# Эксперимент: Observable-Reduced Neural World Model (#783)

{
  "worked": true,
  "confidence": 9,
  "verdict": "Built and verified the linear observable-reduced world-model MVP. Across 16 rate combinations, fitted coefficients obeyed c_y=beta*r and c_v=r-alpha-beta with maximum relative error 1.41e-5. The sampling sweep showed the predicted transition: mean coefficient error rose from 0.00093 for dt/tau<=0.2 to 0.108 for dt/tau>=1, while the reduced second-order rollout had much lower long-horizon tail RMSE than the first-order baseline, 0.083 versus 1.407.",
  "metrics": {
    "baseline": "First-order y'=k*y: full rollout RMSE 0.997; tail RMSE 1.407",
    "idea": "Reduced second-order y''=c_y*y+c_v*y': full rollout RMSE 0.060; tail RMSE 0.083; invariant maximum relative coefficient error 1.41e-5; sampling coefficient error 0.00093 at dt/tau<=0.2 versus 0.108 at dt/tau>=1"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 observable_reduced_experiment.py",
  "files": [
    "observable_reduced_experiment.py",
    "results.json",
    "README.md",
    "final_run.txt"
  ],
  "limitations": "This MVP tests the exact linear reduction rather than training the proposed neural residual network, GRU, or learned recurrent state-space model. It uses exact noiseless hidden-system simulation with only a small observation-noise perturbation in the rollout comparison, finite differences rather than Savitzky-Golay smoothing, and does not estimate all three rates from only two observable coefficients."
}