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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 10,
  "verdict": "The bounded predictive-gain optimizer was implemented as a groupwise Euclidean mirror-descent modification of SGD and tested on the registered tabular Friedman#1 regression track with the fixed mlp_tiny architecture. The analytic update check passed to numerical precision, but the idea was significantly worse on test MSE: 23.9583 versus 7.6676 for baseline, paired delta +16.2907, permutation p=0.0081. The trained-model mechanism signature was not confirmed, so there is no observed transfer win.",
  "metrics": {
    "baseline": "mean test MSE=7.6675900817, std=1.7205125356, best_cfg={lr:0.006,wd:0.0}",
    "idea": "mean test MSE=23.9582793713, std=3.6945581467, selected_cfg={lr:0.003,gain_eta:0.08,rho:0.15}",
    "paired_delta": "16.2906892896 (idea minus baseline; lower is better)",
    "permutation_p": "0.0081"
  },
  "bench_report": {
    "bench_version": 1,
    "track": "tabular",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.006,
        "wd": 0.0
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "wd": 0.0
          },
          "mean": 9.14509105682373
        },
        {
          "cfg": {
            "lr": 0.001,
            "wd": 0.0001
          },
          "mean": 9.145180225372314
        },
        {
          "cfg": {
            "lr": 0.003,
            "wd": 0.0
          },
          "mean": 6.927029252052307
        },
        {
          "cfg": {
            "lr": 0.003,
            "wd": 0.0001
          },
          "mean": 6.927072286605835
        },
        {
          "cfg": {
            "lr": 0.006,
            "wd": 0.0
          },
          "mean": 6.792275428771973
        },
        {
          "cfg": {
            "lr": 0.006,
            "wd": 0.0001
          },
          "mean": 6.792290449142456
        }
      ],
      "full": {
        "mean": 7.667590081691742,
        "std": 1.7205125356211413,
        "per_seed": [
          6.4639973640441895,
          6.4978742599487305,
          7.33742094039917,
          6.869809150695801,
          7.3997697830200195,
          7.772507190704346,
          6.91866397857666,
          12.08067798614502
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 23.958279371261597,
      "std": 3.6945581466806723,
      "per_seed": [
        21.538589477539062,
        17.00618553161621,
        25.721471786499023,
        23.093345642089844,
        27.321157455444336,
        26.617502212524414,
        29.042064666748047,
        21.325918197631836
      ],
      "n": 8,
      "selected_cfg": {
        "lr": 0.003,
        "gain_eta": 0.08,
        "rho": 0.15
      }
    },
    "comparison": {
      "delta_mean": 16.290689289569855,
      "idea_wins": 0,
      "n_pairs": 8,
      "per_seed_diffs": [
        15.074592113494873,
        10.50831127166748,
        18.384050846099854,
        16.223536491394043,
        19.921387672424316,
        18.84499502182007,
        22.123400688171387,
        9.245240211486816
      ],
      "p_value": 0.0081,
      "mde": 3.7748401575264636,
      "mde_rel_pct": 49.2311158696371,
      "verdict": "idea worse (significant)",
      "system_worked": false
    },
    "mechanism_signature": {
      "trained_model_signature": {
        "final_gains": [
          0.008204753574549313,
          0.007716603211568231,
          0.03
        ],
        "clip_frequency": 1.0234375,
        "gradient_reversal_frequency": 1.1484375,
        "predicted_delta_mean": 0.27506878502578563,
        "observed_delta_mean": 4.82632114851212e-05,
        "predicted_delta_slope": -7.362199623823305,
        "observed_delta_slope": -7.362199623823305,
        "n_updates": 765,
        "confirmed": false,
        "idea_cfg": {
          "lr": 0.003,
          "gain_eta": 0.08,
          "rho": 0.15
        }
      },
      "math_check": {
        "prediction": "delta_gain=eta*r*g^2/(1+rho) at gain=reference",
        "predicted_slope": 0.08450000000000002,
        "observed_slope": 0.08450000000000005,
        "max_abs_error": 1.1102230246251565e-16,
        "pass": true
      },
      "idea_grid": [
        {
          "cfg": {
            "lr": 0.006,
            "gain_eta": 0.08,
            "rho": 0.15
          },
          "mean": 34.99524688720703
        },
        {
          "cfg": {
            "lr": 0.003,
            "gain_eta": 0.08,
            "rho": 0.15
          },
          "mean": 23.958279371261597
        },
        {
          "cfg": {
            "lr": 0.006,
            "gain_eta": 0.08,
            "rho": 0.15
          },
          "mean": 34.99524688720703
        }
      ]
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "stage2_bench.py",
    "bench_report.json",
    "run_output.json"
  ],
  "limitations": "Only the structurally matched tabular track was tested. Vision, sequence, dynamics, AdamW, deliberate learning-rate miscalibration, distribution shift, longer training, and equal-FLOP profiling were not tested. The implementation used layerwise gains for SGD rather than AdamW normalized directions.",
  "system_verdict": "failed",
  "practical_verdict": "harms",
  "mechanism_ok": 0,
  "system_judged": true
}