# Эксперимент: Monotone Compositional Reachability Critic (#1091)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built a monotone positive-weight compositional critic and numerically tested its core claims. The aggregator preserved coordinatewise ordering in 100% of trials, primitive-value error propagated linearly with log-log exponent 0.9999, and the direct order-reversal transition occurred exactly when the swept coefficient became negative (0% reversal for c2>=0, 100% for c2<0). In the matched synthetic critic task, monotone and unconstrained aggregators performed essentially identically, so this verifies the mechanism but does not demonstrate a practical accuracy win.",
  "metrics": {
    "baseline": "Unconstrained linear aggregator: held-out RMSE 0.03186373 and set IoU 0.92797786; learned weights [0.998989, 0.795554].",
    "idea": "Positive-weight monotone aggregator: held-out RMSE 0.03186373 and set IoU 0.92797786; learned weights [0.998989, 0.795554]. Core checks: ordering preservation 1.0, error-bound maximum ratio 1.00073, error-scaling exponent 0.999923, and negative-coefficient order reversal 0% for c2>=0 versus 100% for c2<0."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json"
  ],
  "limitations": "This is a small synthetic static value-function experiment, not a continuous-time HJ solver, adversarial rollout, RL critic, or learned nonlinear aggregator. The unconstrained model was trained on data generated by a monotone target, so it learned positive weights and had no opportunity to expose a practical generalization advantage. Runtime, scalability, and high-dimensional reachability were not tested."
}