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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Built a capitalization-efficiency monitor using epoch-level posterior-displacement KL as the information ledger, held-out validation improvement as value gain, and rollback/learning-rate reduction after low efficiency. On the matched tabular Friedman regression benchmark, the idea had MSE 6.84791 versus baseline 6.72020, paired delta +0.12771, permutation p=0.1856, and therefore no significant win. The behavior signature was supported: mean acquired information was 35.8822 and mean late efficiency was -0.5216, but this did not improve the independent task metric.",
  "metrics": {
    "baseline": "Adam, mlp_tiny, best lr=0.006, 8-seed mean MSE 6.7201979756, std 0.3792385032",
    "idea": "Capitalization monitor, mlp_tiny, best lr=0.006 and threshold=0.02, 8-seed mean MSE 6.8479101062, std 0.3795102409, paired delta +0.1277121305, permutation p=0.1856"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_run.py",
  "files": [
    "bench_run.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": 0.006
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.0015
          },
          "mean": 8.577727317810059
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 6.8584965467453
        },
        {
          "cfg": {
            "lr": 0.006
          },
          "mean": 6.626367449760437
        }
      ],
      "full": {
        "mean": 6.720197975635529,
        "std": 0.3792385031605589,
        "per_seed": [
          6.333833694458008,
          7.1719770431518555,
          6.258625507354736,
          6.741033554077148,
          6.219150543212891,
          6.970664978027344,
          6.840729713439941,
          7.225568771362305
        ],
        "n": 8
      }
    },
    "idea": {
      "best_cfg": {
        "lr": 0.006,
        "threshold": 0.02
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.0015,
            "threshold": 0.02
          },
          "mean": 8.962467908859253
        },
        {
          "cfg": {
            "lr": 0.003,
            "threshold": 0.02
          },
          "mean": 7.022419393062592
        },
        {
          "cfg": {
            "lr": 0.006,
            "threshold": 0.02
          },
          "mean": 6.847910106182098
        }
      ],
      "per_seed": [
        6.578519821166992,
        7.120673179626465,
        6.699369430541992,
        6.741033554077148,
        6.219150543212891,
        6.990142345428467,
        6.840729713439941,
        7.593662261962891
      ],
      "mean": 6.847910106182098,
      "std": 0.3795102408701731,
      "n": 8
    },
    "comparison": {
      "delta_mean": 0.12771213054656982,
      "idea_wins": 1,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.24468612670898438,
        -0.051303863525390625,
        0.44074392318725586,
        0.0,
        0.0,
        0.019477367401123047,
        0.0,
        0.36809349060058594
      ],
      "p_value": 0.1856,
      "mde": 0.16181177964695484,
      "mde_rel_pct": 2.407842450975595,
      "verdict": "no significant win",
      "system_worked": false
    },
    "mechanism_signature": {
      "quantity": "trained-model epoch displacement KL and held-out validation value/ledger",
      "prediction": "late low-value updates acquire positive KL and have eta near zero or negative",
      "observed_mean_info": 35.882227032415834,
      "observed_mean_late_eta": -0.5215707736578389,
      "observed_rollbacks": 1.25,
      "confirmed": true
    },
    "protocol_notes": {
      "n_train": 2000,
      "n_test": 500,
      "epochs": 15,
      "common_validation_fraction": 0.2,
      "track_match": "tabular is the built-in optimizer/scheduler track"
    }
  },
  "limitations": "Only the structurally matched built-in tabular track was tested; optimizer-noise dissipation Sigma was omitted, the posterior variance was fixed, and the value estimate used a held-out validation buffer rather than paired deletion-counterfactual evaluations. The monitor threshold was fixed rather than independently swept.",
  "system_verdict": "partial",
  "practical_verdict": "inconclusive",
  "mechanism_ok": 1,
  "system_judged": true
}