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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "The built system uses the registered dynamics track and matched rnn_small architecture, adding only a finite-difference Lipschitz sensitivity penalty during training. The mechanism signature was confirmed: observed mean gain decreased from 0.0680803 to 0.0639061 (-6.13%). Test MSE improved from 0.000765244 to 0.000686484, but the paired permutation p-value was 0.2712, yielding no significant win.",
  "metrics": {
    "baseline": "Best lr=0.006; 8-seed test MSE mean 0.0007652444473933429, std 0.00014266442888030616",
    "idea": "Best lr=0.006, coefficient=0.03, target=0.75; 8-seed test MSE mean 0.0006864842143841088, std 0.0001532192657171232; paired delta=-0.00007876023300923407; p=0.2712"
  },
  "bench_report": {
    "bench_version": 1,
    "track": "dynamics",
    "model": "rnn_small",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.006
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.0015
          },
          "mean": 0.0017903338593896478
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 0.0011179699067724869
        },
        {
          "cfg": {
            "lr": 0.006
          },
          "mean": 0.0007884320366429165
        }
      ],
      "full": {
        "mean": 0.0007652444473933429,
        "std": 0.00014266442888030616,
        "per_seed": [
          0.0007294044480659068,
          0.0007733436650596559,
          0.0005735447048209608,
          0.0010774353286251426,
          0.0006340121617540717,
          0.0008162351441569626,
          0.0006983152125030756,
          0.0008196649141609669
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.0006864842143841088,
      "std": 0.0001532192657171232,
      "per_seed": [
        0.0007881892961449921,
        0.0005242026527412236,
        0.0005977022228762507,
        0.0009137507877312601,
        0.0007462537032552063,
        0.0004245340824127197,
        0.0006721826503053308,
        0.0008250583196058869
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -7.876023300923407e-05,
      "idea_wins": 4,
      "n_pairs": 8,
      "per_seed_diffs": [
        5.878484807908535e-05,
        -0.00024914101231843233,
        2.4157518055289984e-05,
        -0.0001636845408938825,
        0.00011224154150113463,
        -0.0003917010617442429,
        -2.6132562197744846e-05,
        5.393405444920063e-06
      ],
      "p_value": 0.2712,
      "mde": 0.00014483215243757036,
      "mde_rel_pct": 18.926259828596347,
      "verdict": "no significant win",
      "system_worked": false
    },
    "mechanism_signature": {
      "track_structure": "controlled pendulum multi-step dynamics",
      "idea_sweep": [
        {
          "cfg": {
            "lr": 0.0015,
            "coeff": 0.03,
            "target": 0.75
          },
          "result": {
            "mean": 0.0016631976614007726,
            "std": 0.000559542889000477,
            "per_seed": [
              0.0011964859440922737,
              0.0018847997998818755,
              0.0014795587630942464,
              0.002758872462436557,
              0.0010670105693861842,
              0.0009833034127950668,
              0.0019550875294953585,
              0.001980462810024619
            ],
            "n": 8
          }
        },
        {
          "cfg": {
            "lr": 0.003,
            "coeff": 0.03,
            "target": 0.75
          },
          "result": {
            "mean": 0.0010474532173248008,
            "std": 0.0001957735370690522,
            "per_seed": [
              0.0008862946415320039,
              0.0009964596247300506,
              0.000995335285551846,
              0.001299441559240222,
              0.0009098018635995686,
              0.0007802878390066326,
              0.001124624046497047,
              0.0013873808784410357
            ],
            "n": 8
          }
        },
        {
          "cfg": {
            "lr": 0.006,
            "coeff": 0.03,
            "target": 0.75
          },
          "result": {
            "mean": 0.0006864842143841088,
            "std": 0.0001532192657171232,
            "per_seed": [
              0.0007881892961449921,
              0.0005242026527412236,
              0.0005977022228762507,
              0.0009137507877312601,
              0.0007462537032552063,
              0.0004245340824127197,
              0.0006721826503053308,
              0.0008250583196058869
            ],
            "n": 8
          }
        }
      ],
      "predicted": "idea observed gain < baseline observed gain",
      "baseline_mean_gain": 0.06808034889400005,
      "idea_mean_gain": 0.06390607543289661,
      "relative_change_pct": -6.13139258084989,
      "confirmed": true
    },
    "custom_track": null
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_experiment.py",
  "files": [
    "bench_experiment.py",
    "bench_report.json"
  ],
  "limitations": "The built-in dynamics task is supervised multi-step pendulum prediction rather than PPO/SAC policy learning. The intervention regularizes predictor input sensitivity; it does not perform full closed-loop policy certification with learned transition uncertainty, explicit safety functions, disturbance sweeps, spectral normalization, or long-horizon violation testing.",
  "system_verdict": "partial",
  "practical_verdict": "inconclusive",
  "mechanism_ok": 1,
  "system_judged": true
}