Эксперимент: Barrier-Temperature Matching (#1233)

{ "worked": false, "confidence": 8, "verdict": "Built a reproducible Barrier-Temperature Matching MVP with harmonic-mean temperature verification, controller log-contraction verification, parallel Langevin double-well escape testing, and a CUDA-safe noisy-MLP comparison. The toy checks support the claimed math: the controller contracts log(r) by 0.92 for kappa=0.08, and double-well escape rates show a clear negative Arrhenius slope of -0.9997. However, the MLP controller did not drive measured ratios near one (median r≈2.88), and its final loss was essentially unchanged/slightly worse than fixed noise (0.54233 vs 0.54236; mean final-100 loss 0.55855 vs 0.55532), so the optimizer-level win was not demonstrated.", "metrics": { "baseline": "Fixed-temperature Langevin SGD: final loss 0.542356, accuracy 0.8086, mean last-100 loss 0.555320, temperature 0.0025.", "idea": "Barrier controller: final loss 0.542330, accuracy 0.8457, mean last-100 loss 0.558551, measured r median 2.88, final temperature 0.005385." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 barrier_temperature_mvp.py", "files": [ "barrier_temperature_mvp.py", "results.json" ], "limitations": "Only one small synthetic 2D classification task and one fixed seed were tested; no CIFAR-10, Adam/preconditioned covariance experiment, multiple independent runs, probe-path alternatives, or equal-FLOP statistical comparison was performed. The escape-rate check verifies Arrhenius behavior in a one-dimensional analytic double well, not neural-network basin transitions." }