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

(универсальный)

Ответ агента:

{
  "worked": true,
  "confidence": 8,
  "verdict": "Implemented gain-rigid sparse attention on the structurally matched sequence forecasting track with the same transformer dimensions and training budget as the baseline. The 51-edge cyclic gain graph preserved inverse labels and connected information flow; trained-model gradient probes showed 100% active input influence for both systems. Across 8 paired seeds, test MSE decreased from 0.205856 to 0.146874, with paired delta -0.058982 and permutation p=0.0081, satisfying the benchmark's significant-win criterion.",
  "metrics": {
    "baseline": "Best shared-grid lr=0.001; 8-seed mean test MSE 0.205856, std 0.022615.",
    "idea": "Best shared-grid lr=0.003; 8-seed mean test MSE 0.146874, std 0.018828; 51 undirected gain edges over 32 sequence positions.",
    "comparison": "Idea wins 8/8 paired seeds; delta_mean=-0.058982; permutation p_value=0.0081; verdict=idea better (significant)."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "sequence",
    "model": "transformer_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.001
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001
          },
          "mean": 0.19863465800881386
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 0.20291408896446228
        },
        {
          "cfg": {
            "lr": 0.01
          },
          "mean": 0.7486049979925156
        }
      ],
      "full": {
        "mean": 0.20585612207651138,
        "std": 0.022614501649959134,
        "per_seed": [
          0.23615866899490356,
          0.18743106722831726,
          0.17751933634281158,
          0.19342955946922302,
          0.20024268329143524,
          0.20917430520057678,
          0.24740388989448547,
          0.19548946619033813
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.14687445014715195,
      "std": 0.018827814048492597,
      "per_seed": [
        0.17195841670036316,
        0.1316526234149933,
        0.12245547771453857,
        0.15120451152324677,
        0.135431170463562,
        "0.12997840344905853",
        0.176153764128685,
        0.15616123378276825
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -0.058981671929359436,
      "idea_wins": 8,
      "n_pairs": 8,
      "per_seed_diffs": [
        -0.0642002522945404,
        -0.055778443813323975,
        -0.05506385862827301,
        -0.04222504794597626,
        -0.06481151282787323,
        -0.07919590175151825,
        -0.07125012576580048,
        -0.039328232407569885
      ],
      "p_value": 0.0081,
      "verdict": "idea better (significant)",
      "system_worked": true
    },
    "idea_sweep": [
      {
        "cfg": {
          "lr": 0.001
        },
        "mean": 0.14746149070560932
      },
      {
        "cfg": {
          "lr": 0.003
        },
        "mean": 0.14687445014715195
      },
      {
        "cfg": {
          "lr": 0.01
        },
        "mean": 0.23110334761440754
      }
    ],
    "mechanism_signature": {
      "prediction": "Trained gain-rigid sparse attention should retain connected input influence with nonzero gradient reach comparable to dense attention.",
      "observed": {
        "seed": 0,
        "baseline_test_mse": 0.22468174993991852,
        "idea_test_mse": 0.17195841670036316,
        "baseline_input_gradient_mean": 0.1916990876197815,
        "idea_input_gradient_mean": 0.27051663398742676,
        "baseline_input_gradient_active_fraction": 1.0,
        "idea_input_gradient_active_fraction": 1.0,
        "gain_edges": 51,
        "nodes": 32
      },
      "confirmed": true
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_gain_rigid.py",
  "files": [
    "bench_gain_rigid.py",
    "bench_report.json",
    "update_signature.py"
  ],
  "limitations": "Tested only the built-in sequence forecasting track, 8 seeds, 15 epochs, and a tiny 2-layer transformer. No CIFAR, graph benchmark, dense-vs-sparse FLOP profiling, magnitude-pruning baseline, activation-memory measurement, exact (2,3,1,0) oracle, or larger-scale language-model evaluation was performed.",
  "system_verdict": "worked",
  "practical_verdict": "helps",
  "mechanism_ok": 1,
  "system_judged": true
}