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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented Proximal-Mismatch Fine-Tuning as a modified training loss for the registered tabular Friedman#1 track using the shared mlp_tiny architecture. The idea was slightly worse than the tuned baseline (mean test MSE 10.996044 vs 10.930936; paired delta +0.065108; permutation p=0.8863), so it did not yield a significant improvement. The mechanism signature was not confirmed.",
  "metrics": {
    "baseline": "Mean test MSE 10.930936, std 1.398331, best configuration lr=0.006 and weight_decay=0.0",
    "idea": "Mean test MSE 10.996044, std 1.169448, best configuration lr=0.006 and weight_decay=0.0; paired delta +0.065108; permutation p=0.8863; 5/8 paired wins"
  },
  "bench_report": {
    "bench_version": 1,
    "track": "tabular",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.006,
        "weight_decay": 0.0
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0
          },
          "mean": 25.60164165496826
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.0
          },
          "mean": 13.860755920410156
        },
        {
          "cfg": {
            "lr": 0.006,
            "weight_decay": 0.0
          },
          "mean": 10.442761182785034
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.0001
          },
          "mean": 13.839483976364136
        }
      ],
      "full": {
        "mean": 10.930935978889465,
        "std": 1.3983305692789296,
        "per_seed": [
          10.067484855651855,
          12.066851615905762,
          9.622394561767578,
          10.014313697814941,
          8.787652015686035,
          11.810267448425293,
          12.032318115234375,
          13.046205520629883
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 10.996043920516968,
      "std": 1.169447967118747,
      "per_seed": [
        9.497220993041992,
        12.14220142364502,
        11.333624839782715,
        9.796889305114746,
        9.323980331420898,
        11.6688232421875,
        11.8473482131958,
        12.35826301574707
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": 0.06510794162750244,
      "idea_wins": 5,
      "n_pairs": 8,
      "per_seed_diffs": [
        -0.5702638626098633,
        0.07534980773925781,
        1.7112302780151367,
        -0.2174243927001953,
        0.5363283157348633,
        -0.14144420623779297,
        -0.18496990203857422,
        -0.6879425048828125
      ],
      "p_value": 0.8863,
      "mde": 0.6389629457421157,
      "mde_rel_pct": 5.845455018455167,
      "verdict": "no measurable effect",
      "system_worked": false
    },
    "mechanism_signature": {
      "quantity": "Mean squared action error to a quadratic proximal teacher on held-out benchmark-derived perturbed states",
      "predicted": "Proximal matching should reduce teacher-action mismatch",
      "confirmed": false
    },
    "idea_sweep": [
      {
        "config": {
          "lr": 0.001,
          "weight_decay": 0.0
        },
        "mean": 25.094110250473022
      },
      {
        "config": {
          "lr": 0.003,
          "weight_decay": 0.0
        },
        "mean": 14.121166110038757
      },
      {
        "config": {
          "lr": 0.006,
          "weight_decay": 0.0
        },
        "mean": 10.996043920516968
      }
    ],
    "protocol": {
      "paired_seeds": [
        0,
        1,
        2,
        3,
        4,
        5,
        6,
        7
      ],
      "epochs": 18,
      "batch": 128,
      "grid_union": [
        {
          "lr": 0.001,
          "weight_decay": 0.0
        },
        {
          "lr": 0.003,
          "weight_decay": 0.0
        },
        {
          "lr": 0.006,
          "weight_decay": 0.0
        },
        {
          "lr": 0.003,
          "weight_decay": 0.0001
        }
      ],
      "structural_match": "tabular: loss/regularization intervention"
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_experiment.py",
  "files": [
    "bench_experiment.py",
    "bench_report.json"
  ],
  "limitations": "Only the registered tabular Friedman#1 track was tested. The intended image-denoising/PnP reconstruction setting, real target-domain proximal teacher, noisy inverse operator, SSIM, and few-shot image adaptation were not evaluated. The benchmark implementation used a quadratic proximal teacher and solver-like input perturbations, so transfer to the intended image reconstruction task remains untested.",
  "system_verdict": "failed",
  "practical_verdict": "no_effect",
  "mechanism_ok": 0,
  "system_judged": true
}