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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Built a custom structurally matched manufactured PDE track and paired neural-network benchmark using identical independently trained MLP experts. The baseline's full-diagnostic selection achieved mean test MSE 0.0010874, while shared-response ranking achieved 0.0018048 (delta +0.0007174, p=0.252, 2/8 wins), so there is no significant accuracy win. The mechanism signature matched the observed best expert on only 3/8 seeds, although diagnostic calls were reduced 3x.",
  "metrics": {
    "baseline": "mean test MSE 0.0010874417348532006; per-seed [0.0010367966, 0.0011202694, 0.0009211542, 0.0013915685, 0.0007677504, 0.0012855530, 0.0010599738, 0.0011164680]; best_cfg lr=0.01, scale=0.5; 3 residual evaluations",
    "idea": "mean test MSE 0.0018048072524834424; per-seed [0.0010367966, 0.0011202694, 0.0009211542, 0.0048554414, 0.0020367303, 0.0011935772, 0.0009934523, 0.0022810367]; best_cfg lr=0.01, scale=0.5; 1 residual evaluation",
    "paired_delta": "0.0007173655176302418 (idea - baseline; positive is worse)",
    "permutation_p_value": 0.252,
    "idea_wins": 2,
    "n_pairs": 8,
    "mechanism_signature": {
      "top1_matches": 3,
      "n": 8,
      "confirmed": false,
      "diagnostic_calls": {
        "baseline": 3,
        "idea": 1
      }
    }
  },
  "bench_report": {
    "bench_version": 1,
    "track": "shared_response_pde",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.01,
        "scale": 0.5
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.002,
            "scale": 0.5
          },
          "mean": 0.005421972367912531
        },
        {
          "cfg": {
            "lr": 0.002,
            "scale": 1.0
          },
          "mean": 0.005421972367912531
        },
        {
          "cfg": {
            "lr": 0.002,
            "scale": 1.5
          },
          "mean": 0.005421972367912531
        },
        {
          "cfg": {
            "lr": 0.005,
            "scale": 0.5
          },
          "mean": 0.001959583372808993
        },
        {
          "cfg": {
            "lr": 0.005,
            "scale": 1.0
          },
          "mean": 0.001959583372808993
        },
        {
          "cfg": {
            "lr": 0.005,
            "scale": 1.5
          },
          "mean": 0.001959583372808993
        },
        {
          "cfg": {
            "lr": 0.01,
            "scale": 0.5
          },
          "mean": 0.0011174471583217382
        },
        {
          "cfg": {
            "lr": 0.01,
            "scale": 1.0
          },
          "mean": 0.0011174471583217382
        },
        {
          "cfg": {
            "lr": 0.01,
            "scale": 1.5
          },
          "mean": 0.0011174471583217382
        }
      ],
      "full": {
        "mean": 0.0010874417348532006,
        "std": 0.0001826317353798132,
        "per_seed": [
          0.0010367966024205089,
          0.0011202694149687886,
          0.0009211541619151831,
          0.0013915684539824724,
          0.0007677504327148199,
          0.001285552978515625,
          0.0010599738452583551,
          0.001116467989049852
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.0018048072524834424,
      "std": 0.0012479045307866915,
      "per_seed": [
        0.0010367966024205089,
        0.0011202694149687886,
        0.0009211541619151831,
        0.004855441395193338,
        0.002036730293184519,
        0.001193577190861106,
        0.0009934522677212954,
        0.0022810366936028004
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": 0.0007173655176302418,
      "idea_wins": 2,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.0,
        0.0,
        0.0,
        0.003463872941210866,
        0.001268979860469699,
        -9.197578765451908e-05,
        -6.652157753705978e-05,
        0.0011645687045529485
      ],
      "p_value": 0.252,
      "mde": 0.001041526980281041,
      "mde_rel_pct": 95.77772738524168,
      "verdict": "no significant win",
      "system_worked": false
    },
    "mechanism_signature": {
      "top1_matches": 3,
      "n": 8,
      "predicted_vs_observed": [
        {
          "predicted": 0,
          "observed": 1,
          "match": false
        },
        {
          "predicted": 1,
          "observed": 1,
          "match": true
        },
        {
          "predicted": 2,
          "observed": 2,
          "match": true
        },
        {
          "predicted": 2,
          "observed": 0,
          "match": false
        },
        {
          "predicted": 2,
          "observed": 1,
          "match": false
        },
        {
          "predicted": 0,
          "observed": 1,
          "match": false
        },
        {
          "predicted": 0,
          "observed": 0,
          "match": true
        },
        {
          "predicted": 2,
          "observed": 0,
          "match": false
        }
      ],
      "confirmed": false,
      "custom_track": {
        "name": "shared_response_pde",
        "file": "pde_track.py",
        "domain": "pde"
      },
      "diagnostic_calls": {
        "baseline": 3,
        "idea": 1
      }
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 run_bench.py",
  "files": [
    "run_bench.py",
    "pde_track.py",
    "bench_report.json",
    "bench_stdout.txt"
  ],
  "limitations": "The experiment used a small manufactured 1D PDE, three MLP experts, 12 epochs, and finite expert libraries rather than a large neural operator or nonlinear 2D PDE. Wall-clock speedup was not measured; only residual evaluation count was compared. The residual correction used a fixed analytic scaling rather than an iterative Jacobian/preconditioned solve, and no soft weighted-combination variant or confidence certification was tested.",
  "system_verdict": "failed",
  "practical_verdict": "inconclusive",
  "mechanism_ok": 0,
  "system_judged": true
}