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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 10,
  "verdict": "Implemented Følner-gated recurrent message passing as a local modification of the registered dynamics/rnn_small benchmark system. Across 8 paired seeds, the idea had slightly lower MSE, but the paired permutation test was not significant; the required system verdict is therefore no significant win. The trained-model mechanism signature also failed to confirm the predicted expansive regime.",
  "metrics": {
    "baseline": "Registered track dynamics, model rnn_small; tuned baseline sweep over lr={0.001,0.003,0.01} and weight_decay={0,0.0001}; best lr=0.01, weight_decay=0.0001. Full 8-seed mean test MSE=0.0005577069, std=0.0001623548.",
    "idea": "Chosen gated configuration lr=0.01, weight_decay=0.0001, delta=0.5, beta=0.7, slope=8; full 8-seed mean test MSE=0.0005104744, std=0.0001625943. Paired delta=-0.0000472325, 6/8 idea wins, permutation p=0.5225.",
    "mechanism_signature": "Predicted observed expansion ratio >1.35 and mean gate <0.5; trained-model measurements were mean ratio=1.2843889354 and mean gate=0.8395185024, 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,
        "weight_decay": 0.0001
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0
          },
          "mean": 0.001921573159052059
        },
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0001
          },
          "mean": 0.0018964317569043487
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.0
          },
          "mean": 0.0011179699067724869
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.0001
          },
          "mean": 0.0010905505041591823
        },
        {
          "cfg": {
            "lr": 0.01,
            "weight_decay": 0.0
          },
          "mean": 0.0006299250526353717
        },
        {
          "cfg": {
            "lr": 0.01,
            "weight_decay": 0.0001
          },
          "mean": 0.0005868283187737688
        }
      ],
      "full": {
        "mean": 0.0005577069205173757,
        "std": 0.00016235484867295895,
        "per_seed": [
          0.00046681062667630613,
          0.0004913305747322738,
          0.0004835708241444081,
          0.0009056012495420873,
          0.0005242845509201288,
          0.0003316242655273527,
          0.0006971154361963272,
          0.0005613178364001215
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.0005104744304844644,
      "std": 0.00016259431597973005,
      "per_seed": [
        0.0005603914032690227,
        0.0004172833578195423,
        0.00043428907520137727,
        0.0008996041724458337,
        0.00048453747876919806,
        0.00045123344170860946,
        0.000313605327391997,
        0.0005228511872701347
      ],
      "n": 8,
      "chosen_cfg": {
        "lr": 0.01,
        "weight_decay": 0.0001,
        "delta": 0.5,
        "beta": 0.7,
        "slope": 8.0
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0001,
            "delta": 0.25,
            "beta": 0.7,
            "slope": 8.0
          },
          "mean": 0.001631094521144405
        },
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0001,
            "delta": 0.35,
            "beta": 0.7,
            "slope": 8.0
          },
          "mean": 0.0015781476977281272
        },
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0001,
            "delta": 0.5,
            "beta": 0.7,
            "slope": 8.0
          },
          "mean": 0.0017034276097547263
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.0001,
            "delta": 0.25,
            "beta": 0.7,
            "slope": 8.0
          },
          "mean": 0.0012407805188558996
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.0001,
            "delta": 0.35,
            "beta": 0.7,
            "slope": 8.0
          },
          "mean": 0.0012196671304991469
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.0001,
            "delta": 0.5,
            "beta": 0.7,
            "slope": 8.0
          },
          "mean": 0.0011900454992428422
        },
        {
          "cfg": {
            "lr": 0.01,
            "weight_decay": 0.0001,
            "delta": 0.25,
            "beta": 0.7,
            "slope": 8.0
          },
          "mean": 0.0006524444761453196
        },
        {
          "cfg": {
            "lr": 0.01,
            "weight_decay": 0.0001,
            "delta": 0.35,
            "beta": 0.7,
            "slope": 8.0
          },
          "mean": 0.0006070969975553453
        },
        {
          "cfg": {
            "lr": 0.01,
            "weight_decay": 0.0001,
            "delta": 0.5,
            "beta": 0.7,
            "slope": 8.0
          },
          "mean": 0.000577892002183944
        }
      ]
    },
    "comparison": {
      "delta_mean": -4.72324900329113e-05,
      "idea_wins": 6,
      "n_pairs": 8,
      "per_seed_diffs": [
        9.358077659271657e-05,
        -7.404721691273153e-05,
        -4.9281748943030834e-05,
        -5.9970770962536335e-06,
        -3.974707215093076e-05,
        0.00011960917618125677,
        -0.00038351010880433023,
        -3.846664912998676e-05
      ],
      "p_value": 0.5225,
      "mde": 0.00012771297744744513,
      "mde_rel_pct": 22.89965800118956,
      "verdict": "no significant win",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "expansive temporal receptive fields should yield r>1+delta and gate<0.5",
      "observed_mean_ratio": 1.284388935357143,
      "observed_mean_gate": 0.8395185023546219,
      "predicted_threshold": 1.35,
      "confirmed": false
    },
    "protocol_note": "Direct bench.make_report output for registered dynamics/rnn_small; baseline and idea use shared lr/weight-decay union, paired seeds 0-7, and train_model GPU-to-CPU fallback."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_folner_bench.py",
  "files": [
    "stage2_folner_bench.py",
    "bench_report.json",
    "stage2_rerun.log"
  ],
  "limitations": "The registered dynamics track is a controlled pendulum rollout, so the graph neighborhood monitor is adapted as a temporal receptive-field proxy. No literal graph dataset, sparse graph frontier memory/FLOP measurement, learned pooling hierarchy, or graph radius-3 node-classification task was tested.",
  "system_verdict": "failed",
  "practical_verdict": "inconclusive",
  "mechanism_ok": 0,
  "system_judged": true
}