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

(универсальный)

Ответ агента:

{
  "worked": true,
  "confidence": 8,
  "verdict": "The Semiglobal-PL scheduler was implemented as a matched SGD intervention on the registered tabular Friedman#1 track. It significantly improved test MSE across all eight paired seeds, but its neural-scale q-to-log-gap mechanism signature was not confirmed quantitatively.",
  "metrics": {
    "baseline": "Fixed SGD, mlp_tiny, best lr=0.009: test MSE 200.0606 +/- 11.0770.",
    "idea": "Semiglobal-PL scheduler, same setup, best lr=0.009: test MSE 185.3659 +/- 11.0952; paired delta -14.6948; p=0.0081; 8/8 wins."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "tabular",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.009
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 225.13445281982422
        },
        {
          "cfg": {
            "lr": 0.006
          },
          "mean": 214.2943878173828
        },
        {
          "cfg": {
            "lr": 0.009
          },
          "mean": 201.62980270385742
        }
      ],
      "full": {
        "mean": 200.0606346130371,
        "std": 11.077029829348923,
        "per_seed": [
          212.36439514160156,
          200.49159240722656,
          205.28561401367188,
          188.3776092529297,
          203.29046630859375,
          185.93289184570312,
          187.44577026367188,
          217.29673767089844
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 185.36586952209473,
      "std": 11.095241493609498,
      "per_seed": [
        195.4981231689453,
        187.09390258789062,
        192.18927001953125,
        175.18148803710938,
        185.1798553466797,
        171.63917541503906,
        171.85842895507812,
        204.28671264648438
      ],
      "n": 8,
      "cfg": {
        "lr": 0.009
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 222.8292999267578
        },
        {
          "cfg": {
            "lr": 0.006
          },
          "mean": 206.63719177246094
        },
        {
          "cfg": {
            "lr": 0.009
          },
          "mean": 187.49069595336914
        }
      ]
    },
    "comparison": {
      "delta_mean": -14.694765090942383,
      "idea_wins": 8,
      "n_pairs": 8,
      "per_seed_diffs": [
        -16.86627197265625,
        -13.397689819335938,
        -13.096343994140625,
        -13.196121215820312,
        -18.110610961914062,
        -14.293716430664062,
        -15.58734130859375,
        -13.010025024414062
      ],
      "p_value": 0.0081,
      "mde": 1.6318810404189403,
      "mde_rel_pct": 0.8156932239945007,
      "verdict": "idea better (significant)",
      "system_worked": true
    },
    "mechanism_signature": {
      "quantity": "Tail log(training loss gap) slope versus -mean(q^2), measured during trained neural-network runs.",
      "observed_mean": -0.039613116657370454,
      "predicted_mean": -359271464354.93317,
      "relative_error_mean": 0.9999999999998959,
      "tolerance": 0.2,
      "confirmed": false,
      "switch_fraction": 1.0
    },
    "audit": {
      "baseline_lr_grid": [
        0.003,
        0.006,
        0.009
      ],
      "idea_lr_grid": [
        0.003,
        0.006,
        0.009
      ],
      "baseline_selected_lr": 0.009,
      "idea_selected_lr": 0.009,
      "epochs": 18,
      "batch": 128,
      "clip_norm": 1.0,
      "domain_rationale": "tabular is the prescribed structural track for optimizer and learning-rate schedule ideas; tabular is a registered built-in bench track."
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_run.py > bench_report.json",
  "files": [
    "bench_run.py",
    "bench_report.json"
  ],
  "limitations": "Only the registered tabular optimizer track was tested; vision, sequence, and dynamics were not run. The benchmark used the small 400/200 Friedman#1 split, mlp_tiny, SGD, 18 epochs, batch 128, and three learning rates. The q diagnostic was numerically degenerate near the loss floor because the smoothed running f* estimate made the gap extremely small, so the mechanism signature was not confirmed despite the significant end-to-end win.",
  "system_verdict": "worked",
  "practical_verdict": "helps",
  "mechanism_ok": 0,
  "system_judged": true
}