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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented an end-to-end IID-versus-Owen-scrambled-Sobol diffusion-noise intervention with identical MLP architectures, matched datasets, shared learning-rate sweep, and eight paired seeds. Test MSE was essentially unchanged: baseline 0.0760472 versus idea 0.0760515, paired delta +0.0000043 with permutation p=1.0. Sobol lowered trained-model batch-mean standard deviation by about 61% (0.000336 versus 0.000862), but the observed scaling slope was -0.419 versus IID -0.660, so the stage-1 N^-1 mechanism prediction did not transfer quantitatively.",
  "metrics": {
    "baseline": "conditional_multitoken_diffusion / mlp_tiny; best lr=0.003; full 8-seed test MSE mean 0.0760472286, std 0.00776520.",
    "idea": "Owen-scrambled Sobol Gaussian corruption; best lr=0.003; full 8-seed test MSE mean 0.0760515332, std 0.00745630; paired delta +4.3046e-06; permutation p=1.0; batch-mean SD ratio Sobol/IID=0.3894; observed slopes Sobol -0.4193, IID -0.6601."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "conditional_multitoken_diffusion",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.003
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001
          },
          "mean": 0.08654761128127575
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 0.07295109704136848
        },
        {
          "cfg": {
            "lr": 0.01
          },
          "mean": 0.07489615492522717
        }
      ],
      "full": {
        "mean": 0.07604722864925861,
        "std": 0.007765201697800962,
        "per_seed": [
          0.06751493364572525,
          0.06798635423183441,
          0.08183492720127106,
          0.07446817308664322,
          0.08383433520793915,
          0.0655355304479599,
          0.08018559962511063,
          0.0870179757475853
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.07605153322219849,
      "std": 0.007456295188304263,
      "per_seed": [
        0.06727362424135208,
        0.06813433766365051,
        0.08250146359205246,
        0.07412734627723694,
        0.0823955237865448,
        0.06638552248477936,
        0.08205603063106537,
        0.08553841710090637
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": 4.304572939872742e-06,
      "idea_wins": 4,
      "n_pairs": 8,
      "per_seed_diffs": [
        -0.00024130940437316895,
        0.00014798343181610107,
        0.0006665363907814026,
        -0.0003408268094062805,
        -0.0014388114213943481,
        0.000849992036819458,
        0.0018704310059547424,
        -0.0014795586466789246
      ],
      "p_value": 1.0,
      "mde": 0.0009544449208548851,
      "mde_rel_pct": 1.2550686432728932,
      "verdict": "no measurable effect",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "scrambled Sobol batch-mean variance lower and slope closer to -1 than IID",
      "trained_model_batch_mean_sd": {
        "iid": 0.0008616268068651571,
        "sobol": 0.0003355427185340906
      },
      "observed_log2N_slopes": {
        "iid": -0.6601498584773617,
        "sobol": -0.4193229684723195
      },
      "variance_ratio_sobol_over_iid": 0.3894292933560067,
      "confirmed": false
    },
    "custom_track": {
      "name": "conditional_multitoken_diffusion",
      "file": "bench/custom_tracks/conditional_multitoken_diffusion.py",
      "domain": "diffusion-sampling"
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 sobol_bench.py",
  "files": [
    "sobol_bench.py",
    "bench_report.json"
  ],
  "limitations": "This is a small synthetic multi-token diffusion-denoising track rather than a learned image diffusion sampler or probability-flow ODE with Jacobian importance weights. The benchmark used 400 training and 200 test examples, 12 epochs, and a small MLP; exact weighted transport correction, Hutchinson divergence estimation, wall-clock sampling cost, and high-dimensional image sampling were not tested. Sobol generation used random(n) for the 400-example training size, producing a warning because the size is not a power of two.",
  "system_verdict": "failed",
  "practical_verdict": "no_effect",
  "mechanism_ok": 0,
  "system_judged": true
}