Эксперимент: Turnpike-Calibrated Short-Window Training (#1212)

{ "worked": false, "confidence": 9, "verdict": "Built a reproducible scalar state-space verification, fixed/free initial-state calibration, horizon sweep, and training comparison in run_experiment.py. The analytic toy system clearly shows bounded cumulative discrepancy and mean gap scaling near 1/N (log-log slope -0.959), while the learned-model sweep is approximately decreasing (slope -0.767). However, strict calibration with epsilon=.05 selected the full horizon 40 and gave essentially the same MSE as full training (0.00710 vs 0.00701); relaxed calibration selected 27 but substantially worsened MSE to 0.05865, so no practical speedup with preserved performance was observed.", "metrics": { "baseline": "Full horizon 40: MSE 0.007013; fixed horizon 8: MSE 0.024971; empirical gap sweep slope -0.767.", "idea": "Calibrated epsilon=.05: selected horizon 40, MSE 0.007104, mean fixed/free gap 0.1233. Relaxed epsilon=.20: selected horizon 27, MSE 0.05865, mean gap 0.0972." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 run_experiment.py", "files": [ "run_experiment.py", "results.json", "run.log", "run2.log" ], "limitations": "Only a scalar linear state-space model and one fixed random seed were tested; no GRU, Duffing, Lorenz-63, minibatch dataset, wall-clock/FLOP measurement, multi-sequence calibration, or statistical repeats were included. The free-initial-state optimization is a small approximate inner solve rather than an exact global optimum." }