# Эксперимент: Decision-Weighted Variance Acquisition (#981)

{
  "worked": true,
  "confidence": 9,
  "verdict": "Built an exact rank-one Bayesian residual covariance scorer with decision-weighted acquisition, posterior updates, toy verification, and a controlled 30-seed comparison. The mechanism manifested: direct weighted variance reduction matched the formula to 2.1e-15 relative error, importance scaling matched exactly, and noise-denominator scaling matched to 3.0e-16 relative error. Decision acquisition reduced task-variance AUC from 10.81 for uncertainty sampling to 4.90 and from 11.46 for random sampling to 4.90; final realized task MSE was comparable to uncertainty sampling and better than random.",
  "metrics": {
    "baseline": "Random: final task variance 0.8995, variance AUC 11.4626, final realized task MSE 0.8297. Uncertainty: final task variance 0.3059, variance AUC 10.8131, final realized task MSE 0.2134.",
    "idea": "Decision-weighted: final task variance 0.2658, variance AUC 4.8973, final realized task MSE 0.2162. Formula verification: maximum relative identity error 2.08e-15; importance-scaling error 0; noise-scaling relative error 2.95e-16."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json"
  ],
  "limitations": "This is a synthetic Bayesian linear residual experiment rather than a neural world model or closed-loop RL/navigation task. It does not test neural feature learning, rollout-gradient errors, candidate costs, diversity constraints, ensemble-UCB, or GPU behavior; the uncertainty baseline is feature-posterior variance rather than a neural ensemble."
}