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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented Residual-Pivoted Kernel Attention as a local replacement for transformer self-attention on the structurally matched sequence forecast task. The trained-model mechanism signature confirmed the predicted trace-decrease identity with 0.07% relative error, but the idea had mean test MSE 1.023332 versus 1.034990 for the tuned exact-attention baseline; paired delta was -0.011658 with permutation p=0.84055, so there was no significant win.",
  "metrics": {
    "baseline": "Exact transformer attention, tuned lr=0.001: mean test MSE 1.034990, std 0.096300. Sweep means: lr=0.001 1.003167, lr=0.003 2.889053, lr=0.01 10.712081.",
    "idea": "Residual-pivoted exponential-kernel attention, rank=4, lr=0.001: mean test MSE 1.023332, std 0.090481. Sweep means: lr=0.001 1.023332, lr=0.003 4.040841, lr=0.01 6.475607. Paired delta=-0.011658, 4/8 wins, p=0.84055."
  },
  "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": 1.003167301416397
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 2.8890531063079834
        },
        {
          "cfg": {
            "lr": 0.01
          },
          "mean": 10.712080866098404
        }
      ],
      "full": {
        "mean": 1.0349903628230095,
        "std": 0.09630027230457686,
        "per_seed": [
          0.898553729057312,
          1.0466291904449463,
          1.114719271659851,
          0.952767014503479,
          0.9697455167770386,
          0.9766437411308289,
          1.1204972262955322,
          1.2003672122955322
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 1.023332104086876,
      "std": 0.09048064334835597,
      "per_seed": [
        1.0840766429901123,
        0.9333583116531372,
        1.0344642400741577,
        1.211755394935608,
        1.0095853805541992,
        0.9797840714454651,
        1.0361275672912598,
        0.8975052237510681
      ],
      "n": 8,
      "best_cfg": {
        "lr": 0.001
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001
          },
          "mean": 1.023332104086876
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 4.04084075987339
        },
        {
          "cfg": {
            "lr": 0.01
          },
          "mean": 6.475606739521027
        }
      ]
    },
    "comparison": {
      "delta_mean": -0.011658258736133575,
      "idea_wins": 4,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.1855229139328003,
        -0.11327087879180908,
        -0.08025503158569336,
        0.2589883804321289,
        0.039839863777160645,
        0.0031403303146362305,
        -0.08436965942382812,
        -0.3028619885444641
      ],
      "p_value": 0.84055,
      "mde": 0.1482740607951793,
      "mde_rel_pct": 14.326129606729024,
      "verdict": "no significant win",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "E[trace decrease] = ||R||_F^2 / tr(R)",
      "predicted": 33.759521484375,
      "observed": 33.735044193267825,
      "relative_error": 0.0007250485205634143,
      "confirmed": true,
      "trained_model": true,
      "rank": 4
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "stage2_bench.py",
    "bench_report.json",
    "run.log"
  ],
  "limitations": "The benchmark was reduced to 200 training samples, 100 test samples, 3 epochs, and rank 4 after the original larger run exceeded 40 minutes. Only the built-in sequence forecast track was tested; no long-context, autoregressive decoding, peak-memory, latency, perplexity, or ranks 32/64/128 measurements were run.",
  "system_verdict": "partial",
  "practical_verdict": "no_effect",
  "mechanism_ok": 1,
  "system_judged": true
}