Эксперимент: Semi-Passive Energy-Gated Optimizer (#1070)

{ "worked": true, "confidence": 9, "verdict": "Built the semi-passive energy-gated optimizer with explicit velocity, energy, logistic damping gate, toy dynamical verification, and a controlled digits MLP benchmark. The mechanism manifested: q(E) transitioned at E*=2 with the predicted 10–90% width, active unforced decay matched slope -2c within 0.08%, and forced active-regime plateaus matched G²/(2c²) essentially exactly. The optimizer did not improve this benchmark: at lr=0.3 momentum reached 98.22% accuracy versus 97.56% gated, although gating reduced maximum energy from 0.521 to 0.389.", "metrics": { "baseline": "Momentum SGD: lr 0.03/0.1/0.3 accuracy 97.33%/95.78%/98.22%; max energy 0.0073/0.0549/0.5211.", "idea": "Gated optimizer: lr 0.03/0.1/0.3 accuracy 97.11%/94.44%/97.56%; max energy 0.0068/0.0313/0.3891. Toy gate width relative error <1e-11; decay relative errors 0.01%-0.08%; forced plateau relative errors <=3.3e-5." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "The benchmark used sklearn digits rather than full MNIST, one fixed seed, three learning rates, and 300 updates. It did not test the proposed online exponential moving maximum estimate of B, Adam integration, larger networks, multiple seeds, or sustained pathological divergence regimes." }