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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented coupled shared-prefix fine-minus-coarse gradient corrections with inverse-probability weighting and norm clipping in a matched mlp_tiny training system on a custom ordered AR(1) regression track. The mechanism signature was confirmed: mean correction norm 4.148 versus mean gradient norm 7.382, but clipping occurred on 42.4% of updates. Across 8 paired seeds and a parity-compliant learning-rate sweep, the idea was significantly worse: test MSE 1.9568 versus baseline 0.6354, paired delta +1.3214, permutation p=0.0081; therefore the system did not work as a training win.",
  "metrics": {
    "baseline": "Best lr=0.006; 8-seed test MSE mean 0.6354323998, std 0.3334732789; sweep means lr=0.001: 2.7850674093, lr=0.003: 0.9566748813, lr=0.006: 0.6123837829.",
    "idea": "Best lr=0.006; 8-seed test MSE mean 1.9568064436, std 0.6155676368; sweep means lr=0.001: 4.6203299761, lr=0.003: 3.1034981161, lr=0.006: 1.9568064436.",
    "paired_comparison": "delta_mean=+1.3213740438, idea_wins=0/8, permutation p=0.0081, verdict='idea worse (significant)'.",
    "mechanism_signature": "correction_norm_mean=4.1483772459, mean_gradient_norm=7.3824362755, gradient_lag1_mean=0.0805212060, clipping_fraction_mean=0.4244791667, confirmed=true"
  },
  "bench_report": {
    "bench_version": 1,
    "track": "markov_stream_regression",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.006
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001
          },
          "mean": 2.7850674092769623
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 0.9566748812794685
        },
        {
          "cfg": {
            "lr": 0.006
          },
          "mean": 0.6123837828636169
        }
      ],
      "full": {
        "mean": 0.6354323998093605,
        "std": 0.333473278949733,
        "per_seed": [
          0.36419546604156494,
          0.2755792737007141,
          1.0398473739624023,
          0.7699130177497864,
          0.5838971138000488,
          0.3222571909427643,
          0.47670307755470276,
          1.2510666847229004
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 1.956806443631649,
      "std": 0.615567636769626,
      "per_seed": [
        1.6518237590789795,
        0.9949750304222107,
        2.369184970855713,
        1.7660677433013916,
        3.053445816040039,
        1.3799846172332764,
        2.4778213500976562,
        1.9611482620239258
      ],
      "n": 8,
      "sweep": [
        {
          "cfg": {
            "lr": 0.001
          },
          "mean": 4.620329976081848,
          "std": 1.0518765869021856
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 3.1034981161355972,
          "std": 1.2009995397914373
        },
        {
          "cfg": {
            "lr": 0.006
          },
          "mean": 1.956806443631649,
          "std": 0.615567636769626
        }
      ]
    },
    "comparison": {
      "delta_mean": 1.3213740438222885,
      "idea_wins": 0,
      "n_pairs": 8,
      "per_seed_diffs": [
        1.2876282930374146,
        0.7193957567214966,
        1.3293375968933105,
        0.9961547255516052,
        2.4695487022399902,
        1.057727426290512,
        2.0011182725429535,
        0.7100815773010254
      ],
      "p_value": 0.0081,
      "mde": 0.5186169124394706,
      "mde_rel_pct": 81.61637848417293,
      "verdict": "idea worse (significant)",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "shared fine-minus-coarse corrections have smaller norm than raw fine gradients under correlated streams",
      "observed_correction_norm_mean": 4.148377245852362,
      "observed_mean_gradient_norm": 7.382436275482178,
      "observed_gradient_lag1_mean": 0.08052120597292914,
      "observed_clip_fraction_mean": 0.4244791666666667,
      "confirmed": true
    },
    "custom_track": {
      "name": "markov_stream_regression",
      "file": "markov_stream_bench.py",
      "domain": "optimizer"
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 markov_stream_bench.py",
  "files": [
    "markov_stream_bench.py",
    "artifacts/bench_report.json",
    "artifacts_run.txt"
  ],
  "limitations": "Tested one custom AR(1) regression stream with rho=0.9, one matched mlp_tiny architecture, Adam without decoupled weight decay, 8 epochs, and three learning rates. The full built-in tabular/vision/sequence/dynamics tracks, adaptive level-probability estimation, explicit mixing-time estimation, wall-clock speed, and RL training were not tested.",
  "system_verdict": "partial",
  "practical_verdict": "harms",
  "mechanism_ok": 1,
  "system_judged": true
}