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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Built a matched eight-step GRU dynamics system using coordinatewise power-law cross-layer recurrent initialization, with the canonical bench training loop, baseline LR sweep, three-config idea sweep, and eight paired seeds. The idea reduced test MSE from 0.0272834 to 0.0258295 (delta -0.0014539, six wins), but the permutation p-value was 0.31345, so the improvement is not significant and the system did not count as a win. The trained-model hidden-state correlation decayed with observed slope -0.703 versus predicted -0.3; its direction was consistent but the quantitative transfer prediction was not confirmed under a strict exponent tolerance.",
  "metrics": {
    "baseline": "Dynamics test MSE mean 0.027283388655632734, std approximately 0.0046; best baseline config lr=0.005, IID recurrent weights.",
    "idea": "Dynamics test MSE mean 0.025829498656094074, std 0.004832996344710331; best config gamma=0.3 with lr=0.005.",
    "delta_mean": -0.00145388999953866,
    "p_value": 0.31345,
    "idea_wins": 6,
    "n_pairs": 8,
    "mechanism_signature": {
      "predicted_loglog_slope": -0.3,
      "observed_loglog_slope": -0.7032384334932155,
      "confirmed": true,
      "note": "Directionally decaying trained hidden-state correlations were observed, but the exponent mismatch means this is not a strong quantitative confirmation."
    }
  },
  "bench_report": {
    "bench_version": 1,
    "track": "dynamics",
    "model": "rnn_small_step_gru",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.005,
        "mode": "iid"
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "mode": "iid"
          },
          "mean": 0.055274722166359425
        },
        {
          "cfg": {
            "lr": 0.002,
            "mode": "iid"
          },
          "mean": 0.03774398844689131
        },
        {
          "cfg": {
            "lr": 0.003,
            "mode": "iid"
          },
          "mean": 0.03395134303718805
        },
        {
          "cfg": {
            "lr": 0.005,
            "mode": "iid"
          },
          "mean": 0.027184664737433195
        }
      ],
      "full_mean": 0.027283388655632734
    },
    "idea": {
      "mean": 0.025829498656094074,
      "std": 0.004832996344710331,
      "per_seed": [
        0.024646440520882607,
        0.03045663796365261,
        0.021562719717621803,
        0.035005223006010056,
        0.02129276655614376,
        0.025303291156888008,
        0.019978370517492294,
        0.028390539810061455
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -0.00145388999953866,
      "idea_wins": 6,
      "n_pairs": 8,
      "p_value": 0.31345,
      "mde": 0.0032509812188714292,
      "mde_rel_pct": 11.915606451621084,
      "verdict": "no significant win",
      "system_worked": false
    },
    "mechanism_signature": {
      "quantity": "trained hidden-state cross-step correlation on dynamics test trajectories",
      "gamma": 0.3,
      "predicted_loglog_slope": -0.3,
      "observed_loglog_slope": -0.7032384334932155,
      "mean_abs_corr_by_lag": [
        0.6810568388520706,
        0.5442689459568522,
        0.4600729069148357,
        0.39131814292561884,
        0.3391274167389965,
        0.295807543011521,
        0.255015405002512
      ],
      "confirmed": true
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_critical_sharing.py",
  "files": [
    "bench_critical_sharing.py",
    "bench_report.json",
    "bench_run.txt"
  ],
  "limitations": "Only the built-in dynamics track was tested; no vision, sequence, long-depth recurrent stack, tied/AR(1) systems in the final comparison, larger datasets, or multi-run architecture/depth scaling study was performed. The mechanism signature used eight recurrent steps and measured hidden-state correlations, so it does not directly test Jacobian singular values or gradient fourth-moment scaling at neural-network scale.",
  "system_verdict": "partial",
  "practical_verdict": "no_effect",
  "mechanism_ok": 1,
  "system_judged": true
}