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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Built and evaluated the holonomy-regularized recurrent system on the registered dynamics track with the same 64-unit GRU backbone as the baseline. The idea achieved lower mean MSE (0.0004547013 vs 0.0005451723) and won 7/8 paired seeds, but the permutation p-value was 0.07265, so the required significant-win criterion was not met. The trained-model perturbation signature was confirmed at observed 1.0 versus predicted 1.0, but mechanism confirmation does not make the task-metric result a significant win.",
  "metrics": {
    "baseline": "Best lr=0.01; mean test MSE 0.0005451723 over 8 seeds.",
    "idea": "lr=0.01, cycle_weight=0.03; mean test MSE 0.0004547013 over 8 seeds; paired delta -0.0000904710; 7/8 wins; p=0.07265."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "dynamics",
    "model": "rnn_small",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.01,
        "cycle_weight": 0.0
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "cycle_weight": 0.0
          },
          "mean": 0.001864717691205442
        },
        {
          "cfg": {
            "lr": 0.003,
            "cycle_weight": 0.0
          },
          "mean": 0.0010901302302954718
        },
        {
          "cfg": {
            "lr": 0.01,
            "cycle_weight": 0.0
          },
          "mean": 0.0006186682076076977
        }
      ],
      "full": {
        "mean": 0.00054517226817552,
        "std": 0.00020361842727135583,
        "per_seed": [
          0.0004930912400595844,
          0.0004765945195686072,
          0.0004673648509196937,
          0.0010376222198829055,
          0.000511663849465549,
          0.00027233464061282575,
          0.0005571767687797546,
          0.0005455300561152399
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.00045470128861779813,
      "std": 0.00014832940019044596,
      "per_seed": [
        0.0004892470315098763,
        0.0003560753830242902,
        0.0005445053684525192,
        0.0007670727791264653,
        0.00048597707063890994,
        0.00024241428764071316,
        0.0003992895071860403,
        0.0003530288813635707
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -9.047097955772188e-05,
      "idea_wins": 7,
      "n_pairs": 8,
      "per_seed_diffs": [
        -3.844208549708128e-06,
        -0.00012051913654431701,
        7.714051753282547e-05,
        -0.00027054944075644016,
        -2.5686778826639056e-05,
        -2.9920352972112596e-05,
        -0.00015788726159371436,
        -0.00019250117475166917
      ],
      "p_value": 0.07265,
      "mde": 9.572692629336764e-05,
      "mde_rel_pct": 17.559023428269473,
      "verdict": "no significant win",
      "system_worked": false
    },
    "mechanism_signature": {
      "cfg": {
        "lr": 0.01,
        "cycle_weight": 0.03
      },
      "behavior": {
        "prediction": "small input perturbations preserve decoded joint state",
        "predicted": 1.0,
        "observed": 1.0,
        "tolerance": 0.1,
        "confirmed": true,
        "n_models": 8
      }
    },
    "idea_sweep": [
      {
        "cfg": {
          "lr": 0.01,
          "cycle_weight": 0.03
        },
        "mean": 0.00045470128861779813,
        "std": 0.00014832940019044596,
        "per_seed": [
          0.0004892470315098763,
          0.0003560753830242902,
          0.0005445053684525192,
          0.0007670727791264653,
          0.00048597707063890994,
          0.00024241428764071316,
          0.0003992895071860403,
          0.0003530288813635707
        ]
      },
      {
        "cfg": {
          "lr": 0.001,
          "cycle_weight": 0.03
        },
        "mean": 0.0023312663834076375,
        "std": 0.0007589181455697688,
        "per_seed": [
          0.0011521552223712206,
          0.0023543194402009253,
          0.0021086849737912416,
          0.002465922152623534,
          0.0014372181612998247,
          0.0023133959621191025,
          0.0035222345031797886,
          0.0032962006516754627
        ]
      },
      {
        "cfg": {
          "lr": 0.01,
          "cycle_weight": 0.03
        },
        "mean": 0.00045470128861779813,
        "std": 0.00014832940019044596,
        "per_seed": [
          0.0004892470315098763,
          0.0003560753830242902,
          0.0005445053684525192,
          0.0007670727791264653,
          0.00048597707063890994,
          0.00024241428764071316,
          0.0003992895071860403,
          0.0003530288813635707
        ]
      }
    ],
    "notes": "Matched dynamics task; baseline used canonical train_model, while the idea used the same GRU backbone with a differentiable factor-state regularizer."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "stage2_bench.py",
    "bench_report.json"
  ],
  "limitations": "The implementation uses a cycle-inspired factor-state regularizer rather than a full learned finite transition-monoid closure. No FLOP-normalized or variable-horizon comparison was run, and the favorable point estimate did not reach permutation-test significance.",
  "system_verdict": "failed",
  "practical_verdict": "inconclusive",
  "mechanism_ok": 0,
  "system_judged": true
}