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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "The global-statistics context shortcut was implemented as per-example channelwise spatial normalization in a CNN matched to the registered vision cnn_small architecture. Its trained-model distant Jacobian prediction was confirmed quantitatively, but the task metric did not show a significant improvement: paired delta was -0.0100 with permutation p=0.67055, so the protocol requires worked=false.",
  "metrics": {
    "baseline": "Full 8-seed mean err 0.754375; best swept configuration lr=0.003, weight_decay=0.0.",
    "idea": "Full 8-seed mean err 0.744375; best idea configuration lr=0.001, weight_decay=0.0; paired delta -0.0100000, p=0.67055, 3/8 paired wins."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_stage2.py",
  "files": [
    "bench_stage2.py",
    "bench_report.json"
  ],
  "bench_report": {
    "bench_version": 1,
    "track": "vision",
    "model": "cnn_small",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.003,
        "weight_decay": 0.0
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0
          },
          "mean": 0.73499995470047
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.0
          },
          "mean": 0.7299999892711639
        },
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0001
          },
          "mean": 0.739999994635582
        }
      ],
      "full": {
        "mean": 0.7543749883770943,
        "std": 0.05033869289963452,
        "per_seed": [
          0.6850000023841858,
          0.7199999690055847,
          0.824999988079071,
          0.6899999976158142,
          0.7649999856948853,
          0.8149999976158142,
          0.7899999618530273,
          0.7450000047683716
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.7443749830126762,
      "std": 0.05204790338215126,
      "per_seed": [
        0.7450000047683716,
        0.7549999952316284,
        0.8499999642372131,
        0.7299999594688416,
        0.6499999761581421,
        0.7649999856948853,
        0.7149999737739563,
        0.7450000047683716
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -0.01000000536441803,
      "idea_wins": 3,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.06000000238418579,
        0.0350000262260437,
        0.02499997615814209,
        0.039999961853027344,
        -0.11500000953674316,
        -0.050000011920928955,
        -0.07499998807907104,
        0.0
      ],
      "p_value": 0.67055,
      "mde": 0.05250391180418798,
      "mde_rel_pct": 6.959922135958001,
      "verdict": "no significant win",
      "system_worked": false
    },
    "mechanism_signature": {
      "signature": {
        "location": "trained global-normalization layer on bench vision model",
        "predicted_offdiagonal": -0.014527199789881706,
        "observed_offdiagonal": -0.01452720072120428,
        "absolute_error": 9.313225746154785e-10,
        "n": 1024,
        "confirmed": true
      },
      "idea_sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0
          },
          "result": {
            "mean": 0.7443749830126762,
            "std": 0.05204790338215126,
            "per_seed": [
              0.7450000047683716,
              0.7549999952316284,
              0.8499999642372131,
              0.7299999594688416,
              0.6499999761581421,
              0.7649999856948853,
              0.7149999737739563,
              0.7450000047683716
            ],
            "n": 8
          }
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.0
          },
          "result": {
            "mean": 0.784374974668026,
            "std": 0.0535862355865634,
            "per_seed": [
              0.7799999713897705,
              0.7649999856948853,
              0.8799999952316284,
              0.7199999690055847,
              0.7599999904632568,
              0.85999995470047,
              0.7799999713897705,
              0.7299999594688416
            ],
            "n": 8
          }
        },
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0001
          },
          "result": {
            "mean": 0.7474999725818634,
            "std": 0.056623754264485855,
            "per_seed": [
              0.7199999690055847,
              0.7649999856948853,
              0.85999995470047,
              0.7249999642372131,
              0.6499999761581421,
              0.7849999666213989,
              0.7249999642372131,
              0.75
            ],
            "n": 8
          }
        }
      ],
      "matched_architecture": true,
      "normalization": "per-example channelwise spatial global statistics"
    },
    "custom_track": null
  },
  "limitations": "The benchmark used the fixed registered vision CIFAR-10 subset with 400 training examples, 200 test examples, and 6 epochs to fit the runtime budget. Larger-data and longer-training validation, throughput, activation-memory, and inference-latency measurements were not run. The mechanism check used one trained model and one distant spatial pair; padding masks are not applicable to fixed-size CIFAR images.",
  "system_verdict": "failed",
  "practical_verdict": "no_effect",
  "mechanism_ok": 0,
  "system_judged": true
}