Эксперимент: Greedy Singular-Value Delay Scheduler (#906)

{ "worked": true, "confidence": 8, "verdict": "Built an exact matrix-exponential greedy delay scheduler with fixed-budget selection and stopping-threshold support. The mechanism manifested across sweeps: row-appending margins were monotone, selected delays tracked pi/(2 omega) with maximum error 0.0385, and reconstruction MSE scaled with noise variance; MSE versus 1/sigma_min^2 had correlation 0.986. Greedy selection improved linear toy MSE by 21.7% at 4 taps and 9.7% at 8 taps versus uniform delays, but no neural delay-MLP or rollout win was tested.", "metrics": { "baseline": "Uniform delays: at 4 taps sigma_min=1.1093 and MSE=0.00061785; at 8 taps sigma_min=1.5816 and MSE=0.00030887; at 16 taps sigma_min=2.2761 and MSE=0.00015513.", "idea": "Greedy delays: at 4 taps sigma_min=1.3124 and MSE=0.00048366; at 8 taps sigma_min=1.7912 and MSE=0.00027900; at 16 taps sigma_min=2.4233 and MSE=0.00015152. Quarter-period sweep maximum absolute error=0.0385; noise-scaling ratio CV=2.1e-16; MSE versus inverse-margin-squared correlation=0.9858." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "Only the exact two-dimensional damped oscillator was tested. Nonlinear delay MLPs, missing observations, two-frequency signals, 100-step rollouts, learned/local Jacobian scheduling, profiling, and broader systems were not tested. The candidate grid also permits near-duplicate delays." }