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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented implicit two-factor higher-order attention as an end-to-end modification on the structurally matched sequence forecast track, with an identical single-factor baseline architecture and paired-seed training. The idea achieved MSE 1.016057 versus baseline 1.023103, a small improvement, but the permutation p-value was 0.34205, so there was no significant benchmark win. The trained-model contraction signature matched the product factorization with maximum error 0.0 and correlation 0.99999994, confirming the mechanism but not a task-performance improvement.",
  "metrics": {
    "baseline": "Full 8-seed test MSE 1.0231029689; best configuration lr=0.003, tau=0.25. Baseline sweep covered lr={0.001,0.003,0.01} and tau={0.25,0.5}.",
    "idea": "Full 8-seed test MSE 1.0160572454 at lr=0.003, tau=0.25; paired delta=-0.0070457235, 5/8 wins, permutation p=0.34205."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 run_bench.py",
  "files": [
    "run_bench.py",
    "bench_report.json"
  ],
  "limitations": "Only the built-in sequence forecast track was tested with 400 training examples, 100 test examples, 15 epochs, and a compact transformer. No vision, tabular, dynamics, custom-track, explicit materialized tensor memory benchmark, signed-filler ablation, or larger-scale speed study was run.",
  "bench_report": {
    "bench_version": 1,
    "track": "sequence",
    "model": "shared_transformer_token_attention",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.003,
        "tau": 0.25
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "tau": 0.25
          },
          "mean": 1.0073408037424088
        },
        {
          "cfg": {
            "lr": 0.001,
            "tau": 0.5
          },
          "mean": 1.0072430819272995
        },
        {
          "cfg": {
            "lr": 0.003,
            "tau": 0.25
          },
          "mean": 0.9897160083055496
        },
        {
          "cfg": {
            "lr": 0.003,
            "tau": 0.5
          },
          "mean": 0.9971326589584351
        },
        {
          "cfg": {
            "lr": 0.01,
            "tau": 0.25
          },
          "mean": 1.041932761669159
        },
        {
          "cfg": {
            "lr": 0.01,
            "tau": 0.5
          },
          "mean": 1.0047774612903595
        }
      ],
      "full": {
        "mean": 1.0231029689311981,
        "std": 0.11072729156070826,
        "per_seed": [
          0.9939484000205994,
          0.8335484266281128,
          1.1223986148834229,
          1.0089685916900635,
          0.9049072861671448,
          1.0517245531082153,
          1.2106995582580566,
          1.0586283206939697
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 1.0160572454333305,
      "std": 0.10683002426665564,
      "per_seed": [
        0.9757922291755676,
        0.856389582157135,
        1.1297253370285034,
        1.0210782289505005,
        0.8922733664512634,
        1.0258088111877441,
        1.2052284479141235,
        1.0221619606018066
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -0.007045723497867584,
      "idea_wins": 5,
      "n_pairs": 8,
      "per_seed_diffs": [
        -0.01815617084503174,
        0.022841155529022217,
        0.007326722145080566,
        0.012109637260437012,
        -0.012633919715881348,
        -0.02591574192047119,
        -0.0054711103439331055,
        -0.036466360092163086
      ],
      "p_value": 0.34205,
      "mde": 0.016858466245238783,
      "mde_rel_pct": 1.6477780592162945,
      "verdict": "no measurable effect",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "Two-factor contraction equals the product of trained token-query similarities.",
      "predicted_max_abs_error": 0.0,
      "observed_max_abs_error": 0.0,
      "predicted_correlation": 1.0,
      "observed_correlation": 0.9999999403953552,
      "confirmed": true
    },
    "track_justification": "The sequence forecast track contains multi-token correlations, matching the conjunction-attention structure."
  },
  "system_verdict": "partial",
  "practical_verdict": "no_effect",
  "mechanism_ok": 1,
  "system_judged": true
}