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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 10,
  "verdict": "The full stage-2 bench was run on the registered tabular track with eight paired seeds and a tuned three-configuration baseline sweep. The idea and baseline are identical trained systems because the certificate is an inference-time diagnostic, yielding exactly equal test MSE (delta_mean=0, p=1.0); the trained residual boundary also failed the predicted projective mechanism (mean K=15, projectivity=0.85), so this is not a win.",
  "metrics": {
    "baseline": "mlp_tiny tabular regression; best lr=0.006, epochs=12, alpha=0.15; full 8-seed MSE mean 20.881689, std 2.809771.",
    "idea": "Same paired neural systems with explicit residual boundary certificate; full 8-seed MSE mean 20.881689, std 2.809771; delta_mean 0.0, p=1.0."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "tabular",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.006,
        "epochs": 12,
        "alpha": 0.15
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.0015,
            "epochs": 12,
            "alpha": 0.05
          },
          "mean": 116.23149108886719
        },
        {
          "cfg": {
            "lr": 0.003,
            "epochs": 12,
            "alpha": 0.1
          },
          "mean": 29.072664737701416
        },
        {
          "cfg": {
            "lr": 0.006,
            "epochs": 12,
            "alpha": 0.15
          },
          "mean": 20.75340461730957
        }
      ],
      "full": {
        "mean": 20.881689310073853,
        "std": 2.8097707761369097,
        "per_seed": [
          24.462068557739258,
          22.150100708007812,
          18.394702911376953,
          18.006746292114258,
          16.289684295654297,
          22.511140823364258,
          21.119823455810547,
          24.119247436523438
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 20.881689310073853,
      "std": 2.8097707761369097,
      "per_seed": [
        24.462068557739258,
        22.150100708007812,
        18.394702911376953,
        18.006746292114258,
        16.289684295654297,
        22.511140823364258,
        21.119823455810547,
        24.119247436523438
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": 0.0,
      "idea_wins": 0,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0
      ],
      "p_value": 1.0,
      "mde": 0.0,
      "mde_rel_pct": 0.0,
      "verdict": "no measurable effect",
      "system_worked": false
    },
    "selection": {
      "baseline_grid": [
        {
          "lr": 0.0015,
          "epochs": 12,
          "alpha": 0.05
        },
        {
          "lr": 0.003,
          "epochs": 12,
          "alpha": 0.1
        },
        {
          "lr": 0.006,
          "epochs": 12,
          "alpha": 0.15
        }
      ],
      "idea_grid": [
        {
          "lr": 0.0015,
          "epochs": 12,
          "alpha": 0.05
        },
        {
          "lr": 0.003,
          "epochs": 12,
          "alpha": 0.1
        },
        {
          "lr": 0.006,
          "epochs": 12,
          "alpha": 0.15
        }
      ],
      "baseline_best": {
        "lr": 0.006,
        "epochs": 12,
        "alpha": 0.15
      },
      "idea_best": {
        "lr": 0.006,
        "epochs": 12,
        "alpha": 0.15
      }
    },
    "structural_match": "Independent neural calibration residuals define a scalar acceptance set and explicit order-statistic boundary, matching the selective-prediction structure of the idea.",
    "mechanism_signature": {
      "prediction": "trained scalar residual order-statistic has K=1 and projective deletion; risk approximately Beta(1,n) mean",
      "observed_mean": {
        "boundary_size": 15.0,
        "empirical_test_risk": 0.175,
        "beta_predicted_mean_risk": 0.1485148514851485,
        "deletion_projectivity": 0.85,
        "deletion_equivalence": 0.99
      },
      "confirmed": false
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_experiment.py",
  "files": [
    "bench_experiment.py",
    "bench_report.json"
  ],
  "limitations": "Only the registered tabular track was tested; vision, sequence, and dynamics were not structurally necessary for scalar calibration residual certificates. Vector/Pareto boundaries, mixture-profile certificates, and full multiclass selective classification were not tested. The intervention did not change training, so no task-MSE improvement was expected.",
  "system_verdict": "failed",
  "practical_verdict": "no_effect",
  "mechanism_ok": 0,
  "system_judged": true
}