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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 10,
  "verdict": "Implemented greedy Pick-to-Learn-style residual scenario compression for the registered tabular Friedman#1 MLP track. The core numerical proxy contracted monotonically with increasing N and the trained-model signature confirmed high-loss-tail selection, but compression to k=20 caused a large, statistically significant test-MSE regression. Therefore the idea did not win the standard benchmark metric.",
  "metrics": {
    "baseline": "mean test MSE 15.31271517276764, std 0.822924635150327, best configuration lr=0.006 epochs=12",
    "idea": "mean test MSE 103.05307102203369, std 20.493133534705294, best configuration lr=0.006 epochs=12",
    "comparison": "idea minus baseline delta_mean +87.74035584926605; permutation p_value 0.0081; 0 of 8 paired wins"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_p2l.py",
  "files": [
    "bench_p2l.py",
    "bench_report.json"
  ],
  "limitations": "The registered tabular track was used because the idea concerns training-time calibration and optimizer/training dynamics. The implementation uses a practical greedy residual heuristic rather than claiming the exact theorem for adaptive neural-network training; k was fixed at 20, and vision corruption-specific experiments were not run.",
  "bench_report": {
    "bench_version": 1,
    "track": "tabular",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.006,
        "epochs": 12
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.0015,
            "epochs": 12
          },
          "mean": 51.05318737030029
        },
        {
          "cfg": {
            "lr": 0.003,
            "epochs": 12
          },
          "mean": 17.679327249526978
        },
        {
          "cfg": {
            "lr": 0.006,
            "epochs": 12
          },
          "mean": 15.346336364746094
        }
      ],
      "full": {
        "mean": 15.31271517276764,
        "std": 0.822924635150327,
        "per_seed": [
          15.071464538574219,
          14.645296096801758,
          16.341999053955078,
          14.45390510559082,
          14.129950523376465,
          15.382646560668945,
          16.234203338623047,
          16.24225616455078
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 103.05307102203369,
      "std": 20.493133534705294,
      "per_seed": [
        130.216796875,
        99.72685241699219,
        92.01232147216797,
        98.55255889892578,
        89.51197052001953,
        66.24312591552734,
        119.2832260131836,
        128.87771606445312
      ],
      "n": 8,
      "sweep": [
        {
          "cfg": {
            "lr": 0.0015,
            "epochs": 12
          },
          "mean": 218.74835777282715,
          "std": 10.794174158154743,
          "per_seed": [
            230.59194946289062,
            216.66702270507812,
            231.0927276611328,
            194.62643432617188,
            222.1121063232422,
            216.87411499023438,
            214.635498046875,
            223.3870086669922
          ],
          "n": 8
        },
        {
          "cfg": {
            "lr": 0.003,
            "epochs": 12
          },
          "mean": 197.7052059173584,
          "std": 8.29119691504502,
          "per_seed": [
            193.0837860107422,
            201.17518615722656,
            196.46839904785156,
            183.23458862304688,
            213.4451141357422,
            198.17083740234375,
            203.3914337158203,
            192.67230224609375
          ],
          "n": 8
        },
        {
          "cfg": {
            "lr": 0.006,
            "epochs": 12
          },
          "mean": 103.05307102203369,
          "std": 20.493133534705294,
          "per_seed": [
            130.216796875,
            99.72685241699219,
            92.01232147216797,
            98.55255889892578,
            89.51197052001953,
            66.24312591552734,
            119.2832260131836,
            128.87771606445312
          ],
          "n": 8
        }
      ]
    },
    "comparison": {
      "delta_mean": 87.74035584926605,
      "idea_wins": 0,
      "n_pairs": 8,
      "per_seed_diffs": [
        115.14533233642578,
        85.08155632019043,
        75.67032241821289,
        84.09865379333496,
        75.38201999664307,
        50.8604793548584,
        103.04902267456055,
        112.63545989990234
      ],
      "p_value": 0.0081,
      "mde": 18.110913310349133,
      "mde_rel_pct": 118.27369023723402,
      "verdict": "idea worse (significant)",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "greedy selected scenarios cover the high-loss tail",
      "observed_selected_tail_fraction": 0.9,
      "observed_baseline_top_tail_fraction": 1.0,
      "selected_count": 20,
      "idea_train_mse": 100.62527465820312,
      "baseline_train_mse": 15.9457426071167,
      "confirmed": true
    },
    "core_math_check": {
      "proxy_epsilon_N_100_200_400_800": [
        0.1351292546497023,
        0.06756462732485115,
        0.033782313662425574,
        0.016891156831212787
      ],
      "monotone_decrease": true,
      "N": 400,
      "k": 20,
      "beta": 1e-05
    },
    "bench_report": {
      "track_justification": "tabular is the harness-matched registered track for optimizer/training-dynamics/calibration interventions",
      "custom_track": null,
      "protocol": "8 paired seeds; baseline and idea sweeps share the exact lr/epoch union",
      "budget_note": "400-sample, 12-epoch mlp_tiny; greedy compression followed by selected-set retraining"
    }
  },
  "system_verdict": "partial",
  "practical_verdict": "harms",
  "mechanism_ok": 1,
  "system_judged": true
}