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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 10,
  "verdict": "The official tabular/Friedman benchmark used the registered mlp_tiny architecture, a tuned baseline sweep over the shared learning-rate and temperature grid, and eight paired seeds for the certificate-controlled Langevin variant. The resulting paired test-MSE delta was 0.0 with permutation p=1.0; the certificate bound remained 1.0 and observed unsafe frequency was 1.0, so no transient-risk control was activated. This is no measurable effect, not a win.",
  "metrics": {
    "baseline": "Official bench track tabular, model mlp_tiny; best config lr=0.003, temperature=0.0005; full-seed mean test MSE equals the idea mean (exact paired delta 0.0).",
    "idea": "Official certificate-controlled Langevin loop at lr=0.003, temperature=0.0005; 8 paired seeds; mean test MSE identical to baseline; mean observed unsafe frequency 1.0; mean final predicted bound 1.0; confirmed signature=true only in the weak sense that observed risk did not exceed the saturated bound."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 official_bench.py",
  "files": [
    "official_bench.py",
    "bench_report.json"
  ],
  "bench_report": {
    "bench_version": 1,
    "track": "tabular",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.003,
        "temperature": 0.0005
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "temperature": 0.0005
          },
          "mean": 0.9271184056997299
        },
        {
          "cfg": {
            "lr": 0.001,
            "temperature": 0.002
          },
          "mean": 1.003583923280239
        },
        {
          "cfg": {
            "lr": 0.003,
            "temperature": 0.0005
          },
          "mean": 0.838671835899353
        },
        {
          "cfg": {
            "lr": 0.003,
            "temperature": 0.002
          },
          "mean": 0.9744909703731537
        },
        {
          "cfg": {
            "lr": 0.006,
            "temperature": 0.0005
          },
          "mean": 0.8743404820561409
        },
        {
          "cfg": {
            "lr": 0.006,
            "temperature": 0.002
          },
          "mean": 0.9762508645653725
        }
      ],
      "full": {
        "mean": 0.838671835899353,
        "std": 0.11650225868258758,
        "per_seed": [
          0.8142433166503906,
          0.8665369153022766,
          0.7470943927764893,
          0.7199063301086426,
          0.8208378553390503,
          0.95234614610672,
          1.0413399934768677,
          0.7470707309246063
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.838671835899353,
      "std": 0.11650225868258758,
      "per_seed": [
        0.8142433166503906,
        0.8665369153022766,
        0.7470943927764893,
        0.7199063301086426,
        0.8208378553390503,
        0.95234614610672,
        1.0413399934768677,
        0.7470707309246063
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": 0.0,
      "idea_wins": 0,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0,
        0.0
      ],
      "p_value": 1.0,
      "mde": 0.0,
      "mde_rel_pct": 0.0,
      "verdict": "no measurable effect",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "observed unsafe frequency should not exceed certificate envelope",
      "predicted_final_bound": 1.0,
      "observed_mean_unsafe_frequency": 1.0,
      "observed_post_stop_unsafe_frequency": 1.0,
      "n_trained_models": 8,
      "confirmed": true
    },
    "idea_sweep": [
      [
        0.838671835899353,
        {
          "lr": 0.001,
          "temperature": 0.0005
        }
      ],
      [
        0.838671835899353,
        {
          "lr": 0.001,
          "temperature": 0.002
        }
      ],
      [
        0.838671835899353,
        {
          "lr": 0.003,
          "temperature": 0.0005
        }
      ]
    ],
    "protocol_note": "Official bench tabular track; custom Langevin loop is the intervention."
  },
  "limitations": "The certificate used a heuristic parameter-radius unsafe predicate and local Gaussian proxy, not a rigorously estimated stationary distribution, Hessian spectral gap, or chi-square divergence. The bound saturated at 1.0 throughout this neural-network run, causing the intervention to be inactive; no vision, sequence, or dynamics track was needed because optimizer/noise scheduling structurally matches tabular. The idea-sweep summary is compact and the official report is fully stored in bench_report.json.",
  "system_verdict": "partial",
  "practical_verdict": "no_effect",
  "mechanism_ok": 1,
  "system_judged": true
}