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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented differentiable two-vector JVP/QR projective-gap regularization for the shared rnn_small GRU on the structurally matched dynamics track. The best idea configuration had lower mean test MSE than the tuned baseline (0.037517 versus 0.040376), but the paired permutation test was not significant (delta=-0.002859, p=0.10075), so there is no validated training win. The trained-model mechanism signature also failed quantitatively: observed projective slope -0.03503 versus predicted -0.08474, relative error 58.7%.",
  "metrics": {
    "baseline": "dynamics/rnn_small, 8 seeds, best lr=0.002, mean test MSE=0.0403763, std=0.0196119",
    "idea": "dynamics/rnn_small with JVP/QR gap and top-exponent penalty, 8 seeds, best lr=0.002 and kappa=0.05, mean test MSE=0.0375170, std=0.0202588, paired delta=-0.0028594, permutation p=0.10075",
    "mechanism_signature": {
      "observed_log_angle_slope": -0.035032859627017375,
      "predicted_minus_gap": -0.08474337961524725,
      "observed_gap": 0.08474337961524725,
      "relative_error": 0.5866006313876798,
      "confirmed": false
    }
  },
  "bench_report": {
    "bench_version": 1,
    "track": "dynamics",
    "model": "rnn_small",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.002
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001
          },
          "mean": 0.36154668405652046
        },
        {
          "cfg": {
            "lr": 0.002
          },
          "mean": 0.04037633468396962
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 0.05718149617314339
        }
      ],
      "full": {
        "mean": 0.04037633468396962,
        "std": 0.019611854198954906,
        "per_seed": [
          0.014195265248417854,
          0.05633978545665741,
          0.01657027006149292,
          0.06460500508546829,
          0.021465152502059937,
          0.04150689020752907,
          0.04312236234545708,
          0.06520594656467438
        ],
        "n": 8
      }
    },
    "idea": {
      "config": {
        "lr": 0.002,
        "kappa": 0.05,
        "lambda_star": -0.02,
        "gamma_star": 0.05,
        "epochs": 4
      },
      "mean": 0.03751695808023214,
      "std": 0.020258781501441284,
      "per_seed": [
        0.01551117654889822,
        0.05140165239572525,
        0.008671858347952366,
        0.06345316767692566,
        0.025820285081863403,
        0.03666491433978081,
        0.037566497921943665,
        0.061046112328767776
      ]
    },
    "comparison": {
      "delta_mean": -0.0028593766037374735,
      "idea_wins": 6,
      "n_pairs": 8,
      "p_value": 0.10075,
      "verdict": "no significant win",
      "system_worked": false
    },
    "mechanism_signature": {
      "observed_log_angle_slope": -0.035032859627017375,
      "predicted_minus_gap": -0.08474337961524725,
      "observed_gap": 0.08474337961524725,
      "relative_error": 0.5866006313876798,
      "confirmed": false
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "stage2_bench.py",
    "bench_report.json"
  ],
  "limitations": "The benchmark used 4 epochs and 400 training/400 test samples to fit the execution budget; no longer training schedule, larger model, or additional task tracks were tested. The signature used short-window trained-GRU JVP behavior and did not meet the 30% quantitative tolerance.",
  "system_verdict": "failed",
  "practical_verdict": "inconclusive",
  "mechanism_ok": 0,
  "system_judged": true
}