Эксперимент: Time-Delay Error Adaptive Optimizer (#842)

{ "worked": false, "confidence": 9, "verdict": "Built and ran a deterministic toy verification plus a CUDA PyTorch SGD/MLP comparison. The quadratic residual prediction was confirmed exactly (observed log-log slope 2.0000 versus predicted 2.0), and interval ratios respected [1/1.35, 1.35]. However, the stated absolute update h_next=sqrt(epsilon/||r||) does not converge to the claimed sqrt(epsilon/C) target when ||r||=C h^2: it converged to 0.3758 versus the claimed 0.1387, matching the fourth-root fixed point 0.3724. The adaptive benchmark had slightly lower final loss than fixed SGD but essentially identical accuracy, so the strict mechanism criterion fails.", "metrics": { "baseline": "Fixed SGD: final loss 0.07088, accuracy 0.96971, mean interval 0.0800.", "idea": "Adaptive SGD: final loss 0.06977, accuracy 0.96975, mean interval 0.13217, interval range [0.0800, 0.1793]. Toy slope: observed 2.0000 versus predicted 2.0; observed scheduler interval 0.37582; claimed square-root target 0.13868; fourth-root fixed point 0.37239." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "Only a small synthetic two-class MLP was tested, not MNIST or larger networks. No matched-FLOP study, divergence-boundary sweep, multiple-seed statistics, or direct noisy-gradient residual-to-tolerance analysis was performed. CUDA fallback is handled for runtime errors, but the reported run used CUDA." }