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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Built a finite-resolution gradient-state channel with quantization and an actionable accept/reject controller, evaluated against the same mlp_tiny architecture and tabular Friedman#1 task. The mechanism sanity check observed acquired-information growth with slope 0.9964 and R² 0.999998, while the fine-resolution useful-information increment was only 0.000744, supporting the toy prediction. However, the controller significantly worsened test MSE: delta_mean=+1.5471 with permutation p=0.0081, so there is no benchmark win.",
  "metrics": {
    "baseline": "Tuned SGD mlp_tiny, 8-seed mean test MSE approximately 5.42893 (derived from idea mean 6.97602 minus paired delta 1.54709); per-seed baseline values were [5.57806, 5.47383, 5.20039, 4.85402, 6.36176, 5.23242, 5.25580, 5.47516].",
    "idea": "Actionable quantized controller, best configuration lr=0.003, delta=0.5, lambda=0.03; 8-seed mean test MSE=6.97602, std=0.26848; paired delta=+1.54709, p=0.0081; accepted fraction=0.7734."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_actionable.py",
  "files": [
    "bench_actionable.py",
    "bench_report.json"
  ],
  "limitations": "Only the structurally matched tabular track and mlp_tiny model were tested; vision, sequence, dynamics, larger MLP replication, learned utility prediction, Adam comparison, wall-clock speed, and stability-boundary sweeps were not tested. The mechanism signature combines a trained-model update-behaviour measurement with the required quantitative channel sanity check; it is not evidence of improved task performance.",
  "bench_report": {
    "bench_version": 1,
    "track": "tabular",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.003,
        "weight_decay": 0.0
      },
      "full": {
        "mean": 5.428929984569549,
        "std": 0.445186,
        "per_seed": [
          5.578059196472168,
          5.473832130432129,
          5.200394153594971,
          4.854016304016113,
          6.361762046813965,
          5.232422828674316,
          5.255797863006592,
          5.475155353546143
        ],
        "n": 8
      },
      "grid_union": "Baseline evaluated lr values 0.001, 0.003, 0.006 and weight decay values 0.0, 0.0001; idea evaluated the same learning-rate union."
    },
    "idea": {
      "mean": 6.97602242231369,
      "std": 0.26847889510235706,
      "per_seed": [
        6.515726566314697,
        6.685352802276611,
        7.203780651092529,
        7.1638712882995605,
        6.83967924118042,
        7.354576110839844,
        6.9066338539123535,
        7.138558864593506
      ],
      "n": 8,
      "best_cfg": {
        "lr": 0.003,
        "delta": 0.5,
        "lambda": 0.03
      }
    },
    "comparison": {
      "delta_mean": 1.5470924377441406,
      "idea_wins": 0,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.9376673698425293,
        1.2115206718444824,
        2.0033864974975586,
        2.3098549842834473,
        0.4779171943664551,
        2.1221532821655273,
        1.6508359909057617,
        1.6634035110473633
      ],
      "p_value": 0.0081,
      "verdict": "idea worse (significant)",
      "system_worked": false
    },
    "mechanism_signature": {
      "predicted": "Finite-resolution channel suppresses noisy observations and rejects anti-aligned updates.",
      "observed": {
        "accepted_fraction": 0.7734375,
        "mean_raw_grad_norm": 27.050026551771833,
        "mean_channel_update_norm": 27.05706130868445,
        "delta": 0.5
      },
      "confirmed": true
    }
  },
  "system_verdict": "partial",
  "practical_verdict": "harms",
  "mechanism_ok": 1,
  "system_judged": true
}