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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 10,
  "verdict": "Implemented Minimal Negative-Curvature L-BFGS as an optimizer-only modification of the official tabular mlp_tiny training procedure, using the registered tabular track and Euclidean secant correction. The official benchmark produced identical baseline and idea test MSE: delta_mean=0.0 with permutation p=1.0; no negative-curvature pairs or corrections occurred across the paired runs. Therefore the idea did not demonstrate a significant improvement.",
  "metrics": {
    "baseline": "Official tabular/mlp_tiny: tuned best lr=0.12, epochs=15; full 8-seed mean test MSE 12.419063, std 4.653165; baseline sweep means: lr 0.03 -> 17.872909, lr 0.06 -> 18.236159, lr 0.12 -> 7.929520 on sweep seeds.",
    "idea": "Official tabular/mlp_tiny: best lr=0.12, epochs=15; 8-seed mean test MSE 12.419063, std 4.653165; identical per-seed results; 0 negative pairs, 0 corrected pairs; delta_mean=0.0; permutation p=1.0; verdict=no measurable effect."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "tabular",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.12,
        "epochs": 15
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.03,
            "epochs": 15
          },
          "mean": 17.87290906906128
        },
        {
          "cfg": {
            "lr": 0.06,
            "epochs": 15
          },
          "mean": 18.236158847808838
        },
        {
          "cfg": {
            "lr": 0.12,
            "epochs": 15
          },
          "mean": 7.929520010948181
        }
      ],
      "full": {
        "mean": 12.419063031673431,
        "std": 4.653164949772833,
        "per_seed": [
          7.232073783874512,
          9.054744720458984,
          8.681772232055664,
          6.7494893074035645,
          14.7591552734375,
          18.111841201782227,
          18.314743041992188,
          16.448684692382812
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 12.419063031673431,
      "std": 4.653164949772833,
      "per_seed": [
        7.232073783874512,
        9.054744720458984,
        8.681772232055664,
        6.7494893074035645,
        14.7591552734375,
        18.111841201782227,
        18.314743041992188,
        16.448684692382812
      ],
      "n": 8,
      "best_cfg": {
        "lr": 0.12,
        "epochs": 15
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.03,
            "epochs": 15
          },
          "mean": 22.84104561805725,
          "std": 11.555765903919722,
          "per_seed": [
            18.62317657470703,
            17.563692092895508,
            16.379343032836914,
            13.706111907958984,
            13.296585083007812,
            38.26602554321289,
            46.30095291137695,
            18.592477798461914
          ],
          "n": 8
        },
        {
          "cfg": {
            "lr": 0.06,
            "epochs": 15
          },
          "mean": 17.78099489212036,
          "std": 4.228547463617409,
          "per_seed": [
            9.133557319641113,
            17.798871994018555,
            16.796100616455078,
            15.525813102722168,
            24.372446060180664,
            17.10875129699707,
            21.30739402770996,
            20.20502471923828
          ],
          "n": 8
        },
        {
          "cfg": {
            "lr": 0.12,
            "epochs": 15
          },
          "mean": 12.419063031673431,
          "std": 4.653164949772833,
          "per_seed": [
            7.232073783874512,
            9.054744720458984,
            8.681772232055664,
            6.7494893074035645,
            14.7591552734375,
            18.111841201782227,
            18.314743041992188,
            16.448684692382812
          ],
          "n": 8
        }
      ]
    },
    "comparison": {
      "delta_mean": 0.0,
      "idea_wins": 0,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0
      ],
      "p_value": 1.0,
      "mde": 0.0,
      "mde_rel_pct": 0.0,
      "verdict": "no measurable effect",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "negative secant pairs are corrected to positive curvature; correction should occur only when the trained consecutive-gradient secant is negative",
      "baseline_observed": {
        "samples": 128,
        "mean_abs_parameter_input_gradient": 2.754227876663208,
        "finite_fraction": 1.0,
        "output_std": 3.702409267425537
      },
      "idea_observed": {
        "samples": 128,
        "mean_abs_parameter_input_gradient": 2.754227876663208,
        "finite_fraction": 1.0,
        "output_std": 3.702409267425537
      },
      "trained_pair_counts": {
        "baseline_negative_pairs": 0,
        "idea_negative_pairs": 0,
        "idea_corrected_pairs": 0
      },
      "confirmed": false,
      "measurement_note": "Behavior probes and pair counts are measured from trained official-tabular systems; no analytical toy identity is used."
    },
    "protocol_note": "Official registered tabular track; optimizer-only custom loop because the intervention changes training."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 official_stage2_bench.py",
  "files": [
    "official_stage2_bench.py",
    "bench_report.json"
  ],
  "limitations": "Only the structurally matched official tabular track was tested. The official baseline sweep uses its prescribed four sweep seeds, while the selected baseline and idea are evaluated on eight paired seeds. In this setup no negative-curvature pair appeared, so correction behavior, inverse-metric correction, sign-flip correction, damped L-BFGS, and performance under deliberately more nonconvex or noisy training conditions were not tested.",
  "system_verdict": "failed",
  "practical_verdict": "no_effect",
  "mechanism_ok": 0,
  "system_judged": true
}