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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 10,
  "verdict": "The registered dynamics track was rerun with 8 paired seeds, tuned baseline hyperparameters, matched rnn_small architecture, and idea settings using learning rates included in the baseline sweep. Baseline mean test MSE was 0.00459717 versus 0.79256819 for the best idea setting; paired delta was +0.78797102 with permutation p=0.0081, so the idea was significantly worse. The mechanism signature was included for transparency, but this parameter-space transfer does not establish the original latent-sampling claim.",
  "metrics": {
    "baseline": "mean test MSE=0.0045971721; best config lr=0.002, weight_decay=0.0001",
    "idea": "mean test MSE=0.7925681882; best config alpha=0.05, lr=0.0005, weight_decay=0.0001; paired delta=+0.7879710161; p=0.0081"
  },
  "bench_report": {
    "bench_version": 1,
    "track": "dynamics",
    "model": "rnn_small",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.002,
        "weight_decay": 0.0001
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.0005,
            "weight_decay": 0.0
          },
          "mean": 0.11454633437097073
        },
        {
          "cfg": {
            "lr": 0.0005,
            "weight_decay": 0.0001
          },
          "mean": 0.11465956829488277
        },
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0
          },
          "mean": 0.005499854683876038
        },
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0001
          },
          "mean": 0.005453515565022826
        },
        {
          "cfg": {
            "lr": 0.002,
            "weight_decay": 0.0
          },
          "mean": 0.004555012041237205
        },
        {
          "cfg": {
            "lr": 0.002,
            "weight_decay": 0.0001
          },
          "mean": 0.004531972575932741
        }
      ],
      "full": {
        "mean": 0.004597172053763643,
        "std": 0.0008625188036902094,
        "per_seed": [
          0.004187586717307568,
          0.006327331997454166,
          0.0038855248130857944,
          0.003727446775883436,
          0.005093815270811319,
          0.005022076889872551,
          0.004930851981043816,
          0.0036027419846504927
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.7925681881606579,
      "std": 0.47593743937124783,
      "per_seed": [
        0.9278137683868408,
        1.54276442527771,
        0.2908315360546112,
        1.384081244468689,
        0.39941179752349854,
        0.5426146984100342,
        1.049137830734253,
        0.20389020442962646
      ],
      "n": 8,
      "sweep": [
        {
          "cfg": {
            "lr": 0.002,
            "weight_decay": 0.0001,
            "alpha": 0.05
          },
          "mean": 2.3324266374111176
        },
        {
          "cfg": {
            "lr": 0.0005,
            "weight_decay": 0.0001,
            "alpha": 0.05
          },
          "mean": 0.7925681881606579
        },
        {
          "cfg": {
            "lr": 0.002,
            "weight_decay": 0.0001,
            "alpha": 0.05
          },
          "mean": 2.3324266374111176
        }
      ]
    },
    "comparison": {
      "delta_mean": 0.7879710161068942,
      "idea_wins": 0,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.9236261816695333,
        1.5364370932802558,
        0.2869460112415254,
        1.3803537976928055,
        0.3943179822526872,
        0.5375926215201616,
        1.0442069787532091,
        0.20028746244497597
      ],
      "p_value": 0.0081,
      "mde": 0.4250928421498136,
      "mde_rel_pct": 9246.833426688821,
      "verdict": "idea worse (significant)",
      "system_worked": false
    },
    "mechanism_signature": {
      "quantity": "state-dependent temperature coefficient in the trained-model parameter update",
      "predicted_slope": 0.6931471805599453,
      "observed_slope": 0.6931471805599447,
      "max_abs_error": 0.0,
      "confirmed": true
    },
    "transfer_note": "No built-in latent or energy-based sampler track exists; dynamics was used as the closest registered stability/control track. This tests a parameter-space Langevin transfer rather than latent-state sampling."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_experiment.py",
  "files": [
    "bench_experiment.py",
    "bench_report.json"
  ],
  "limitations": "The built-in bench lacks a latent posterior or energy-based sampling task, so ESS, metastability, Wasserstein distance, MALA acceptance, and latent sampling quality were not tested. The mechanism signature tests the coefficient scaling in the implemented update but does not validate sampling invariance on a neural latent posterior.",
  "system_verdict": "partial",
  "practical_verdict": "harms",
  "mechanism_ok": 1,
  "system_judged": true
}