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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented reversal-defect adaptive checkpointing around the shared rnn_small GRU on the structurally matched dynamics track, with a three-value learning-rate sweep and eight paired seeds. The idea achieved lower mean test MSE (0.001765 vs 0.002776; paired delta -0.001011), but the permutation p-value was 0.1697, so there was no significant win. Trained-model probes measured large reversal defects and triggered denser checkpointing, but the quantitative mechanism signature was not confirmed.",
  "metrics": {
    "baseline": "Best lr=0.01; mean test MSE 0.0027758953, std 0.0017084856; sweep means lr=0.001: 0.02141551, lr=0.003: 0.00377186, lr=0.01: 0.00342993.",
    "idea": "Best lr=0.01; mean test MSE 0.0017648405, std 0.0009922700; paired delta -0.0010110548, 6/8 seed wins, permutation p=0.1697.",
    "mechanism_signature": "Mean reversal defect 0.468672, maximum up to 0.853700, mean checkpoints 81, final interval 1; defect threshold did trigger dense checkpointing, but confirmed=false."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "dynamics",
    "model": "rnn_small",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.01
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001
          },
          "mean": 0.02141550718806684
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 0.0037718631356256083
        },
        {
          "cfg": {
            "lr": 0.01
          },
          "mean": 0.0034299326362088323
        }
      ],
      "full": {
        "mean": 0.002775895278318785,
        "std": 0.0017084856346187775,
        "per_seed": [
          0.0014615225372835994,
          0.004334237892180681,
          0.0012712624156847596,
          0.006652707699686289,
          0.0020110509358346462,
          0.0024756770581007004,
          0.0018914247630164027,
          0.0021092789247632027
        ],
        "n": 8
      }
    },
    "idea": {
      "best_cfg": {
        "lr": 0.01
      },
      "mean": 0.0017648405191721395,
      "std": 0.0009922699493986984,
      "per_seed": [
        0.0030640370678156614,
        0.003339420771226287,
        0.0008833673782646656,
        0.0013923275982961059,
        0.0008476653601974249,
        0.002570778364315629,
        0.001297973794862628,
        0.0007231538183987141
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -0.0010110547591466457,
      "idea_wins": 6,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.001602514530532062,
        -0.0009948171209543943,
        -0.000387895037420094,
        -0.005260380101390183,
        -0.0011633855756372213,
        9.510130614928632e-05,
        -0.0005934509681537747,
        -0.0013861251063644886
      ],
      "p_value": 0.1697,
      "verdict": "no significant win",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "larger trained-model reversal defects trigger denser checkpoint policy",
      "observed_mean_defect": 0.4686719482764602,
      "observed_mean_checkpoints": 81.0,
      "low_defect_checkpoint_relation": "adaptive policy increases checkpoints when measured defect exceeds 0.08",
      "confirmed": false
    },
    "idea_sweep": {
      "0.001": {
        "mean": 0.017555608399561606
      },
      "0.003": {
        "mean": 0.0037500788166653365
      },
      "0.01": {
        "mean": 0.0017648405191721395
      }
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "stage2_bench.py",
    "bench_report.json"
  ],
  "limitations": "The built-in dynamics benchmark is a small 400/200-sample pendulum rollout task, not Sequential-MNIST or a reversible low-rank ODE. The rnn_small GRU is not mathematically reversible; the neural-scale probe therefore tests proxy behavior rather than exact integrator reversibility. The adaptive loop used CPU for reliable probing, and rank enrichment was not implemented because the shared benchmark GRU has fixed hidden size; peak VRAM and dense-reference gradient cosine were not measured.",
  "system_verdict": "failed",
  "practical_verdict": "inconclusive",
  "mechanism_ok": 0,
  "system_judged": true
}