Эксперимент: Bidirectional Saturation-Aware Trust Region (#1162)

{ "worked": true, "confidence": 8, "verdict": "Built a global-radius saturation-aware SGD trust-region controller with hard update safety bounds, exponential bidirectional radius adaptation, and deterministic toy/MLP tests. The formula-level check observed expansion under saturated proposals and contraction under unsaturated proposals, and the quadratic descent inequality passed numerically. In the matched MLP run, adaptive clipping reduced clipping frequency from 97.8% to 6.0% and improved final loss (0.2647 vs 0.2904), but final accuracy was slightly lower (92.7% vs 94.0%), so this is a promising optimization signal rather than an across-the-board accuracy win.", "metrics": { "baseline": "Fixed global clip radius 0.055: final loss 0.2904, final accuracy 93.96%, clip fraction 97.8%, mean update scale 0.435, final radius 0.055.", "idea": "Adaptive bidirectional radius: final loss 0.2647, final accuracy 92.71%, clip fraction 6.0%, mean update scale 0.988, radius 0.055 -> 0.428; toy radius 0.100 -> 0.190 during saturation -> 0.129 during unsaturation." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "Only one deterministic synthetic 2D classification task and one seed were tested; no MNIST/CIFAR-10 or ResNet comparison, Adam/AdamW comparison, curvature-adaptive r_max, FLOP/speed measurement, or multi-seed statistical evaluation was performed. The implementation uses a single global radius and does not explicitly place tensors on CUDA." }