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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented First-Spike Laplacian attention as an end-to-end replacement for scaled dot-product attention in the structurally matched sequence forecasting track, using identical transformer projections, feed-forward blocks, optimizer, epochs, and batch size. Across 8 paired seeds, the idea had higher MSE (0.3292590510 vs 0.3237519413; paired delta +0.0055071097) and was not significant (permutation p=0.63405), so there is no observed benchmark win. The trained-model mechanism signature was confirmed: attention rows were normalized and positive, with row-sum error 2.38e-7 and learned sigma values near initialization.",
  "metrics": {
    "baseline": "Scaled dot-product transformer_tiny on sequence forecasting; best lr=0.003; 8-seed test MSE mean 0.3237519413, std 0.0466246347.",
    "idea": "First-Spike Laplacian transformer_tiny using sigmoid latency surrogates, L1 distances, and learned positive bandwidth; best sigma=0.7 at lr=0.003; 8-seed test MSE mean 0.3292590510, std 0.0575468007.",
    "paired_comparison": "delta_mean=+0.0055071097, idea_wins=3/8, permutation p=0.63405, verdict=no significant win.",
    "baseline_sweep": "lr=0.0015: 0.3423246816; lr=0.003: 0.3276267201; lr=0.006: 0.5004829466 on 4 sweep seeds.",
    "idea_sweep": "sigma=0.35: 0.3341145515; sigma=0.7: 0.3163300715; sigma=1.4: 0.3244466111 on 4 sweep seeds, all at lr=0.003.",
    "operation_accounting": "Per query-key channel, dot-product attention uses 32 multiplications; Laplacian attention uses 0 query-key channel multiplications and 32 subtract/absolute operations, excluding projections and reductions."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "sequence",
    "model": "transformer_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.003,
        "epochs": 12
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.0015,
            "epochs": 12
          },
          "mean": 0.3423246815800667
        },
        {
          "cfg": {
            "lr": 0.003,
            "epochs": 12
          },
          "mean": 0.32762672007083893
        },
        {
          "cfg": {
            "lr": 0.006,
            "epochs": 12
          },
          "mean": 0.5004829466342926
        }
      ],
      "full": {
        "mean": 0.32375194132328033,
        "std": 0.046624634658664434,
        "per_seed": [
          0.34710654616355896,
          0.2919960618019104,
          0.30691948533058167,
          0.3644847869873047,
          0.31837883591651917,
          0.24451658129692078,
          0.4092934727668762,
          0.3073197603225708
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.3292590510100126,
      "std": 0.057546800666266544,
      "per_seed": [
        0.34751513600349426,
        0.24287833273410797,
        0.32763731479644775,
        0.347289502620697,
        0.3484017848968506,
        0.23375016450881958,
        0.41173285245895386,
        0.37486732006073
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": 0.005507109686732292,
      "idea_wins": 3,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.00040858983993530273,
        -0.04911772906780243,
        0.02071782946586609,
        -0.017195284366607666,
        0.03002294898033142,
        -0.010766416788101196,
        0.0024393796920776367,
        0.06754755973815918
      ],
      "p_value": 0.63405,
      "mde": 0.029104389124102455,
      "mde_rel_pct": 8.989718796787217,
      "verdict": "no significant win",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "Laplacian rows are normalized, nonnegative, and larger sigma gives less selective attention.",
      "trained_model_observed": {
        "row_sum_error": 2.384185791015625e-07,
        "min_attention": 1.5814957521342876e-07,
        "mean_entropy_first_two_seeds": 2.5159993171691895,
        "learned_sigma_first_two_seeds": [
          0.7006007432937622,
          0.7081389427185059
        ]
      },
      "predicted": {
        "row_sum_error": 0.0,
        "min_attention": 0.0
      },
      "confirmed": true
    },
    "idea_sweep": [
      {
        "cfg": {
          "lr": 0.003,
          "epochs": 12,
          "sigma": 0.35
        },
        "mean": 0.33411455154418945,
        "per_seed": [
          0.3964906334877014,
          0.2513992488384247,
          0.33635154366493225,
          0.35221678018569946
        ]
      },
      {
        "cfg": {
          "lr": 0.003,
          "epochs": 12,
          "sigma": 0.7
        },
        "mean": 0.31633007153868675,
        "per_seed": [
          0.34751513600349426,
          0.24287833273410797,
          0.32763731479644775,
          0.347289502620697
        ]
      },
      {
        "cfg": {
          "lr": 0.003,
          "epochs": 12,
          "sigma": 1.4
        },
        "mean": 0.3244466111063957,
        "per_seed": [
          0.3361342251300812,
          0.26523905992507935,
          0.3481976091861725,
          0.3482155501842499
        ]
      }
    ],
    "best_idea_cfg": {
      "lr": 0.003,
      "epochs": 12,
      "sigma": 0.7
    },
    "operation_accounting": {
      "dot_qk_channel_multiplications_per_pair": 32,
      "lap_qk_channel_multiplications_per_pair": 0,
      "lap_abs_subtracts_per_pair": 32
    },
    "runtime_note": "400 train/200 test, 12 epochs, batch 128, 8 paired seeds"
  },
  "how_to_run": "cd /home/maxwelhelp/all/math2nn/experiments/exp2152_first-spike-laplacian-attention && /home/maxwelhelp/main/bin/python3 bench_experiment.py",
  "files": [
    "bench_experiment.py",
    "bench_report.json",
    "bench_stdout.txt",
    "laplacian_attention.py",
    "verify_module.py"
  ],
  "limitations": "Only the built-in sequence forecasting track was tested; no CIFAR spiking ViT, actual first-spike event simulation, wall-clock kernel benchmark, GPU energy measurement, or larger/deeper transformer was evaluated. The latency representation used a differentiable sigmoid surrogate rather than discrete first-spike times.",
  "system_verdict": "partial",
  "practical_verdict": "inconclusive",
  "mechanism_ok": 1,
  "system_judged": true
}