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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented a recurrent message-passing dynamics system with synchronous full-sweep baseline and histogram-selected synchronous cluster updates, using the same data, optimizer, architecture components, and shared hyperparameter union. Across 8 paired seeds, the idea had higher test MSE (0.006128 vs 0.003836), with delta_mean=+0.002293 and permutation p=0.0081, constituting a significant loss. The trained-model mechanism signature confirmed the predicted approximately eta² scaling of synchronous-versus-sequential update discrepancy, but this did not translate into better task performance.",
  "metrics": {
    "baseline": "Dynamics test MSE mean 0.00383558, std 0.00081823; best lr=0.01, epochs=12; per-seed [0.00405838, 0.00342526, 0.00307760, 0.00440629, 0.00449852, 0.00260400, 0.00526552, 0.00334905].",
    "idea": "Histogram-controlled cluster updates test MSE mean 0.00612822, std 0.00194303, best lr=0.01, epochs=12; per-seed [0.00588618, 0.00363651, 0.00578116, 0.01028094, 0.00752147, 0.00445210, 0.00660217, 0.00486525]. Paired delta +0.00229264, 0/8 wins, permutation p=0.0081; mechanism signature confirmed=true with eta² ratio range [0.5408, 0.5880]."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "stage2_bench.py",
    "bench_report.json"
  ],
  "bench_report": {
    "bench_version": 1,
    "track": "dynamics",
    "model": "rnn_small",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.01,
        "epochs": 12
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "epochs": 12
          },
          "mean": 0.023774019442498684
        },
        {
          "cfg": {
            "lr": 0.003,
            "epochs": 12
          },
          "mean": 0.01013653608970344
        },
        {
          "cfg": {
            "lr": 0.01,
            "epochs": 12
          },
          "mean": 0.003741882392205298
        }
      ],
      "full": {
        "mean": 0.003835578914731741,
        "std": 0.0008182271774829689,
        "per_seed": [
          0.004058382008224726,
          0.003425257047638297,
          0.00307759759016335,
          0.004406292922794819,
          0.004498522728681564,
          0.0026040049269795418,
          0.005265520885586739,
          0.003349053207784891
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.006128221459221095,
      "std": 0.0019430310597602655,
      "per_seed": [
        0.005886178929358721,
        0.003636509645730257,
        0.005781157873570919,
        0.010280938819050789,
        0.007521466817706823,
        0.0044521004892885685,
        0.006602167151868343,
        0.004865251947194338
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": 0.002292642544489354,
      "idea_wins": 0,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.001827796921133995,
        0.00021125259809195995,
        0.002703560283407569,
        0.00587464589625597,
        0.003022944089025259,
        0.0018480955623090267,
        0.0013366462662816048,
        0.0015161987394094467
      ],
      "p_value": 0.0081,
      "mde": 0.0014061178480241382,
      "mde_rel_pct": 36.65985967916089,
      "verdict": "idea worse (significant)",
      "system_worked": false
    },
    "mechanism_signature": {
      "track_structure": "controlled pendulum multi-step dynamics; recurrent state updates",
      "scheduler": "largest high-bin hidden-state disagreement histogram",
      "clusters": 4,
      "histogram_bins": 4,
      "signature": {
        "prediction": "trained-model synchronous-vs-sequential discrepancy scales approximately eta^2",
        "rows": [
          {
            "eta": 0.2,
            "observed_linf": 0.023520752787590027,
            "eta2": 0.04000000000000001
          },
          {
            "eta": 0.4,
            "observed_linf": 0.09223496913909912,
            "eta2": 0.16000000000000003
          },
          {
            "eta": 0.6,
            "observed_linf": 0.20175543427467346,
            "eta2": 0.36
          },
          {
            "eta": 0.8,
            "observed_linf": 0.3461129665374756,
            "eta2": 0.6400000000000001
          }
        ],
        "eta2_ratio_range": [
          0.5408015102148055,
          0.5880188196897506
        ],
        "confirmed": true
      }
    },
    "protocol_notes": {
      "paired_seeds": [
        0,
        1,
        2,
        3,
        4,
        5,
        6,
        7
      ],
      "dataset_sizes": [
        400,
        100
      ],
      "idea_grid": [
        {
          "cfg": {
            "lr": 0.001,
            "epochs": 12
          },
          "mean": 0.2794509418308735
        },
        {
          "cfg": {
            "lr": 0.003,
            "epochs": 12
          },
          "mean": 0.012458630255423486
        },
        {
          "cfg": {
            "lr": 0.01,
            "epochs": 12
          },
          "mean": 0.006128221459221095
        }
      ],
      "selected_idea_cfg": {
        "lr": 0.01,
        "epochs": 12
      },
      "baseline_grid_union": [
        {
          "lr": 0.001,
          "epochs": 12
        },
        {
          "lr": 0.003,
          "epochs": 12
        },
        {
          "lr": 0.01,
          "epochs": 12
        }
      ]
    }
  },
  "limitations": "The scheduler was a deterministic greedy largest-high-bin histogram policy rather than a learned Q-learning controller, so learned-policy training was not tested. Wall-clock speedup, GPU utilization, larger graphs, alternative cluster partitions, and more than three recurrent sweeps were not evaluated. The local intervention uses a compact ring message-passing model compatible with the dynamics structure rather than modifying the bench rnn_small GRU internals directly.",
  "system_verdict": "failed",
  "practical_verdict": "harms",
  "mechanism_ok": 0,
  "system_judged": true
}