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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Built and evaluated a pullback-attractor regularizer on the structurally matched registered dynamics/rnn_small benchmark. The idea had lower mean MSE than baseline, but the paired permutation test was not significant (p=0.59995), and the trained-model contraction slope did not quantitatively match the prediction, so the idea did not establish a reliable win.",
  "metrics": {
    "baseline": "Mean MSE 0.0020994337 (8 seeds), tuned lr=0.006 and weight_decay=0.0001.",
    "idea": "Mean MSE 0.0019659589 (8 seeds), paired delta -0.0001334748, 6/8 wins, permutation p=0.59995."
  },
  "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": 0.0001,
        "epochs": 12,
        "attr_weight": 0.0,
        "target_lambda": -0.08
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.0015,
            "weight_decay": 0.0,
            "epochs": 12,
            "attr_weight": 0.0,
            "target_lambda": -0.08
          },
          "mean": 0.004987949621863663
        },
        {
          "cfg": {
            "lr": 0.0015,
            "weight_decay": 0.0001,
            "epochs": 12,
            "attr_weight": 0.0,
            "target_lambda": -0.08
          },
          "mean": 0.00498378137126565
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.0,
            "epochs": 12,
            "attr_weight": 0.0,
            "target_lambda": -0.08
          },
          "mean": 0.00358188062091358
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.0001,
            "epochs": 12,
            "attr_weight": 0.0,
            "target_lambda": -0.08
          },
          "mean": 0.0035488905268721282
        },
        {
          "cfg": {
            "lr": 0.006,
            "weight_decay": 0.0,
            "epochs": 12,
            "attr_weight": 0.0,
            "target_lambda": -0.08
          },
          "mean": 0.0019017510931007564
        },
        {
          "cfg": {
            "lr": 0.006,
            "weight_decay": 0.0001,
            "epochs": 12,
            "attr_weight": 0.0,
            "target_lambda": -0.08
          },
          "mean": 0.001846620871219784
        }
      ],
      "full": {
        "mean": 0.0020994337101001292,
        "std": 0.0003699326898898649,
        "per_seed": [
          0.0021909112110733986,
          0.0014349172124639153,
          0.0016000064788386226,
          0.0021606485825031996,
          0.0026131158228963614,
          0.002182674128562212,
          0.002174227498471737,
          0.0024389687459915876
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.0019659589452203363,
      "std": 0.0007528780037898819,
      "per_seed": [
        0.002175788627937436,
        0.0017074780771508813,
        0.0010215449146926403,
        0.001942452392540872,
        0.002001833636313677,
        0.0019844763446599245,
        0.0012087502982467413,
        0.003685347270220518
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -0.00013347476487979293,
      "idea_wins": 6,
      "n_pairs": 8,
      "per_seed_diffs": [
        -1.5122583135962486e-05,
        0.00027256086468696594,
        -0.0005784615641459823,
        -0.00021819618996232748,
        -0.0006112821865826845,
        -0.00019819778390228748,
        -0.0009654772002249956,
        0.0012463785242289305
      ],
      "p_value": 0.59995,
      "mde": 0.0005664705121806638,
      "mde_rel_pct": 26.982062327352402,
      "verdict": "no significant win",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "trained hidden pullback diameter contracts exponentially under shared replay",
      "config": {
        "lr": 0.006,
        "weight_decay": 0.0001,
        "epochs": 12,
        "attr_weight": 0.001,
        "target_lambda": -0.08
      },
      "predicted_log_diameter_step": -0.08,
      "observed_log_diameter_slope": -0.4773993907385317,
      "final_diameter": 0.034216802567243576,
      "confirmed": false
    },
    "idea_sweep": [
      {
        "cfg": {
          "lr": 0.006,
          "weight_decay": 0.0001,
          "epochs": 12,
          "attr_weight": 0.001,
          "target_lambda": -0.08
        },
        "result": {
          "mean": 0.0019659589452203363,
          "std": 0.0007528780037898819,
          "per_seed": [
            0.002175788627937436,
            0.0017074780771508813,
            0.0010215449146926403,
            0.001942452392540872,
            0.002001833636313677,
            0.0019844763446599245,
            0.0012087502982467413,
            0.003685347270220518
          ],
          "n": 8
        }
      },
      {
        "cfg": {
          "lr": 0.006,
          "weight_decay": 0.0001,
          "epochs": 12,
          "attr_weight": 0.01,
          "target_lambda": -0.08
        },
        "result": {
          "mean": 0.0019659589452203363,
          "std": 0.0007528780037898819,
          "per_seed": [
            0.002175788627937436,
            0.0017074780771508813,
            0.0010215449146926403,
            0.001942452392540872,
            0.002001833636313677,
            0.0019844763446599245,
            0.0012087502982467413,
            0.003685347270220518
          ],
          "n": 8
        }
      },
      {
        "cfg": {
          "lr": 0.006,
          "weight_decay": 0.0001,
          "epochs": 12,
          "attr_weight": 0.03,
          "target_lambda": -0.04
        },
        "result": {
          "mean": 0.0019659589452203363,
          "std": 0.0007528780037898819,
          "per_seed": [
            0.002175788627937436,
            0.0017074780771508813,
            0.0010215449146926403,
            0.001942452392540872,
            0.002001833636313677,
            0.0019844763446599245,
            0.0012087502982467413,
            0.003685347270220518
          ],
          "n": 8
        }
      }
    ],
    "track_justification": "The registered dynamics track is structurally matched because the idea concerns stability and contraction of recurrent latent trajectories."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_pullback.py",
  "files": [
    "bench_pullback.py",
    "bench_report.json"
  ],
  "limitations": "Only the registered dynamics track and rnn_small model were tested. The regularizer sweep produced identical metrics, suggesting its effective gradient influence was inactive or ineffective; longer histories, alternative recurrent models, and a diagnostic-only inference study were not tested.",
  "system_verdict": "failed",
  "practical_verdict": "inconclusive",
  "mechanism_ok": 0,
  "system_judged": true
}