# Эксперимент: Delay-Kernel Bifurcation Scheduler (#434)

{
  "worked": true,
  "confidence": 9,
  "verdict": "Built a delayed-RNN curriculum scheduler and a Lambert-W characteristic-root verification. Across four parameter settings, the predicted Hopf delay matched the numerical crossing within 0.00033% and the predicted frequency within 0.00025%; the rightmost-root slope was positive and stability changed from decay to growth on crossing. In the tiny classification test, scheduling reduced validation loss (0.0420 vs 0.0514) but reduced accuracy (0.9805 vs 0.9961), so the stability mechanism is confirmed but an accuracy win was not observed.",
  "metrics": {
    "baseline": "val_loss=0.05138, accuracy=0.9961, fixed integer delay=1",
    "idea": "val_loss=0.04205, accuracy=0.9805, final continuous tau=1.0345, final integer delay=4; root sweep max tau error=0.000324%, max frequency error=0.000246%"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 delay_scheduler_experiment.py",
  "files": [
    "delay_scheduler_experiment.py",
    "results.json",
    "run_output.txt"
  ],
  "limitations": "Only one synthetic frequency-classification seed and one small model were tested; no multi-seed significance analysis, FLOP-normalized comparison, validation-triggered rollback, distributed kernels, or direct Jacobian-vector-product gain estimation was evaluated."
}