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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Built a local change-gated training loop for the standard sequence forecast benchmark using the shared transformer_tiny architecture and paired 8-seed evaluation. The best gated configuration (lr=0.001, gamma=10) reduced mean test MSE from 0.351743 to 0.276914, but the paired permutation p-value was 0.14135, so it did not meet the required significant-win criterion. The NN-scale EMA prediction was confirmed: predicted half-response 6.58 steps versus observed 7 steps, and the observed learning-rate multiplier increased with the gate.",
  "metrics": {
    "baseline": "sequence/transformer_tiny; sweep lr={0.001,0.003,0.01}, best lr=0.003; full 8-seed mean test MSE=0.3517431132, std=0.0977175266",
    "idea": "best gated config lr=0.001, gamma=10; 8-seed mean test MSE=0.2769142557, std=0.0838364339; paired delta=-0.0748288576, 7/8 wins, permutation p=0.14135",
    "mechanism_signature": {
      "prediction": "Persistent detector response reaches q=0.5 after the EMA half-response and the update multiplier increases with q.",
      "predicted_half_response_steps": 6.578813478960585,
      "observed_half_response_steps": 7,
      "observed_mean_q": 0.1630945738877472,
      "observed_mean_eta_multiplier": 1.652378295550989,
      "predicted_eta_multiplier_at_q1": 5.0,
      "confirmed": true
    }
  },
  "bench_report": {
    "bench_version": 1,
    "track": "sequence",
    "model": "transformer_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.003,
        "gamma": 1.0
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "gamma": 1.0
          },
          "mean": 0.3070925883948803
        },
        {
          "cfg": {
            "lr": 0.003,
            "gamma": 1.0
          },
          "mean": 0.3059298060834408
        },
        {
          "cfg": {
            "lr": 0.01,
            "gamma": 1.0
          },
          "mean": 1.3700615465641022
        }
      ],
      "full": {
        "mean": 0.35174311324954033,
        "std": 0.09771752658241298,
        "n": 8
      }
    },
    "idea": {
      "best_cfg": {
        "lr": 0.001,
        "gamma": 10.0
      },
      "mean": 0.2769142556935549,
      "std": 0.08383643390587397,
      "n": 8
    },
    "comparison": {
      "delta_mean": -0.07482885755598545,
      "idea_wins": 7,
      "n_pairs": 8,
      "p_value": 0.14135,
      "verdict": "no significant win",
      "system_worked": false
    },
    "mechanism_signature": {
      "predicted_half_response_steps": 6.578813478960585,
      "observed_half_response_steps": 7,
      "observed_mean_q": 0.1630945738877472,
      "observed_mean_eta_multiplier": 1.652378295550989,
      "confirmed": true
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "stage2_bench.py",
    "bench_report.json"
  ],
  "limitations": "The detector used detached prediction residual magnitude as its compact score; it did not train a separate CPDNet MLP or fully incorporate the transformer's hidden z feature into the detector input. No explicit abrupt regime-change stream or online deployment phase was available in the fixed sequence track, so the experiment tests transfer of gated optimization behavior rather than post-change recovery. The baseline sweep used four seeds for selection and eight seeds for final evaluation as specified; no wall-clock comparison was run.",
  "system_verdict": "partial",
  "practical_verdict": "inconclusive",
  "mechanism_ok": 1,
  "system_judged": true
}