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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "The tau-leaped categorical refinement was implemented as an end-to-end modification of a shared MLP on a custom categorical-energy classification track. Its mechanism prediction was confirmed, but it worsened test error and was not statistically significant, so the idea did not win.",
  "metrics": {
    "baseline": "lr=0.01; mean test error 0.20250; std 0.03614",
    "idea": "h=0.02, lr=0.01; mean test error 0.21250; std 0.04822; paired delta +0.01000; permutation p=0.3679"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "categorical_track.py",
    "stage2_bench.py",
    "bench_report.json"
  ],
  "limitations": "A custom track was necessary because no built-in track contains categorical discrete-energy structure. This tested training-time stochastic refinement rather than inference-time diffusion/VAE sampling; exact Gillespie quality, GPU throughput, and the proposed 4x speedup were not tested.",
  "bench_report": {
    "bench_version": 1,
    "track": "categorical_energy",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.01
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001
          },
          "mean": 0.293750003
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 0.269999996
        },
        {
          "cfg": {
            "lr": 0.01
          },
          "mean": 0.2075
        }
      ],
      "full": {
        "mean": 0.2025,
        "std": 0.0361421,
        "per_seed": [
          0.22,
          0.2,
          0.195,
          0.215,
          0.265,
          0.15,
          0.15,
          0.225
        ],
        "n": 8
      }
    },
    "idea": {
      "best_cfg": {
        "lr": 0.01,
        "h": 0.02
      },
      "sweep": [
        {
          "h": 0.02,
          "mean": 0.2125,
          "std": 0.0482182,
          "per_seed": [
            0.255,
            0.185,
            0.18,
            0.23,
            0.295,
            0.205,
            0.125,
            0.225
          ]
        },
        {
          "h": 0.05,
          "mean": 0.221875
        },
        {
          "h": 0.1,
          "mean": 0.24875
        }
      ],
      "mean": 0.2125,
      "std": 0.0482182,
      "per_seed": [
        0.255,
        0.185,
        0.18,
        0.23,
        0.295,
        0.205,
        0.125,
        0.225
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": 0.01,
      "idea_wins": 3,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.035,
        -0.015,
        -0.015,
        0.015,
        0.03,
        0.055,
        -0.025,
        0.0
      ],
      "p_value": 0.3679,
      "verdict": "no significant win",
      "system_worked": false
    },
    "custom_track": {
      "name": "categorical_energy",
      "file": "categorical_track.py",
      "domain": "discrete_energy_sampling"
    },
    "mechanism_signature": {
      "quantity": "per-leap active proposal count versus resolved coordinate updates",
      "predicted": 26.5915291,
      "observed": 26.3515625,
      "relative_error": 0.00902417,
      "confirmed": true
    }
  },
  "system_verdict": "partial",
  "practical_verdict": "inconclusive",
  "mechanism_ok": 1,
  "system_judged": true
}