# Стенд-проверка (stage-2) · промт оператора:

(универсальный)

Ответ агента:

{
  "worked": true,
  "confidence": 7,
  "verdict": "Implemented the Cramér–Rao temperature controller as an adaptive optimizer schedule on the structurally matched dynamics track with identical rnn_small systems. Across 8 paired seeds, the controller reduced test MSE from 0.0003047815 to 0.0002041774, with paired delta -0.0001006041 and permutation p=0.0081, satisfying the benchmark's significant-win criterion. The neural-scale mechanism signature was not confirmed: observed update energy shifts exceeded the estimated bound substantially.",
  "metrics": {
    "baseline": "Dynamics/rnn_small fixed-temperature Adam; 9-config sweep; best lr=0.0045 and temperature=1.5; mean test MSE 0.00030478146436507814.",
    "idea": "Adaptive controller; best lr=0.0045 and delta=0.02; mean test MSE 0.00020417736959643662; paired delta=-0.00010060409476864152; permutation p=0.0081; 8/8 paired wins.",
    "mechanism_signature": "1008 trained-system updates; predicted mean shift 0.0022922240415369968; observed mean shift 0.009866443170303394; rho mean 22.36028769385943; rho max 518.6289797241246; confirmed=false"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "stage2_bench.py",
    "bench_report.json",
    "bench_output.txt"
  ],
  "limitations": "Only the dynamics track was tested, with 400 training examples, 400 test examples, 18 epochs, and a small hyperparameter grid. The implementation uses minibatch squared training loss as energy and a centered-loss Fisher surrogate; the failed signature indicates this surrogate does not quantitatively satisfy the original Cramér–Rao prediction for optimizer updates. No wall-clock speed, larger models, other tracks, or independent replication was tested.",
  "bench_report": {
    "track": "dynamics",
    "model": "rnn_small",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.0045,
        "temp": 1.5
      },
      "mean": 0.00030478146436507814
    },
    "idea": {
      "best_cfg": {
        "lr": 0.0045,
        "delta": 0.02
      },
      "mean": 0.00020417736959643662
    },
    "comparison": {
      "delta_mean": -0.00010060409476864152,
      "p_value": 0.0081,
      "idea_wins": 8,
      "n_pairs": 8,
      "verdict": "idea better (significant)"
    },
    "mechanism_signature": {
      "predicted_mean_shift": 0.0022922240415369968,
      "observed_mean_shift": 0.009866443170303394,
      "rho_mean": 22.36028769385943,
      "rho_max": 518.6289797241246,
      "confirmed": false
    }
  },
  "system_verdict": "failed",
  "practical_verdict": "inconclusive",
  "mechanism_ok": 0,
  "system_judged": true
}