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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 10,
  "verdict": "Implemented the double-bracket spectral subspace optimizer in a local tabular mlp_tiny training loop and ran the full 8-seed paired protocol with baseline and idea sweeps. The idea significantly worsened test MSE: 20.5336 versus 14.6965 for the tuned baseline, paired delta +5.8371 with permutation p=0.0081. The trained-model mechanism signature was confirmed: QR maintained orthogonality at 4.13e-6 versus baseline 10.31, and the spectral objective increased on 83.5% of measured updates, but this did not improve task performance.",
  "metrics": {
    "baseline": "Adam plus orthogonality penalty, best lr=0.006 and mu=100.0; test MSE mean 14.6965 across 8 seeds.",
    "idea": "Adam task updates with QR retraction and covariance double-bracket flow, best lr=0.006 and spectral=0.003; test MSE mean 20.5336, 0/8 paired wins.",
    "paired_delta": "+5.8371 idea minus baseline; permutation p=0.0081; verdict idea worse (significant).",
    "mechanism": "Idea orthogonality error mean 4.13e-6 versus baseline 10.31; spectral objective increased on 83.5% of measured updates."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "tabular",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.006,
        "mu": 100.0
      },
      "sweep_lrs": [
        0.001,
        0.003,
        0.006
      ],
      "sweep_mu": [
        0.1,
        1.0,
        10.0,
        100.0
      ],
      "full_mean": 14.696504235458374
    },
    "idea": {
      "best_cfg": {
        "lr": 0.006,
        "spectral": 0.003
      },
      "full_mean": 20.533640146255493,
      "full_std": 3.6518642646282813,
      "per_seed": [
        29.84090805053711,
        19.540203094482422,
        20.137889862060547,
        17.428503036499023,
        18.707931518554688,
        20.394594192504883,
        18.142690658569336,
        20.076400756835938
      ]
    },
    "comparison": {
      "delta_mean": 5.837135910987854,
      "idea_wins": 0,
      "n_pairs": 8,
      "p_value": 0.0081,
      "verdict": "idea worse (significant)",
      "system_worked": false
    },
    "mechanism_signature": {
      "observed_baseline_orth_final_mean": 10.308380087216696,
      "observed_idea_orth_final_mean": 4.134903406338708e-06,
      "observed_idea_j_increase_fraction_mean": 0.8351063829787234,
      "observed_idea_j_delta_mean": 93.78326547145844,
      "confirmed": true
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_stage2.py",
  "files": [
    "bench_stage2.py",
    "bench_report.json",
    "bench_stdout.txt"
  ],
  "limitations": "Only the structurally matched built-in tabular track and mlp_tiny model were tested; vision, sequence, and dynamics were not run. The benchmark used 400 training and 400 test examples and 12 epochs, and no wall-clock or FLOP comparison was recorded. The optional Cayley integrator was not tested; the spectral covariance was computed from first-hidden-layer minibatch activations.",
  "system_verdict": "partial",
  "practical_verdict": "harms",
  "mechanism_ok": 1,
  "system_judged": true
}