# Эксперимент: Closure-Decorrelation Memory Scheduler (#963)

{
  "worked": false,
  "confidence": 9,
  "verdict": "Built and numerically verified a closure-decorrelation scheduler on a synthetic unresolved AR(1) closure process with noisy resolved observations. The persistent threshold-crossing prediction matched theory exactly across all five correlation parameters, with 0-step MAE. However, scheduler-selected memory did not reliably match the forecasting elbow, so the claimed mechanism was not sufficiently confirmed.",
  "metrics": {
    "baseline": "Fixed m=1: mean one-step MSE 1.10295 and mean 30-step rollout MSE 1.40463.",
    "idea": "Diagnostic-selected memory had mean m=9.2, mean one-step MSE 1.07141, a 2.86% improvement over m=1, and mean rollout MSE 1.38282. Fixed m=32 achieved 1.06710 one-step MSE, only 0.40% better than the scheduler. Crossing MAE was 0 steps; integrated-correlation-time relative MAE was 20.7%."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 closure_scheduler_experiment.py",
  "files": [
    "closure_scheduler_experiment.py",
    "results.json"
  ],
  "limitations": "This was a synthetic scalar AR(1) closure experiment rather than Burgers, Kuramoto–Sivashinsky, or a neural operator. It used a linear ridge history predictor, did not measure attention/FLOPs or wall-clock savings, did not test regime adaptation, and finite trajectory length biased the integrated-correlation estimate."
}