Эксперимент: Progressive rollout-consistency training (#9)

{ "worked": false, "confidence": 9, "verdict": "Built differentiable RK4 rollout training with L1 regularization, progressive horizons, gradient clipping, synthetic noisy nonlinear oscillator data, and long-horizon evaluation. The corrected numerical sanity check confirmed fourth-order RK4 scaling (error ratios 17.09 and 16.59 under timestep halving), but at approximately equalized training evaluations the one-step baseline was better at every horizon: 64-step MAE 0.04368 versus 0.07336 for progressive training. No trajectories diverged in either method, so the promised forecasting/stability win was not observed in this MVP.", "metrics": { "baseline": "MAE at horizons 8/16/32/64: 0.01121/0.01994/0.03112/0.04368; max norm 1.586; divergence rate 0.0", "idea": "MAE at horizons 8/16/32/64: 0.01759/0.03150/0.05122/0.07336; max norm 1.591; divergence rate 0.0; RK4 halving ratios 17.09 and 16.59" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "Only one seed and one stable 2D synthetic oscillator were tested; no pendulum, Van der Pol, Lorenz, multiple noise levels, hyperparameter tuning, wall-clock/FLOP profiling, sparse-model recovery, or statistical significance analysis was performed." }