Эксперимент: Nonlinear Noise-Tightening Drift (#377)

{ "worked": true, "confidence": 9, "verdict": "Built a readable scalar cubic restoring-drift module and a reproducible verification experiment. All three mechanism predictions manifested: sampled pairwise rates stayed at or above lambda (minimum 1.000001 versus predicted 1), the small-beta relative variance slope was -1.471 versus predicted -1.5 (1.96% error), and exact stationary variance decreased strictly across beta=0 to 1. The matched Euler-Maruyama rollout also reduced variance from 0.5045 to 0.3465, a 31.3% reduction, consistent with the exact 30.97% gap at beta=0.5.", "metrics": { "baseline": "Linear drift, lambda=1, D=0.5: exact stationary variance 0.5000; Euler-Maruyama variance 0.50453 +/- 0.00046.", "idea": "Cubic drift with beta=0.5: exact stationary variance 0.34513; Euler-Maruyama variance 0.34651 +/- 0.00025; pairwise rate >=1 with rate 2.5 at x=y=1." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 run_experiment.py", "files": [ "nonlinear_drift.py", "run_experiment.py", "results.json" ], "limitations": "Only the one-dimensional analytic mechanism was tested; no learned neural residual, multidimensional latent diffusion, bounded radial variant, training task, FLOP/speed comparison, or generalization measurement was evaluated. The simulation uses Euler-Maruyama and finite burn-in, while exact variances come from stationary-density quadrature." }