Эксперимент: Horizon-Adaptive Neural Tube Rollouts (#1092)

{ "worked": true, "confidence": 9, "verdict": "Built an exact axis-aligned neural tube recursion with autograd Jacobians, robust constraint backoff, and a matched small neural state-space comparison. The toy verification confirmed all three mechanism predictions: the stability boundary was observed at gamma=1.25 (=1/lambda), unstable growth slopes matched log(gamma·lambda) within 0.02%, and steady-state radius scaled linearly with disturbance magnitude to numerical precision. The tube regularizer did not improve this particular neural task: rollout MSE was essentially unchanged and constraints were never violated by either model, so the mechanism is validated but a practical win was not demonstrated.", "metrics": { "baseline": "one-step MSE 0.00169880; 20-step rollout MSE 0.12039916; violation rate 0.0", "idea": "one-step MSE 0.00169907; 20-step rollout MSE 0.12040418; violation rate 0.0; toy boundary gamma 1.25 predicted/observed; unstable slope relative error 0.014% at q=1.04 and approximately 0% at q=1.20; disturbance-scaling relative error 2.5e-16" }, "how_to_run": "python3 tube_experiment.py", "files": [ "tube_experiment.py", "results.json" ], "limitations": "The neural comparison used a tiny synthetic stable plant, fixed uncertainty radii rather than learned held-out 99th-percentile residuals, one-step tube regularization rather than full horizon-adaptive training, and no genuinely active constraints. GPU execution, CartPole, policy closed-loop Jacobians, and larger or harder long-horizon tasks were not tested." }