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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented Co-Prime Virtual-Aperture Attention on the structurally matched sequence forecasting track using an end-to-end trained two-stage sparse-attention transformer, with dense causal attention as the matched baseline. The math check held: 6 physical offsets and 12 virtual offsets for (3,4), versus 6 for the non-coprime (2,4) control. The idea improved mean MSE from 0.146195 to 0.139847, but the paired permutation test was not significant (p=0.3787), so this is not an established benchmark win; the trained-model gradient signature confirmed all predicted virtual offsets.",
  "metrics": {
    "baseline": "Dense causal transformer, best lr=0.003, mean test MSE 0.146195, std 0.023272",
    "idea": "Two-stage CPA sparse transformer, M1=3 M2=4, best lr=0.003, mean test MSE 0.139847, std 0.011513",
    "paired_delta_mean": -0.0063477931544184685,
    "idea_wins": 5,
    "n_pairs": 8,
    "permutation_p_value": 0.3787,
    "mechanism_signature_confirmed": true
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "stage2_bench.py",
    "bench_report.json"
  ],
  "bench_report": {
    "bench_version": 1,
    "track": "sequence",
    "model": "transformer_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.003
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001
          },
          "mean": 0.1494479700922966
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 0.14718072488904
        },
        {
          "cfg": {
            "lr": 0.01
          },
          "mean": 0.23090055957436562
        }
      ],
      "full": {
        "mean": 0.14619511179625988,
        "std": 0.023272144211599243,
        "per_seed": [
          0.19329272210597992,
          0.12494795769453049,
          0.12228920310735703,
          0.14819301664829254,
          0.1282258927822113,
          0.1327003538608551,
          0.1498585343360901,
          0.17005321383476257
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.1398473186418414,
      "std": 0.011513116013381047,
      "per_seed": [
        0.15645107626914978,
        0.13615846633911133,
        0.14752109348773956,
        0.13047337532043457,
        0.1331927627325058,
        0.12439116090536118,
        0.13342459499835968,
        0.1571660190820694
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -0.0063477931544184685,
      "idea_wins": 5,
      "n_pairs": 8,
      "per_seed_diffs": [
        -0.03684164583683014,
        0.011210508644580841,
        0.025231890380382538,
        -0.01771964132785797,
        0.004966869950294495,
        -0.008309192955493927,
        -0.016433939337730408,
        -0.012887194752693176
      ],
      "p_value": 0.3787,
      "mde": 0.016241711340640855,
      "mde_rel_pct": 11.109613133492175,
      "verdict": "no significant win",
      "system_worked": false
    },
    "math_check": {
      "M1": 3,
      "M2": 4,
      "gcd": 1,
      "physical_count": 6,
      "physical_formula": 6,
      "virtual_count": 12,
      "noncoprime_2_4_virtual_count": 6,
      "claim_holds": true
    },
    "idea_sweep": [
      {
        "lr": 0.001,
        "mean": 0.1506526917219162
      },
      {
        "lr": 0.003,
        "mean": 0.1398473186418414
      },
      {
        "lr": 0.01,
        "mean": 0.3220752514898777
      }
    ],
    "mechanism_signature": {
      "predicted_virtual_offsets": [
        0,
        3,
        4,
        6,
        7,
        8,
        9,
        10,
        11,
        13,
        14,
        17
      ],
      "predicted_offsets_observed": [
        0,
        3,
        4,
        6,
        7,
        8,
        9,
        10,
        11,
        13,
        14,
        17
      ],
      "coverage": 1.0,
      "confirmed": true
    }
  },
  "limitations": "The built-in sequence forecasting track was tested only; no language modeling, longer contexts, optimized sparse kernels, wall-clock throughput, activation-memory measurement, or additional coprime pairs were evaluated. The benchmark harness README was initially absent at the requested path but was subsequently located and used.",
  "system_verdict": "partial",
  "practical_verdict": "inconclusive",
  "mechanism_ok": 1,
  "system_judged": true
}