# Эксперимент: Poisson-Calibrated Candidate-Pool Scheduler (#784)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built and numerically verified the Poisson-calibrated candidate-pool mechanism on a synthetic quadratic continuous-action problem. The predicted error exponents matched closely: observed slopes were -1.865 vs -2.0 for d=1 and -0.954 vs -1.0 for d=2; doubling ratios were 0.279 vs 0.25 and 0.517 vs 0.5. Scheduler scaling matched N proportional to epsilon^(-d/kappa), with slope -0.9998 vs -1.0 and an estimated asymptotic constant within 1.4% of the exact value. The noisy adaptive scheduler did not beat fixed N=128 in the heterogeneous mini-comparison, so the mechanism is validated but an efficiency win was not demonstrated.",
  "metrics": {
    "baseline": "Fixed N=128: mean evaluations 128, mean deficit 0.01179, value-error proxy 0.11786",
    "idea": "Adaptive scheduler: mean evaluations 117.87, mean deficit 0.01623, value-error proxy 0.16231; scaling checks passed with slopes -1.865/-0.954 and scheduler epsilon slope -0.9998"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 poisson_scheduler_experiment.py",
  "files": [
    "poisson_scheduler_experiment.py",
    "results.json",
    "run_output.txt"
  ],
  "limitations": "Only a static synthetic quadratic action-selection problem was tested; no learned critic, actor distribution, multi-step RL rollout, state-dependent effective-dimension estimation, confidence intervals, or GPU implementation was evaluated. The scheduler comparison used a noisy N=8 pilot and a maximum pool of 128, likely contributing to its weaker accuracy than fixed N=128."
}