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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented the Topological Reachable-Set Coverage Scheduler on the structurally matched dynamics track with the standard rnn_small model and canonical train_model loop. Adaptive sampling reduced empirical reachable-state coverage radius by 54.70% at equal simulator budget, confirming the mechanism signature, and the Lipschitz sanity check held. However, the 8-seed test-MSE improvement was not statistically significant (paired delta=-0.00030233, permutation p=0.51515), so the idea did not demonstrate a significant benchmark win.",
  "metrics": {
    "baseline": "Best lr=0.006; 8-seed test MSE mean=0.00224437, std=0.00068261.",
    "idea": "Adaptive scheduler, lr=0.006; 8-seed test MSE mean=0.00194204, std=0.00059465; paired delta=-0.00030233, p=0.51515; coverage radius 0.34452 versus 0.76052 baseline, 54.70% lower."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "dynamics",
    "model": "rnn_small",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.006
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001
          },
          "mean": 0.00430093941395171
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 0.0033809992601163685
        },
        {
          "cfg": {
            "lr": 0.006
          },
          "mean": 0.002017002771026455
        }
      ],
      "full": {
        "mean": 0.0022443737179855816,
        "std": 0.0006826101486098914,
        "per_seed": [
          0.0008340506465174258,
          0.0019196165958419442,
          0.002481805393472314,
          0.0028325384482741356,
          0.0020983400754630566,
          0.0018523626495152712,
          0.003032150212675333,
          0.0029041257221251726
        ],
        "n": 8
      }
    },
    "idea": {
      "selected_cfg": {
        "lr": 0.006
      },
      "grid_results": [
        {
          "lr": 0.001,
          "mean": 0.012647395371459424
        },
        {
          "lr": 0.003,
          "mean": 0.003680584253743291
        },
        {
          "lr": 0.006,
          "mean": 0.0018993369594682008
        }
      ],
      "mean": 0.00194204316358082,
      "std": 0.0005946512584653817,
      "per_seed": [
        0.0025444745551794767,
        0.0021839358378201723,
        0.0014850844163447618,
        0.0013838530285283923,
        0.002973762573674321,
        0.002029680646955967,
        0.0018950217636302114,
        0.001040532486513257
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -0.00030233055440476164,
      "idea_wins": 4,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.001710423908662051,
        0.0002643192419782281,
        -0.000996720977127552,
        -0.0014486854197457433,
        0.0008754224982112646,
        0.00017731799744069576,
        -0.0011371284490451217,
        -0.0018635932356119156
      ],
      "p_value": 0.51515,
      "mde": 0.0010443640298036328,
      "mde_rel_pct": 46.532536958283075,
      "verdict": "no significant win",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "Adaptive reachable-state selection should reduce empirical coverage radius at equal simulator budget.",
      "observed": {
        "baseline_delta": 0.7605222459149111,
        "idea_delta": 0.3445205023070951,
        "relative_reduction_pct": 54.699483919417034,
        "baseline_pool_mse": 1.55548095703125,
        "idea_pool_mse": 1.5370749235153198,
        "baseline_min_sample_residual": -0.5907788276672363,
        "idea_min_sample_residual": -0.6713848114013672
      },
      "confirmed": true
    },
    "math_check": {
      "checked": true,
      "L": 0.7,
      "delta": 0.767539284296568,
      "sample_min": 0.36195731104790524,
      "cloud_min": 0.3268021435301365,
      "bound": -0.17532018795969234
    },
    "budget": {
      "epochs": 12,
      "train_samples": 400,
      "pool_samples": 1600
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "stage2_bench.py",
    "bench_report.json"
  ],
  "limitations": "Only the built-in dynamics task was tested; the scheduler used an empirical state-space pool and simple residual proxy, without learned reachable-set confidence bounds, physical safety certification, larger models, longer horizons, or more than 8 paired seeds.",
  "system_verdict": "partial",
  "practical_verdict": "inconclusive",
  "mechanism_ok": 1,
  "system_judged": true
}