{
  "bench_version": 1,
  "track": "sequence",
  "model": "transformer_tiny",
  "metric_direction": "lower is better",
  "n_seeds": 8,
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      "n": 8
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      0.9385600090026855,
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    "n": 8,
    "sweep": [
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        "cfg": {
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        "cfg": {
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          "temperature": 0.7,
          "lambda": 0.08,
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  },
  "comparison": {
    "delta_mean": -0.00023869425058364868,
    "idea_wins": 6,
    "n_pairs": 8,
    "per_seed_diffs": [
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      -3.081560134887695e-05,
      -0.00015550851821899414,
      9.685754776000977e-05,
      -1.33514404296875e-05,
      -0.001201331615447998,
      -0.00035881996154785156,
      2.47955322265625e-05
    ],
    "p_value": 0.0863,
    "mde": 0.0003498030498009527,
    "mde_rel_pct": 0.03645033901329803,
    "verdict": "no measurable effect",
    "system_worked": false
  },
  "mechanism_signature": {
    "prediction": "expansion penalty increases token-group expert neighborhood and reduces load dispersion",
    "baseline_observed": {
      "soft_C": 8.0,
      "mean_hard_neighborhood": 4.5625,
      "violation_fraction": 0.21875,
      "load_std": 446.43703842163086
    },
    "idea_observed": {
      "soft_C": 8.0,
      "mean_hard_neighborhood": 4.625,
      "violation_fraction": 0.21875,
      "load_std": 430.1180419921875
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    "confirmed": true
  },
  "idea_hyperparameter_grid": [
    {
      "lr": 0.003,
      "temperature": 0.7,
      "lambda": 0.01,
      "epochs": 3
    },
    {
      "lr": 0.003,
      "temperature": 0.7,
      "lambda": 0.03,
      "epochs": 3
    },
    {
      "lr": 0.003,
      "temperature": 0.7,
      "lambda": 0.08,
      "epochs": 3
    }
  ],
  "notes": "Matched compact sequence forecaster; same 8-expert dense MoE and training budget; CPU fallback used to avoid shared GPU contention."
}