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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "The matched switched SSM benchmark produced a small lower MSE for commutator regularization, but the paired permutation test was not significant (delta_mean=-2.7721675905922893e-06, p=0.6188). The trained-model mechanism signature was confirmed for reduced commutator magnitude (ratio 0.8689), while cycle gain was unchanged, so the idea does not qualify as a benchmark win.",
  "metrics": {
    "baseline": "Best lr=0.009, 20 epochs; 8-seed mean MSE 3.271682101058104e-05.",
    "idea": "Best lr=0.009, comm_lambda=0.05, 20 epochs; 8-seed mean MSE 2.9944653419988754e-05; 6/8 paired wins; p=0.6188."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "dynamics",
    "model": "rnn_small",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.009,
        "epochs": 20
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.003,
            "epochs": 20
          },
          "mean": 5.509081029231311e-05
        },
        {
          "cfg": {
            "lr": 0.006,
            "epochs": 20
          },
          "mean": 4.4340567001199815e-05
        },
        {
          "cfg": {
            "lr": 0.009,
            "epochs": 20
          },
          "mean": 4.234683206050249e-05
        }
      ],
      "full": {
        "per_seed": [
          5.1793333113892004e-05,
          5.785241592093371e-05,
          4.4684398744720966e-05,
          1.5057180462463293e-05,
          1.809621971915476e-05,
          2.5893627025652677e-05,
          2.4117996872519143e-05,
          2.4239396225311793e-05
        ],
        "mean": 3.271682101058104e-05
      }
    },
    "idea": {
      "config": {
        "lr": 0.009,
        "epochs": 20,
        "comm_lambda": 0.05
      },
      "per_seed": [
        1.915467146318406e-05,
        5.784632594441064e-05,
        4.4664833694696426e-05,
        1.5033597264846321e-05,
        1.8127479052054696e-05,
        3.639693386503495e-05,
        2.4111286620609462e-05,
        2.4222099455073476e-05
      ],
      "mean": 2.9944653419988754e-05
    },
    "comparison": {
      "delta_mean": -2.7721675905922893e-06,
      "idea_wins": 6,
      "n_pairs": 8,
      "per_seed_diffs": [
        -3.2638661650707945e-05,
        -6.0899765230715275e-09,
        -1.9565050024539232e-08,
        -2.358319761697203e-08,
        3.1259332899935544e-08,
        1.0503306839382276e-05,
        -6.7102519096806645e-09,
        -1.729677023831755e-08
      ],
      "p_value": 0.6188,
      "mde": 1.0548823579876116e-05,
      "mde_rel_pct": 32.242813494821185,
      "verdict": "no significant win",
      "system_worked": false
    },
    "track_justification": "Dynamics directly tests switched latent stability/control and Lyapunov contraction.",
    "mechanism_signature": {
      "prediction": "commutator regularization lowers trained ordered-cycle noncommutativity and cycle gain",
      "baseline_observed": {
        "observed_commutator_mean": 0.0003485826964606531,
        "observed_cycle_gain_mean": 0.9300649240612984,
        "n_models": 8
      },
      "idea_observed": {
        "observed_commutator_mean": 0.0003028972278116271,
        "observed_cycle_gain_mean": 0.9300689920783043,
        "n_models": 8
      },
      "commutator_ratio": 0.8689393675793576,
      "cycle_gain_delta": 4.06801700592041e-06,
      "confirmed": true
    },
    "idea_sweep": [
      {
        "config": {
          "lr": 0.009,
          "epochs": 20,
          "comm_lambda": 0.0
        },
        "mean": 2.995253646531637e-05,
        "per_seed": [
          1.91609506146051e-05,
          5.785241592093371e-05,
          4.4684398744720966e-05,
          1.5057180462463293e-05,
          1.813357084756717e-05,
          3.637438203440979e-05,
          2.4117996872519143e-05,
          2.4239396225311793e-05
        ]
      },
      {
        "config": {
          "lr": 0.009,
          "epochs": 20,
          "comm_lambda": 0.01
        },
        "mean": 2.9951231795166677e-05,
        "per_seed": [
          1.9159488147124648e-05,
          5.785137182101607e-05,
          4.4682547013508156e-05,
          1.5051909031171817e-05,
          1.8132404875359498e-05,
          3.637882036855444e-05,
          2.4117578504956327e-05,
          2.4235734599642456e-05
        ]
      },
      {
        "config": {
          "lr": 0.009,
          "epochs": 20,
          "comm_lambda": 0.05
        },
        "mean": 2.9944653419988754e-05,
        "per_seed": [
          1.915467146318406e-05,
          5.784632594441064e-05,
          4.4664833694696426e-05,
          1.5033597264846321e-05,
          1.8127479052054696e-05,
          3.639693386503495e-05,
          2.4111286620609462e-05,
          2.4222099455073476e-05
        ]
      }
    ]
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "stage2_bench.py",
    "bench_report.json"
  ],
  "limitations": "Only the built-in dynamics track was tested; no vision, sequence, longer training, duty/order sweep, learned full quadratic Lyapunov P, latency/FLOP measurement, or larger-width evaluation was performed. The baseline was a matched unregularized switched SSM rather than the stock GRU because the intervention changes the recurrent core.",
  "system_verdict": "partial",
  "practical_verdict": "inconclusive",
  "mechanism_ok": 1,
  "system_judged": true
}