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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented a custom rough-energy regression track and paired 8-seed Stage-2 benchmark using the same mlp_tiny architecture, with Adam as baseline and the dilation-quotient update as intervention. The mechanism signature was confirmed at NN scale, but the idea was significantly worse: test MSE 0.7774 versus 0.1198, paired delta +0.6576, permutation p=0.0081. The stability-inspired quotient update therefore did not transfer into a beneficial neural training method.",
  "metrics": {
    "baseline": "Adam, best lr=0.009 and weight_decay=0.0001; 8-seed test MSE mean 0.11978, std 0.01038.",
    "idea": "Dilation-quotient update, best shared-grid lr=0.001 and weight_decay=0; 8-seed test MSE mean 0.77743, std 0.13186; paired delta +0.65765, permutation p=0.0081.",
    "mechanism_signature": {
      "predicted_bound": 10.0,
      "observed_idea_output_roughness": 0.0196735095,
      "confirmed": true
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "rough_track.py",
    "stage2_bench.py",
    "bench_report.json"
  ],
  "limitations": "The built-in tracks did not contain posterior sampling or rough-energy dynamics, so this used a custom supervised transfer track. It tests the quotient field as a parameter-space training update rather than the original Metropolized sampler; posterior acceptance, ESS, and long-chain diagnostics were not measured. The bench build required local NumPy-to-tensor conversion before calling train_model.",
  "bench_report": {
    "bench_version": 1,
    "track": "custom_tracks/rough_energy_regression",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.009,
        "weight_decay": 0.0001
      },
      "full": {
        "mean": 0.11977773066610098,
        "std": 0.010381994134392314,
        "per_seed": [
          0.11285003274679184,
          0.12270589917898178,
          0.1380033791065216,
          0.11392627656459808,
          0.12507213652133942,
          0.10099826753139496,
          0.12710516154766083,
          0.11756069213151932
        ],
        "n": 8
      }
    },
    "idea": {
      "best_cfg": {
        "lr": 0.001,
        "weight_decay": 0.0
      },
      "full": {
        "mean": 0.777425967156887,
        "std": 0.1318602192538638,
        "per_seed": [
          0.9373617172241211,
          0.9086369276046753,
          0.8551380634307861,
          0.555958092212677,
          0.8983770608901978,
          0.70047527551651,
          0.715994656085968,
          0.6474659442901611
        ],
        "n": 8
      }
    },
    "comparison": {
      "delta_mean": 0.6576482364907861,
      "idea_wins": 0,
      "n_pairs": 8,
      "p_value": 0.0081,
      "verdict": "idea worse (significant)",
      "system_worked": false
    },
    "mechanism_signature": {
      "custom_track": {
        "name": "rough_energy_regression",
        "file": "rough_track.py",
        "domain": "optimization"
      },
      "quantity": "prediction-vs-observed NN output roughness",
      "predicted_bound": 10.0,
      "observed_idea": 0.019673509523272514,
      "observed_baseline_target_roughness": 0.7095075249671936,
      "confirmed": true
    }
  },
  "system_verdict": "partial",
  "practical_verdict": "harms",
  "mechanism_ok": 1,
  "system_judged": true
}