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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 10,
  "verdict": "Built a local Order-Adaptive Integral optimizer with nested states, EMA-triggered order growth, gain ramping, and clipping, evaluated against the same tabular MLP using the bench protocol. Across 8 paired seeds, tuned momentum SGD achieved test MSE 8.4621 while the best adaptive setting achieved 11.5672; the paired permutation p-value was 0.0081 for a significantly worse result. The mechanism signature was not confirmed because order activated on all seeds rather than remaining mostly at order zero on this easy tabular task.",
  "metrics": {
    "baseline": "Tuned SGD with momentum=0.9, lr=0.003; 8-seed test MSE mean 8.4620805383, std 0.7635960927.",
    "idea": "Order-adaptive integral optimizer, best tested lr=0.003; 8-seed test MSE mean 11.5672200918, std 1.9770478863. Paired delta +3.1051395535, 0/8 wins, permutation p=0.0081; verdict idea worse (significant)."
  },
  "how_to_run": "PYTHONPATH=/home/maxwelhelp/all/math2nn /home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "stage2_bench.py",
    "bench_report.json"
  ],
  "limitations": "Only the structurally matched built-in tabular/Friedman regression track with mlp_tiny was tested; vision, sequence, and dynamics tracks were not tested. The run used 400 training samples, 400 test samples, 18 epochs, and did not measure wall-clock speed or memory overhead. The baseline sweep used SGD momentum and three learning rates; no AdamW or layerwise-order variant was evaluated.",
  "bench_report": {
    "bench_version": 1,
    "track": "tabular",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.003,
        "momentum": 0.9
      },
      "full": {
        "mean": 8.462080538272858,
        "std": 0.763596092727506,
        "per_seed": [
          9.01702880859375,
          8.495253562927246,
          8.408374786376953,
          7.555444240570068,
          7.235288619995117,
          8.890181541442871,
          9.808090209960938,
          8.286982536315918
        ],
        "n": 8
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.003,
            "momentum": 0.0
          },
          "mean": 11.063072919845581
        },
        {
          "cfg": {
            "lr": 0.003,
            "momentum": 0.9
          },
          "mean": 8.369025349617004
        },
        {
          "cfg": {
            "lr": 0.01,
            "momentum": 0.0
          },
          "mean": 27.549113154411316
        },
        {
          "cfg": {
            "lr": 0.01,
            "momentum": 0.9
          },
          "mean": 21.363237619400024
        },
        {
          "cfg": {
            "lr": 0.03,
            "momentum": 0.0
          },
          "mean": 24.645026683807373
        },
        {
          "cfg": {
            "lr": 0.03,
            "momentum": 0.9
          },
          "mean": 24.67656421661377
        }
      ]
    },
    "idea": {
      "mean": 11.567220091819763,
      "std": 1.9770478862735161,
      "per_seed": [
        11.564115524291992,
        11.624547958374023,
        11.304121017456055,
        9.648195266723633,
        8.412535667419434,
        11.701919555664062,
        12.67589282989502,
        15.606432914733887
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": 3.1051395535469055,
      "idea_wins": 0,
      "n_pairs": 8,
      "p_value": 0.0081,
      "verdict": "idea worse (significant)",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "on easy well-conditioned tabular training adaptive order remains mostly p=0 while residual contracts",
      "observed_activation_fraction": 1.0,
      "first_activation": 5,
      "grad_ratio_range": [
        0.2514142861,
        0.4284247521
      ],
      "confirmed": false
    }
  },
  "system_verdict": "failed",
  "practical_verdict": "harms",
  "mechanism_ok": 0,
  "system_judged": true
}