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

(универсальный)

Ответ агента:

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built a custom periodic 3-D KPZ field forecasting track because no built-in track had PDE/spatial-field structure. Using the same trained KPZ-cell architecture and equal-budget paired protocol, the nonlinear KPZ variant achieved significantly lower MSE than diffusion-only: delta_mean=-0.00058644 with permutation p=0.0081. The trained-model mechanism signature was confirmed: observed idea slope 0.03117 versus predicted 0.03000, a 3.9% relative error.",
  "metrics": {
    "baseline": "Best diffusion-only configuration lr=0.006, lambda=0; 8-seed mean test MSE=0.0011265054, std=0.0000157286.",
    "idea": "Best KPZ configuration lr=0.006, lambda=2.4; 8-seed mean test MSE=0.0005400674, std=0.0000024417; all 8 paired seeds improved; relative reduction about 52.0%.",
    "paired_delta": "-0.0005864380",
    "permutation_p_value": "0.0081",
    "mechanism_signature": "Observed mean-increment slope 0.0311705 versus predicted dt*lambda/2=0.0300000; relative error 3.90%; confirmed=true"
  },
  "bench_report": {
    "bench_version": 1,
    "track": "custom_tracks/kpz_field_forecast",
    "model": "kpz_cell_shared",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.006,
        "lam": 0.0
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "lam": 0.0
          },
          "mean": 0.001372527563944459
        },
        {
          "cfg": {
            "lr": 0.003,
            "lam": 0.0
          },
          "mean": 0.0012717426288872957
        },
        {
          "cfg": {
            "lr": 0.006,
            "lam": 0.0
          },
          "mean": 0.0011299708858132362
        }
      ],
      "full": {
        "mean": 0.001126505361753516,
        "std": 1.5728558641491704e-05,
        "per_seed": [
          0.0011347912950441241,
          0.0011231173994019628,
          0.0011269556125625968,
          0.0011350192362442613,
          0.0010915156453847885,
          0.0011320485500618815,
          0.001149294781498611,
          0.0011193003738299012
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.000540067398105748,
      "std": 2.4417121177500956e-06,
      "per_seed": [
        0.0005460529937408864,
        0.0005373440217226744,
        0.00053932867012918,
        0.0005404861294664443,
        0.0005400783265940845,
        0.0005387364653870463,
        0.0005386892007663846,
        0.0005398233770392835
      ],
      "n": 8,
      "cfg": {
        "lr": 0.006,
        "lam": 2.4
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "lam": 1.2
          },
          "mean": 0.0007497890837839805
        },
        {
          "cfg": {
            "lr": 0.003,
            "lam": 1.8
          },
          "mean": 0.0005883654812350869
        },
        {
          "cfg": {
            "lr": 0.006,
            "lam": 2.4
          },
          "mean": 0.000540067398105748
        }
      ],
      "baseline_best_lr": 0.006
    },
    "comparison": {
      "delta_mean": -0.0005864379636477679,
      "idea_wins": 8,
      "n_pairs": 8,
      "per_seed_diffs": [
        -0.0005887383013032377,
        -0.0005857733776792884,
        -0.0005876269424334168,
        -0.000594533106777817,
        -0.000551437318790704,
        -0.0005933120846748352,
        -0.0006106055807322264,
        -0.0005794769967906177
      ],
      "p_value": 0.0081,
      "mde": 1.4053872303936461e-05,
      "mde_rel_pct": 1.2475637294846278,
      "verdict": "idea better (significant)",
      "system_worked": true
    },
    "mechanism_signature": {
      "prediction": "mean increment slope = dt*lambda/2 vs mean |gradient|^2",
      "rows": [
        {
          "system": "baseline",
          "observed_slope": 0.0002200861940903433,
          "predicted_slope": 0.0,
          "relative_error": 220.08619409034333,
          "test_mse": 0.0011347912950441241
        },
        {
          "system": "idea",
          "observed_slope": 0.031170539640519964,
          "predicted_slope": 0.03,
          "relative_error": 0.03901668746108347,
          "test_mse": 0.0005460529937408864
        }
      ],
      "confirmed": true
    },
    "custom_track": {
      "name": "kpz_field_forecast",
      "file": "kpz_track.py",
      "domain": "pde"
    },
    "protocol": {
      "paired_seeds": [
        0,
        1,
        2,
        3,
        4,
        5,
        6,
        7
      ],
      "baseline_sweep_seeds": [
        0,
        1,
        2,
        3
      ],
      "baseline_grid": [
        {
          "lr": 0.001,
          "lam": 0.0
        },
        {
          "lr": 0.003,
          "lam": 0.0
        },
        {
          "lr": 0.006,
          "lam": 0.0
        }
      ],
      "idea_grid": [
        {
          "lr": 0.001,
          "lam": 1.2
        },
        {
          "lr": 0.003,
          "lam": 1.8
        },
        {
          "lr": 0.006,
          "lam": 2.4
        }
      ],
      "epochs": 15,
      "n_train": 400,
      "n_test": 120
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 run_bench.py",
  "files": [
    "kpz_track.py",
    "run_bench.py",
    "bench_report.json"
  ],
  "limitations": "The custom task is a small one-step deterministic 8^3 synthetic PDE benchmark, not a long-horizon rollout or a real physical dataset. The implementation uses a fixed nonlinear coefficient per configuration, no stochastic forcing during supervised training, no learned residual branch, and does not estimate the 3+1-D KPZ roughening exponents.",
  "system_verdict": "worked",
  "practical_verdict": "helps",
  "mechanism_ok": 1,
  "system_judged": true
}