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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented fixed-penalty linearized augmented-Lagrangian training for the benchmark's tabular Friedman regression task using the shared mlp_tiny architecture and an exact scalar Sherman–Morrison SPD solve. The trained-model Taylor signature was quantitatively confirmed, with observed remainder divided by mean squared step norm equal to 0.649. However, the idea lost significantly on independent test MSE: mean 8.2751 versus baseline 5.6756, paired delta +2.5995 and permutation p=0.0081; therefore there is no benchmark win.",
  "metrics": {
    "baseline": "test MSE mean 5.6756193638, std 1.2181294766, best config lr=0.01 alpha=0.1, 8 seeds",
    "idea": "test MSE mean 8.2750945687, std 0.3594086274, best config lr=0.003 rho=1.0, 8 seeds",
    "paired_delta": "idea - baseline = +2.5994752049 MSE",
    "permutation_p_value": "0.0081",
    "mechanism_signature": "observed nonlinear Taylor remainder / mean ||p||^2 = 0.6492725980; confirmed=true"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_run.py",
  "files": [
    "bench_run.py",
    "bench_report.json",
    "bench_stdout.txt"
  ],
  "limitations": "Only the structurally matched tabular track was tested; vision, sequence, and dynamics tracks were not tested. The constraint was a single first-layer Frobenius-norm equality, so vector orthogonality constraints, CG solves, adaptive preconditioning, MoE routing, and FLOP-normalized comparisons were not evaluated. The AL implementation used a local step cap and multiplier clipping safeguards, and the benchmark report was generated from the available resolved bench package although its README/path was unavailable in the tool filesystem.",
  "bench_report": {
    "track": "tabular",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "protocol": "8 paired seeds; baseline sweep 9 configs over lr in {0.001,0.003,0.01} and alpha in {0.1,1.0,10.0}; idea sweep 3 configs with matched learning rates and rho in {0.1,1.0,10.0}",
    "baseline": {
      "best_cfg": {
        "lr": 0.01,
        "alpha": 0.1
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "alpha": 0.1
          },
          "mean": 6.8355675936
        },
        {
          "cfg": {
            "lr": 0.001,
            "alpha": 1.0
          },
          "mean": 7.6732069254
        },
        {
          "cfg": {
            "lr": 0.001,
            "alpha": 10.0
          },
          "mean": 13.9623990059
        },
        {
          "cfg": {
            "lr": 0.003,
            "alpha": 0.1
          },
          "mean": 6.0221720934
        },
        {
          "cfg": {
            "lr": 0.003,
            "alpha": 1.0
          },
          "mean": 6.3544781208
        },
        {
          "cfg": {
            "lr": 0.003,
            "alpha": 10.0
          },
          "mean": 6.7492649555
        },
        {
          "cfg": {
            "lr": 0.01,
            "alpha": 0.1
          },
          "mean": 5.6146384478
        },
        {
          "cfg": {
            "lr": 0.01,
            "alpha": 1.0
          },
          "mean": 6.0449602604
        },
        {
          "cfg": {
            "lr": 0.01,
            "alpha": 10.0
          },
          "mean": 6.0946222544
        }
      ],
      "full_mean": 5.6756193638,
      "full_per_seed": [
        4.4764847755,
        6.7919020653,
        6.6910963058,
        4.4990706444,
        4.3336653709,
        7.3076577187,
        4.5788097382,
        6.7262682915
      ]
    },
    "idea": {
      "best_cfg": {
        "lr": 0.003,
        "rho": 1.0
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "rho": 0.1
          },
          "mean": 10.2061942816
        },
        {
          "cfg": {
            "lr": 0.003,
            "rho": 1.0
          },
          "mean": 8.2750945687
        },
        {
          "cfg": {
            "lr": 0.01,
            "rho": 10.0
          },
          "mean": 8.3289720416
        }
      ],
      "per_seed": [
        8.4614048004,
        7.8168811798,
        8.0255336761,
        8.2247095102,
        8.8263225555,
        8.7514038086,
        8.2586240768,
        7.8358769417
      ]
    },
    "comparison": {
      "delta_mean": 2.5994752049,
      "idea_wins": 0,
      "n_pairs": 8,
      "p_value": 0.0081,
      "verdict": "idea worse (significant)",
      "system_worked": false
    },
    "mechanism_signature": {
      "quantity": "nonlinear residual Taylor remainder / ||p||^2",
      "predicted_mean_remainder_bound": 0.0071335184,
      "observed_mean_remainder": 0.004631598,
      "observed_to_predicted_ratio": 0.649272598,
      "tolerance": 5.0,
      "confirmed": true
    },
    "custom_track": null
  },
  "system_verdict": "partial",
  "practical_verdict": "harms",
  "mechanism_ok": 1,
  "system_judged": true
}