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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented and ran the full registered vision benchmark with directional PCE versus feature-MSE using the shared cnn_small architecture. PCE improved mean test error from 0.824375 to 0.806250, but the paired permutation test was not significant (p=0.1876), so the idea does not meet the benchmark win criterion. The trained-model mechanism signature was confirmed because unexplained mass was lower for PCE than baseline.",
  "metrics": {
    "baseline": "Mean test error 0.824375, std 0.046060; best baseline sweep config lr=0.003, coef=0.1.",
    "idea": "Mean test error 0.806250, std 0.030285; delta=-0.018125; 5/8 paired wins; p=0.1876; unexplained mass mean 0.021785 versus baseline 0.120660."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "vision",
    "model": "cnn_small",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.003,
        "coef": 0.1
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "coef": 0.03
          },
          "mean": 0.8412500023841858
        },
        {
          "cfg": {
            "lr": 0.003,
            "coef": 0.1
          },
          "mean": 0.8349999934434891
        },
        {
          "cfg": {
            "lr": 0.01,
            "coef": 0.3
          },
          "mean": 0.8937499821186066
        }
      ],
      "full": {
        "mean": 0.8243750035762787,
        "std": 0.04605956605822434,
        "per_seed": [
          0.7699999809265137,
          0.9049999713897705,
          0.8650000095367432,
          0.800000011920929,
          0.8149999976158142,
          0.8700000047683716,
          0.7850000262260437,
          0.7850000262260437
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.8062499985098839,
      "std": 0.03028510823634387,
      "per_seed": [
        0.7699999809265137,
        0.8550000190734863,
        0.8349999785423279,
        0.7549999952316284,
        0.8050000071525574,
        0.8050000071525574,
        0.8050000071525574,
        0.8199999928474426
      ],
      "n": 8,
      "unexplained_mass_per_seed": [
        0.029143333435058594,
        0.017951250076293945,
        0.0059850215911865234,
        0.02043771743774414,
        0.05716085433959961,
        0.006631910800933838,
        0.030822336673736572,
        0.006145954132080078
      ]
    },
    "comparison": {
      "delta_mean": -0.018125005066394806,
      "idea_wins": 5,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.0,
        -0.04999995231628418,
        -0.030000030994415283,
        -0.04500001668930054,
        -0.009999990463256836,
        -0.06499999761581421,
        0.019999980926513672,
        0.034999966621398926
      ],
      "p_value": 0.1876,
      "mde": 0.029557209568880215,
      "mde_rel_pct": 3.585408271800579,
      "verdict": "no significant win",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "Directional PCE should reduce teacher activation-event unexplained mass relative to feature-MSE baseline.",
      "predicted_vs_observed": {
        "baseline_unexplained_mass": 0.12066004425287247,
        "idea_unexplained_mass": 0.021784797310829163
      },
      "confirmed": true,
      "measurement": "Activation-range persistence surrogates measured on trained teacher/student CNN systems."
    },
    "sanity_check": {
      "probability_valid": true,
      "excess_identity_max_abs_error": 4.172325134277344e-06
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "stage2_bench.py",
    "bench_report.json",
    "run_stage2.txt"
  ],
  "limitations": "Only the registered vision track was tested, using 400 training examples, 200 test examples, and 5 epochs. The persistence computation is a differentiable activation-range surrogate rather than a full cubical-complex persistence backend. No reverse-direction ablation, bandwidth sweep beyond fixed sigma, calibration metric, augmentation, or larger-scale CNN experiment was run.",
  "system_verdict": "partial",
  "practical_verdict": "inconclusive",
  "mechanism_ok": 1,
  "system_judged": true
}