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

(универсальный)

Ответ агента:

{
  "worked": true,
  "confidence": 8,
  "verdict": "Implemented delay-aware event-triggered SGD on the structurally matched dynamics track with shared rnn_small models. The idea achieved lower mean test MSE than tuned SGD (0.103706 vs 0.107771), winning all 8 paired seeds with permutation p=0.0081. The trained-model mechanism signature was not confirmed because the short run produced too few measurable events and did not establish the predicted epsilon ordering.",
  "metrics": {
    "baseline": "rnn_small dynamics, SGD lr=0.01: mean test MSE 0.107771, std 0.032131.",
    "idea": "Delayed event-triggered SGD, lr=0.01, epsilon=0.2, delay=3: mean test MSE 0.103706, std 0.031398; paired delta -0.004066, 8/8 wins, p=0.0081.",
    "communication": "Mean recorded event rate 0.0167; minimum event gap was not observable in the collected signature."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "dynamics",
    "model": "rnn_small",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.01
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001
          },
          "mean": 0.662848949432373
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 0.5023095160722733
        },
        {
          "cfg": {
            "lr": 0.01
          },
          "mean": 0.11683708801865578
        }
      ],
      "full": {
        "mean": 0.10777114145457745,
        "std": 0.03213141448403277,
        "per_seed": [
          0.06530865281820297,
          0.12113738805055618,
          0.10681308805942535,
          0.1740892231464386,
          0.07807688415050507,
          0.09743201732635498,
          0.08864468336105347,
          0.13066719472408295
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.1037055691704154,
      "std": 0.03139804758362597,
      "per_seed": [
        0.06182863563299179,
        0.11740742623806,
        0.10329566895961761,
        0.16818949580192566,
        0.07418211549520493,
        0.09319097548723221,
        0.0858229398727417,
        0.12572729587554932
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -0.0040655722841620445,
      "idea_wins": 8,
      "n_pairs": 8,
      "per_seed_diffs": [
        -0.0034800171852111816,
        -0.0037299618124961853,
        -0.0035174190998077393,
        -0.0058997273445129395,
        -0.0038947686553001404,
        -0.004241041839122772,
        -0.0028217434883117676,
        -0.00493989884853363
      ],
      "p_value": 0.0081,
      "mde": 0.0008052187660382514,
      "mde_rel_pct": 0.7471562007883426,
      "verdict": "idea better (significant)",
      "system_worked": true
    },
    "selected_idea_cfg": {
      "lr": 0.01,
      "epsilon": 0.2
    },
    "mechanism_signature": {
      "trained_model_measurements": true,
      "prediction": "Triggering yields positive inter-event gaps and higher epsilon reduces event frequency.",
      "predicted_min_gap_steps": 1,
      "observed_min_gap_steps": null,
      "predicted_event_rate_ordering": "epsilon_0.05 > epsilon_0.20",
      "observed_mean_test_mse_epsilon_0.05": 0.10584526043385267,
      "observed_mean_test_mse_epsilon_0.20": 0.11268030665814877,
      "observed_event_rate_mean": 0.016666666666666666,
      "observed_event_rate_std": 0.0,
      "observed_delayed_energy_ratio_median": 1.0001609402113842,
      "confirmed": false
    },
    "runtime_config": {
      "epochs": 15,
      "batch": 128,
      "delay": 3,
      "n_train": 400,
      "n_test": 400
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_run.py",
  "files": [
    "bench_run.py",
    "bench_report.json"
  ],
  "limitations": "Only the built-in dynamics track was tested. The benchmark used 400 training and 400 test examples and 15 epochs rather than the full default dataset size; wall-clock communication savings, larger distributed systems, longer horizons, and a quantitatively confirmed Lyapunov boundary at neural-network scale were not tested.",
  "system_verdict": "worked",
  "practical_verdict": "helps",
  "mechanism_ok": 0,
  "system_judged": true
}