Эксперимент: Lag-Compensated Spectral Scheduler (#1062)

{ "worked": true, "confidence": 9, "verdict": "Built a lagged-parameter scheduler toy with baseline, proposal minus-sign compensation, and inverse plus-sign compensation. The mechanism manifested strongly: steady-state offset matched r/kappa (0.02500 observed vs 0.02500 predicted), threshold delay scaled as 1/kappa with fitted slope 0.99998 versus 1.0 and R²=1.0, and inverse compensation removed essentially 100% of delay. However, the formula’s stated minus sign is wrong for an increasing ramp and doubled delay; the tiny classifier showed no meaningful final-loss win.", "metrics": { "baseline": "Threshold delays for kappa=[1,2,4,8,16,32]: [1.0000,0.5000,0.2500,0.1250,0.0625,0.03125]; toy final loss 0.235689.", "idea": "Inverse plus compensation delays were approximately [0,0,0,0,0,0], giving 100% mean delay reduction; toy final loss 0.235659. The stated minus-sign compensation doubled delays to [2.0000,1.0000,0.5000,0.2500,0.1250,0.0625]." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 lag_scheduler_experiment.py", "files": [ "lag_scheduler_experiment.py", "results.json", "run_output.txt", "run_output2.txt", "run_output3.txt" ], "limitations": "Did not run MNIST, sequential-MNIST, an actual neural-network spectral/Jacobian estimate, GPU experiments, or a broad optimizer benchmark. The toy uses an analytically specified scalar instability boundary and a manually stabilized discrete EMA; compensation clipping and noisy online slope estimation were not tested." }