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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented Carrier-Probed Hidden-State Training on the structurally matched dynamics track with the shared rnn_small architecture, carrier-perturbed controls, and an output-consistency loss. The best idea achieved MSE 0.000652743 versus baseline 0.000652883, but the paired permutation test was not significant (delta=-1.40e-7, p=0.459), and the mechanism signature was not confirmed because predicted and nominal carrier responses had near-zero correlation.",
  "metrics": {
    "baseline": "dynamics/rnn_small, lr=0.01 after sweep; 8-seed test MSE mean=0.000652883, std=0.000169289",
    "idea": "cosine carrier consistency loss, amplitude=0.30; 8-seed test MSE mean=0.000652743, std=0.000169230; paired delta=-1.3957e-7, 6/8 wins, permutation p=0.45895; signature confirmed=false"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 carrier_bench.py",
  "files": [
    "carrier_bench.py",
    "bench_report.json"
  ],
  "limitations": "Only the built-in actuated-pendulum dynamics track was tested. The implementation used a fixed cosine carrier and simplified nominal consistency target rather than automatic Jacobian-based log-determinant candidate selection and propagated Gramians. Multiple architectures, deployment effects, and a full idea-side learning-rate sweep were not tested.",
  "bench_report": {
    "bench_version": 1,
    "track": "dynamics",
    "model": "rnn_small",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.01
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001
          },
          "mean": 0.005440622277092189
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 0.0012262430100236088
        },
        {
          "cfg": {
            "lr": 0.01
          },
          "mean": 0.000702349963830784
        }
      ],
      "full": {
        "mean": 0.0006528829871967901,
        "std": 0.0001692890895210604,
        "per_seed": [
          0.0006883637397550046,
          0.00054119125707075,
          0.0005475711077451706,
          0.0010322737507522106,
          0.00068665825529024,
          0.00041018708725459874,
          0.0006441730074584484,
          0.0006726456922478974
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.000652743416139856,
      "std": 0.0001692304843358837,
      "per_seed": [
        0.0006887014023959637,
        0.0005411907332018018,
        0.0005468056187964976,
        0.0010318164713680744,
        0.0006858076085336506,
        0.00041013583540916443,
        0.0006440518773160875,
        0.0006734377820976079
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -1.3957105693407357e-07,
      "idea_wins": 6,
      "n_pairs": 8,
      "per_seed_diffs": [
        3.3766264095902443e-07,
        -5.238689482212067e-10,
        -7.654889486730099e-07,
        -4.5727938413619995e-07,
        -8.506467565894127e-07,
        -5.125184543430805e-08,
        -1.2113014236092567e-07,
        7.920898497104645e-07
      ],
      "p_value": 0.45895,
      "verdict": "no measurable effect",
      "system_worked": false
    },
    "mechanism_signature": {
      "carrier_amp": 0.3,
      "predicted_abs_delta_mean": 0.0008173562528099865,
      "observed_nominal_abs_delta_mean": 5.861693352926522e-10,
      "predicted_observed_correlation_mean": null,
      "quadratic_prediction": "not confirmed at NN scale: only one amplitude was behaviorally tested",
      "confirmed": false
    }
  },
  "system_verdict": "failed",
  "practical_verdict": "no_effect",
  "mechanism_ok": 0,
  "system_judged": true
}