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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Built and evaluated a bifurcation-aware adaptive recurrent controller on the structurally matched dynamics track. The trained models showed the intended allocation signal, but the task MSE improvement was negligible and non-significant (permutation p=0.9452), so the benchmark does not establish a win.",
  "metrics": {
    "baseline": "8-seed test MSE mean 0.0089720566, std 0.0018727238; tuned baseline lr=0.003, inner_steps=1, mu0=0.03.",
    "idea": "8-seed test MSE mean 0.0088927213, std 0.0018818210; paired delta=-0.0000793354, 4/8 wins, permutation p=0.9452; observed mean updates 4.0 versus predicted 4.0."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_experiment.py",
  "files": [
    "bench_experiment.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": 0.003,
        "inner_steps": 1,
        "mu0": 0.03
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.0015,
            "inner_steps": 1,
            "mu0": 0.02
          },
          "mean": 0.01301798946224153
        },
        {
          "cfg": {
            "lr": 0.003,
            "inner_steps": 1,
            "mu0": 0.03
          },
          "mean": 0.009061129298061132
        },
        {
          "cfg": {
            "lr": 0.006,
            "inner_steps": 1,
            "mu0": 0.05
          },
          "mean": 0.010191938490606844
        }
      ],
      "full": {
        "mean": 0.008972056617494673,
        "std": 0.0018727237600738788,
        "per_seed": [
          0.008878752589225769,
          0.011135471053421497,
          0.0069000981748104095,
          0.009330195374786854,
          0.006975930649787188,
          0.011603735387325287,
          0.006533934734761715,
          0.010418334975838661
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.008892721263691783,
      "std": 0.0018818210498085222,
      "per_seed": [
        0.0073481034487485886,
        0.007073543034493923,
        0.012936844490468502,
        0.009487112984061241,
        0.008278523571789265,
        0.006781186908483505,
        0.009480423294007778,
        0.00975603237748146
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -7.933535380288959e-05,
      "idea_wins": 4,
      "n_pairs": 8,
      "per_seed_diffs": [
        -0.0015306491404771805,
        -0.004061928018927574,
        0.0060367463156580925,
        0.00015691760927438736,
        0.001302592922002077,
        -0.004822548478841782,
        0.0029464885592460632,
        -0.0006623025983572006
      ],
      "p_value": 0.9452,
      "mde": 0.0029870521811018033,
      "mde_rel_pct": 33.29283695420881,
      "verdict": "no measurable effect",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "allocation increases as positive mu_hat approaches zero",
      "predicted_mean_updates_from_measured_mu": 4.0,
      "observed_mean_updates_on_trained_models": 4.0,
      "per_seed": [
        {
          "seed": 0,
          "baseline_updates": 1.0,
          "idea_updates": 4.0,
          "idea_mu_hat": -0.003297710412880406
        },
        {
          "seed": 1,
          "baseline_updates": 1.0,
          "idea_updates": 4.0,
          "idea_mu_hat": -0.004545253599644639
        },
        {
          "seed": 2,
          "baseline_updates": 1.0,
          "idea_updates": 4.0,
          "idea_mu_hat": -0.0017975553273572586
        },
        {
          "seed": 3,
          "baseline_updates": 1.0,
          "idea_updates": 4.0,
          "idea_mu_hat": -0.004322887591115432
        },
        {
          "seed": 4,
          "baseline_updates": 1.0,
          "idea_updates": 4.0,
          "idea_mu_hat": -0.00040878431536839344
        },
        {
          "seed": 5,
          "baseline_updates": 1.0,
          "idea_updates": 4.0,
          "idea_mu_hat": 0.0011617409294899517
        },
        {
          "seed": 6,
          "baseline_updates": 1.0,
          "idea_updates": 4.0,
          "idea_mu_hat": 0.00031165493237494957
        },
        {
          "seed": 7,
          "baseline_updates": 1.0,
          "idea_updates": 4.0,
          "idea_mu_hat": -0.002752484622760676
        }
      ],
      "confirmed": true
    },
    "idea_sweep": [
      {
        "cfg": {
          "lr": 0.0015,
          "inner_steps": 1,
          "mu0": 0.02
        },
        "mean": 0.01594785531051457
      },
      {
        "cfg": {
          "lr": 0.003,
          "inner_steps": 1,
          "mu0": 0.03
        },
        "mean": 0.0116096215788275
      },
      {
        "cfg": {
          "lr": 0.006,
          "inner_steps": 1,
          "mu0": 0.05
        },
        "mean": 0.009211400989443064
      }
    ],
    "protocol": {
      "paired_seeds": [
        0,
        1,
        2,
        3,
        4,
        5,
        6,
        7
      ],
      "sweep_seeds": [
        0,
        1,
        2,
        3
      ],
      "epochs": 8,
      "batch": 128,
      "structural_match": "dynamics/control",
      "same_architecture": true
    }
  },
  "limitations": "The controller selected four updates for essentially all measured states, so adaptive allocation diversity and wall-clock/FLOP savings were not established. Only the built-in dynamics track was tested; no custom neural-ODE or longer-horizon recurrent benchmark was added.",
  "system_verdict": "partial",
  "practical_verdict": "no_effect",
  "mechanism_ok": 1,
  "system_judged": true
}