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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented the convex-gradient transport augmenter and ran the full 8-seed paired dynamics benchmark with rnn_small, equal learning-rate search space, and a tuned Gaussian baseline sweep. The best idea setting was the identity transport and produced a negligible non-significant improvement (delta_mean=-9.39e-06, p=0.8087), so the promised practical win was not observed. The non-identity mechanism signature was confirmed: observed versus predicted displacement differed by 1.02e-06 relative error, but this does not establish a task-performance gain.",
  "metrics": {
    "baseline": "Best Gaussian baseline: lr=0.003, noise=0.05; 8-seed MSE mean=0.0013185284, std=0.0004747611",
    "idea": "Best convex-gradient setting: lr=0.003, scale=1.00; 8-seed MSE mean=0.0013091382, std=0.0004729142",
    "comparison": "delta_mean=-0.00000939018, idea_wins=5/8, permutation p=0.8087, verdict=no measurable effect",
    "mechanism_signature": "Non-identity scale=1.04: predicted displacement=0.16550005, observed displacement=0.16549988, relative error=1.02e-06, confirmed=true"
  },
  "bench_report": {
    "bench_version": 1,
    "track": "dynamics",
    "model": "rnn_small",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.003,
        "noise": 0.05
      },
      "full": {
        "mean": 0.0013185284115024842,
        "std": 0.00047476113201356173,
        "per_seed": [
          0.0008951174677349627,
          0.0010742346057668328,
          0.0008329956326633692,
          0.0008899347158148885,
          0.0012180092744529247,
          0.0017242580652236938,
          0.001678277039900422,
          0.00223540049046278
        ],
        "n": 8
      }
    },
    "idea": {
      "best_cfg": {
        "lr": 0.003,
        "scale": 1.0
      },
      "mean": 0.0013091382279526442,
      "std": 0.00047291421988416747,
      "per_seed": [
        0.0008530844934284687,
        0.0012209241976961493,
        0.0008412626339122653,
        0.0008439973462373018,
        0.0011196484556421638,
        0.0017749746330082417,
        0.0016231484478339553,
        0.002196065615862608
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -9.390183549840003e-06,
      "idea_wins": 5,
      "n_pairs": 8,
      "per_seed_diffs": [
        -4.2032974306494e-05,
        0.00014668959192931652,
        8.267001248896122e-06,
        -4.593736957758665e-05,
        -9.836081881076097e-05,
        5.0716567784547806e-05,
        -5.512859206646681e-05,
        -3.933487460017204e-05
      ],
      "p_value": 0.8087,
      "mde": 6.445221887975781e-05,
      "mde_rel_pct": 4.888193406944753,
      "verdict": "no measurable effect",
      "system_worked": false
    },
    "mechanism_signature": {
      "selected_idea_cfg": {
        "lr": 0.003,
        "scale": 1.0
      },
      "signature_cfg": {
        "lr": 0.003,
        "scale": 1.04
      },
      "predicted_mean_displacement": 0.1655000495910646,
      "observed_mean_displacement_model_inputs": 0.16549988090991974,
      "displacement_relative_error": 1.0192211136694558e-06,
      "predicted_gaussian_kl": 0.0379028843212495,
      "observed_pushforward_kl_from_exact_gaussian_formula": 0.0379028843212495,
      "confirmed": true
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_convex.py",
  "files": [
    "bench_convex.py",
    "bench_report.json"
  ],
  "limitations": "Only the built-in controlled-pendulum dynamics track was tested. The implementation uses a low-dimensional isotropic quadratic convex potential rather than a learned ICNN, KDE/Sinkhorn ambiguity optimization, or class-conditional adversarial ascent; no vision or custom track evaluation was performed.",
  "system_verdict": "partial",
  "practical_verdict": "no_effect",
  "mechanism_ok": 1,
  "system_judged": true
}