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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented a BT monitor for the structurally matched dynamics track using the shared rnn_small recurrent architecture, centered finite differences, and the smallest right-singular-vector direction. Across 8 paired seeds and a three-point shared learning-rate sweep, the idea and baseline had identical test MSEs (delta_mean=0, permutation p=1.0), and the near-degeneracy fraction was unchanged (0.75 vs 0.75), so neither the promised mechanism nor a training win was observed.",
  "metrics": {
    "baseline": "Best lr=0.006, weight_decay=0; full 8-seed test MSE mean 0.0009940864, std 0.0002634151; sweep means: lr 0.0015=0.00209760, 0.003=0.00122624, 0.006=0.00100919.",
    "idea": "Best idea lr=0.006, weight_decay=0; full 8-seed test MSE mean 0.0009940864, std 0.0002634151. Paired delta_mean=0.0, p=1.0, wins=0/8; |ab| below 0.0004 fraction 0.75, identical to baseline."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "stage2_bench.py",
    "bench_report.json"
  ],
  "limitations": "The monitor was applied at a zero hidden-state probe of the GRU recurrent map rather than solving per-checkpoint equilibria; it used a compact 400-train/200-test subset and 15 epochs for budget reasons. It did not test longer rollouts, alternative equilibrium-search strategies, larger latent projections, or a sweep over BT penalty strengths. The differentiable monitor is computationally expensive and its effect was numerically negligible in this setup.",
  "bench_report": {
    "bench_version": 1,
    "track": "dynamics",
    "model": "rnn_small",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.006,
        "weight_decay": 0.0
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.0015,
            "weight_decay": 0.0
          },
          "mean": 0.0020976006926503032
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.0
          },
          "mean": 0.0012262430100236088
        },
        {
          "cfg": {
            "lr": 0.006,
            "weight_decay": 0.0
          },
          "mean": 0.0010091856674989685
        }
      ],
      "full": {
        "mean": 0.0009940863528754562,
        "std": 0.0002634151189555855,
        "per_seed": [
          0.0007214772049337626,
          0.0010892449645325541,
          0.0007245641318149865,
          0.001501456368714571,
          0.0007053734152577817,
          0.0009875509422272444,
          0.001235381467267871,
          0.0009876423282548785
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.0009940863528754562,
      "std": 0.0002634151189555855,
      "per_seed": [
        0.0007214772049337626,
        0.0010892449645325541,
        0.0007245641318149865,
        0.001501456368714571,
        0.0007053734152577817,
        0.0009875509422272444,
        0.001235381467267871,
        0.0009876423282548785
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": 0.0,
      "idea_wins": 0,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0
      ],
      "p_value": 1.0,
      "mde": 0.0,
      "mde_rel_pct": 0.0,
      "verdict": "no measurable effect",
      "system_worked": false
    },
    "mechanism_signature": {
      "predicted": "BT penalty reduces fraction of trained models with |a*b| below threshold",
      "threshold": 0.0004,
      "baseline_near_degenerate_fraction": 0.75,
      "idea_near_degenerate_fraction": 0.75,
      "confirmed": false
    }
  },
  "system_verdict": "failed",
  "practical_verdict": "no_effect",
  "mechanism_ok": 0,
  "system_judged": true
}