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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented Conditional Sinkhorn Adversarial Augmentation in a local transformer_tiny sequence-training loop with debiased entropic Sinkhorn distance, a smooth residual trajectory generator, radius penalty, and projected multiplier update. On eight paired seeds, the idea had mean test MSE 0.4326547 versus 0.4329637 for the tuned baseline, but the paired improvement was not significant (delta=-0.0003090, p=0.0797), so the required benchmark win was not observed. The standalone translation-scaling math check passed, while the trained adversary was effectively inactive with mean Sinkhorn distance 9.94e-08 versus rho=0.035 and mean lambda 0.",
  "metrics": {
    "baseline": "sequence/transformer_tiny: mean test MSE 0.4329636656, std 0.0464950719, 8 seeds",
    "idea": "Conditional Sinkhorn: mean test MSE 0.4326546714, std 0.0466487336, paired delta -0.0003089942, 6/8 wins, permutation p=0.0797"
  },
  "bench_report": {
    "bench_version": 1,
    "track": "sequence",
    "model": "transformer_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.001,
        "weight_decay": 0.0,
        "epochs": 8,
        "adv_lr": 0.08,
        "lambda_lr": 0.2,
        "radius_scale": 0.1
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0,
            "epochs": 8,
            "adv_lr": 0.08,
            "lambda_lr": 0.2,
            "radius_scale": 0.1
          },
          "mean": 0.41393087804317474
        },
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0001,
            "epochs": 8,
            "adv_lr": 0.08,
            "lambda_lr": 0.2,
            "radius_scale": 0.1
          },
          "mean": 0.41419199854135513
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.0,
            "epochs": 8,
            "adv_lr": 0.08,
            "lambda_lr": 0.2,
            "radius_scale": 0.1
          },
          "mean": 0.46493180841207504
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.0001,
            "epochs": 8,
            "adv_lr": 0.08,
            "lambda_lr": 0.2,
            "radius_scale": 0.1
          },
          "mean": 0.4638128876686096
        },
        {
          "cfg": {
            "lr": 0.006,
            "weight_decay": 0.0,
            "epochs": 8,
            "adv_lr": 0.08,
            "lambda_lr": 0.2,
            "radius_scale": 0.1
          },
          "mean": 0.667711153626442
        },
        {
          "cfg": {
            "lr": 0.006,
            "weight_decay": 0.0001,
            "epochs": 8,
            "adv_lr": 0.08,
            "lambda_lr": 0.2,
            "radius_scale": 0.1
          },
          "mean": 0.5989826694130898
        }
      ],
      "full": {
        "mean": 0.43296366557478905,
        "std": 0.0464950719362365,
        "per_seed": [
          0.4563036859035492,
          0.3589187264442444,
          0.3965584337711334,
          0.443942666053772,
          0.4800455868244171,
          0.38371741771698,
          0.5046233534812927,
          0.4395994544029236
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.4326546713709831,
      "std": 0.046648733640043204,
      "per_seed": [
        0.45671361684799194,
        0.3578817546367645,
        0.3963860869407654,
        0.44397932291030884,
        0.4796009659767151,
        0.3833600878715515,
        0.50420743227005,
        0.43910810351371765
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -0.00030899420380592346,
      "idea_wins": 6,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.000409930944442749,
        -0.0010369718074798584,
        -0.000172346830368042,
        3.6656856536865234e-05,
        -0.00044462084770202637,
        -0.0003573298454284668,
        -0.0004159212112426758,
        -0.0004913508892059326
      ],
      "p_value": 0.0797,
      "mde": 0.00035339177440740587,
      "mde_rel_pct": 0.0816215776301353,
      "verdict": "no measurable effect",
      "system_worked": false
    },
    "mechanism_signature": {
      "math_and_nn_signature": {
        "prediction": "translation S approximately k*delta^2",
        "fitted_k": 0.7166269634451184,
        "max_relative_error": 0.022699180966030196,
        "S_values": [
          0.0,
          0.007007211446762085,
          0.02810005284845829,
          0.0634879618883133,
          0.11350420862436295,
          0.17858079075813293,
          0.25921881198883057
        ],
        "debiased_identity": 0.0,
        "confirmed": true,
        "nn_observed_mean_sinkhorn": 9.942629664028146e-08,
        "nn_observed_mean_lambda": 0.0,
        "nn_predicted_radius": 0.035
      },
      "idea_sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0,
            "epochs": 8,
            "adv_lr": 0.08,
            "lambda_lr": 0.2,
            "radius_scale": 0.1
          },
          "mean": 0.41374019533395767
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.0,
            "epochs": 8,
            "adv_lr": 0.08,
            "lambda_lr": 0.2,
            "radius_scale": 0.1
          },
          "mean": 0.4662386029958725
        },
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0,
            "epochs": 8,
            "adv_lr": 0.04,
            "lambda_lr": 0.2,
            "radius_scale": 0.07
          },
          "mean": 0.4139372706413269
        }
      ],
      "custom_track": null,
      "track_rationale": "Sequence forecasting has context-conditioned multi-token windows; residual trajectory augmentation acts on the generated sequence manifold."
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_experiment.py",
  "files": [
    "bench_experiment.py",
    "bench_report.json"
  ],
  "limitations": "Only the registered sequence track was tested, using 400 training samples, 200 test samples, 8 epochs, and a small three-setting idea sweep. No vision, dynamics, tabular, corruption-shift, wall-clock, or FLOP comparison was run; the adversarial residual basis was a lightweight two-mode manifold rather than a learned generator.",
  "system_verdict": "failed",
  "practical_verdict": "no_effect",
  "mechanism_ok": 0,
  "system_judged": true
}