiment. The math check gives a log-log slope of 1.000 for the omitted cubic remainder divided by the retained quadratic term, and the 1000-step sweep shows bounded initial amplitude increasing from 0.425 (baseline) to 1.083 (quadratic) to at least 3.0 (cubic), with divergences falling from 23 to 12 to 0. This is a clear promising signal for the claimed local/practical robustness effect, but it is only a toy scalar simulation rather than evidence of a neural-network win.",
  "metrics": {
    "baseline": "23/60 divergent rollouts; largest bounded initial amplitude 0.4254; max finite state norm 0.4228; 0 compensator parameters",
    "idea": "Quadratic: 12/60 divergent, bounded amplitude 1.0827, max finite norm 1.0598, 1 kernel parameter. Cubic: 0/60 divergent, bounded amplitude >=3.0, max finite norm 2.1556, 2 kernel parameters. Remainder/quadratic log-log slope: 1.000."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 volterra_experiment.py",
  "files": [
    "volterra_experiment.py",
    "results.json"
  ],
  "limitations": "Only a scalar polynomial discrete plant with fitted coefficients was tested; no vector hidden state, input-driven sequence prediction, MLP residual baseline, Lorenz-63, Duffing oscillator, GPU timing, parameter-matched training, noise robustness, or comparison at equal FLOPs was evaluated. The cubic range is reported as a lower bound because all tested amplitudes up to 3.0 remained bounded."
}