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

(универсальный)

Ответ агента:

{
  "worked": true,
  "confidence": 8,
  "verdict": "Implemented Observability-Gated Spectral Phase Initialization on a structurally matched custom graph-synchronization regression track using identical mlp_tiny systems and a shared learning-rate sweep. The idea significantly improved test MSE: baseline mean 0.718486 versus idea mean 0.217423, paired delta -0.501063, permutation p=0.0081, with 8/8 paired wins. The trained-model mechanism signature confirmed the predicted downstream benefit, although this MVP uses preprocessing alignment rather than a learned GNN phase layer.",
  "metrics": {
    "baseline": "Best lr=0.01; full 8-seed MSE mean 0.718486, std 0.600536; sweep means lr=0.001: 0.554454, lr=0.003: 0.509331, lr=0.01: 0.487007.",
    "idea": "Best lr=0.003; full 8-seed MSE mean 0.217423, std 0.075185; sweep means lr=0.001: 0.245355, lr=0.003: 0.217423, lr=0.01: 0.290202.",
    "paired_delta_mean": -0.5010633105412126,
    "permutation_p_value": 0.0081,
    "idea_wins": 8,
    "mechanism_signature": {
      "predicted_baseline_mse": 0.4704003967344761,
      "observed_aligned_mse": 0.3038702141493559,
      "observed_aligned_residual_std": 0.4300309270620346,
      "confirmed": true
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_phase.py",
  "files": [
    "phase_track.py",
    "bench_phase.py",
    "bench_report.json"
  ],
  "limitations": "The benchmark uses a custom lightweight regression track rather than a built-in GNN track because no built-in track exposes multi-node pairwise relative-phase observations. It tests preprocessing spectral alignment with an MLP, not differentiable Jacobian-gated refinement, WLS refinement, downstream GCN classification, wall-clock speed, or robustness across graph sparsity/noise sweeps.",
  "bench_report": {
    "bench_version": 1,
    "track": "phase_synchronization_regression",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.01
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001
          },
          "mean": 0.5544544011354446
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 0.5093307048082352
        },
        {
          "cfg": {
            "lr": 0.01
          },
          "mean": 0.4870074987411499
        }
      ],
      "full": {
        "mean": 0.7184864804148674,
        "std": 0.6005355689639164,
        "per_seed": [
          0.5706357359886169,
          0.43496403098106384,
          0.42483416199684143,
          0.5175960659980774,
          0.4055282473564148,
          0.73640376329422,
          0.3769463896751404,
          2.2809834480285645
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.21742316987365484,
      "std": 0.0751846884485342,
      "per_seed": [
        0.21176113188266754,
        0.1522442102432251,
        0.22154822945594788,
        0.09710203856229782,
        0.3232591152191162,
        0.24092552065849304,
        0.1655595749616623,
        0.32698553800582886
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -0.5010633105412126,
      "idea_wins": 8,
      "n_pairs": 8,
      "per_seed_diffs": [
        -0.3588746041059494,
        -0.28271982073783875,
        -0.20328593254089355,
        -0.42049402743577957,
        -0.08226913213729858,
        -0.49547824263572693,
        -0.2113868147134781,
        -1.9539979100227356
      ],
      "p_value": 0.0081,
      "mde": 0.5030248995072959,
      "mde_rel_pct": 70.01174179600986,
      "verdict": "idea better (significant)",
      "system_worked": true
    },
    "custom_track": {
      "name": "phase_synchronization_regression",
      "file": "phase_track.py",
      "domain": "geometry/graph synchronization"
    },
    "idea_sweep": [
      {
        "cfg": {
          "lr": 0.001
        },
        "result": {
          "mean": 0.2453548777848482,
          "std": 0.07094670006301455,
          "per_seed": [
            0.27867943048477173,
            0.1840101033449173,
            0.20311710238456726,
            0.15075239539146423,
            0.3714803159236908,
            0.24977633357048035,
            0.19869858026504517,
            0.3263247609138489
          ],
          "n": 8
        }
      },
      {
        "cfg": {
          "lr": 0.003
        },
        "result": {
          "mean": 0.21742316987365484,
          "std": 0.0751846884485342,
          "per_seed": [
            0.21176113188266754,
            0.1522442102432251,
            0.22154822945594788,
            0.09710203856229782,
            0.3232591152191162,
            0.24092552065849304,
            0.1655595749616623,
            0.32698553800582886
          ],
          "n": 8
        }
      },
      {
        "cfg": {
          "lr": 0.01
        },
        "result": {
          "mean": 0.2902024118229747,
          "std": 0.1461298164928991,
          "per_seed": [
            0.3044014573097229,
            0.08412133902311325,
            0.27784958481788635,
            0.13880692422389984,
            0.478528767824173,
            0.47114890813827515,
            0.14776097238063812,
            0.41900134086608887
          ],
          "n": 8
        }
      }
    ],
    "mechanism_signature": {
      "prediction": "spectral alignment reduces downstream test MSE and residual spread",
      "predicted_baseline_mse": 0.4704003967344761,
      "observed_aligned_mse": 0.3038702141493559,
      "observed_aligned_residual_std": 0.4300309270620346,
      "confirmed": true
    },
    "runtime_sec": 49.546178102493286
  },
  "system_verdict": "worked",
  "practical_verdict": "helps",
  "mechanism_ok": 1,
  "system_judged": true
}