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

(универсальный)

Ответ агента:

{
  "worked": true,
  "confidence": 7,
  "verdict": "Implemented learning-rate-scaled decoupled AdamW on the registered tabular Friedman#1 track with the shared mlp_tiny architecture and parity-preserving baseline/idea sweeps. The idea achieved a statistically significant but practically tiny improvement in test MSE across 8 paired seeds: delta=-0.00022018 and permutation p=0.0169, with 7/8 wins. The trained-model mechanism signature confirmed the direct decay multiplier but did not confirm the cooldown log-ratio prediction, so the optimization win should not be generalized beyond this small benchmark.",
  "metrics": {
    "baseline": "Best cfg lr=0.006, weight_decay=0.001; full 8-seed mean MSE=11.662253737449646, std=1.1646783408667203.",
    "idea": "Best cfg lr=0.006, weight_decay=0.001; full 8-seed mean MSE=11.662033557891846, std=1.16472778990251; delta=-0.00022017955780029297; p=0.0169."
  },
  "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": 0.001
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.0015,
            "weight_decay": 0.001
          },
          "mean": 16.25991463661194
        },
        {
          "cfg": {
            "lr": 0.0015,
            "weight_decay": 0.01
          },
          "mean": 16.260149240493774
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.001
          },
          "mean": 13.776280879974365
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.01
          },
          "mean": 13.777657508850098
        },
        {
          "cfg": {
            "lr": 0.006,
            "weight_decay": 0.001
          },
          "mean": 11.124481439590454
        },
        {
          "cfg": {
            "lr": 0.006,
            "weight_decay": 0.01
          },
          "mean": 11.131932497024536
        }
      ],
      "full": {
        "mean": 11.662253737449646,
        "std": 1.1646783408667203,
        "per_seed": [
          11.829092979431152,
          10.211130142211914,
          11.012014389038086,
          11.445688247680664,
          10.679678916931152,
          11.089096069335938,
          13.720934867858887,
          13.310394287109375
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 11.662033557891846,
      "std": 1.16472778990251,
      "per_seed": [
        11.828662872314453,
        10.210942268371582,
        11.011709213256836,
        11.445385932922363,
        10.679506301879883,
        11.088908195495605,
        13.720953941345215,
        13.310199737548828
      ],
      "n": 8,
      "selected_cfg": {
        "lr": 0.006,
        "weight_decay": 0.001
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.0015,
            "weight_decay": 0.001
          },
          "mean": 16.26001787185669
        },
        {
          "cfg": {
            "lr": 0.0015,
            "weight_decay": 0.01
          },
          "mean": 16.261214017868042
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.001
          },
          "mean": 13.776211261749268
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.01
          },
          "mean": 13.776825904846191
        },
        {
          "cfg": {
            "lr": 0.006,
            "weight_decay": 0.001
          },
          "mean": 11.124175071716309
        },
        {
          "cfg": {
            "lr": 0.006,
            "weight_decay": 0.01
          },
          "mean": 11.128536701202393
        }
      ]
    },
    "comparison": {
      "delta_mean": -0.00022017955780029297,
      "idea_wins": 7,
      "n_pairs": 8,
      "per_seed_diffs": [
        -0.00043010711669921875,
        -0.00018787384033203125,
        -0.00030517578125,
        -0.00030231475830078125,
        -0.00017261505126953125,
        -0.00018787384033203125,
        1.9073486328125e-05,
        -0.000194549560546875
      ],
      "p_value": 0.0169,
      "mde": 0.0001096098618612078,
      "mde_rel_pct": 0.0009398686079795221,
      "verdict": "idea better (significant)",
      "system_worked": true
    },
    "mechanism_signature": {
      "source": "trained mlp_tiny models on Friedman#1; decay boundaries instrumented during full paired runs",
      "predicted_mean_decay_multiplier": 0.9999976694444446,
      "observed_mean_decay_multiplier": 0.9999976609459289,
      "multiplier_abs_error": 8.498515668975415e-09,
      "cooldown_predicted_log_ratio": 0.2521996381548064,
      "cooldown_observed_log_ratio": 1.2277044265333381,
      "cooldown_ratio_abs_error": 0.9755047883785317,
      "confirmed": false
    }
  },
  "how_to_run": "cd /home/maxwelhelp/all/math2nn/experiments/exp1333_learning-rate-scaled-weight-decay && /home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "stage2_bench.py",
    "bench_report.json"
  ],
  "limitations": "Only the registered tabular Friedman#1 track and mlp_tiny were tested. Vision, sequence, dynamics, larger networks, SGD, Muon, wall-clock/FLOP efficiency, and independent datasets were not tested. The significant effect is less than 0.001 relative percent, and the cooldown mechanism signature was not confirmed.",
  "system_verdict": "worked",
  "practical_verdict": "helps",
  "mechanism_ok": 0,
  "system_judged": true
}