Holonomy-designed recurrent memory / bench_report.json

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  1{
  2  "bench_version": 1,
  3  "track": "dynamics",
  4  "model": "rnn_small",
  5  "metric_direction": "lower is better",
  6  "n_seeds": 8,
  7  "baseline": {
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 64    "n": 8
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 66  "comparison": {
 67    "delta_mean": -9.047097955772188e-05,
 68    "idea_wins": 7,
 69    "n_pairs": 8,
 70    "per_seed_diffs": [
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 80    "p_value": 0.07265,
 81    "mde": 9.572692629336764e-05,
 82    "mde_rel_pct": 17.559023428269473,
 83    "verdict": "no significant win",
 84    "system_worked": false
 85  },
 86  "mechanism_signature": {
 87    "cfg": {
 88      "lr": 0.01,
 89      "cycle_weight": 0.03
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 91    "behavior": {
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 94      "observed": 1.0,
 95      "tolerance": 0.1,
 96      "confirmed": true,
 97      "n_models": 8
 98    }
 99  },
100  "idea_sweep": [
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104        "cycle_weight": 0.03
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118    },
119    {
120      "cfg": {
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122        "cycle_weight": 0.03
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125      "std": 0.0007589181455697688,
126      "per_seed": [
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128        0.002354319440200925,
129        0.0021086849737912416,
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136    },
137    {
138      "cfg": {
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140        "cycle_weight": 0.03
141      },
142      "mean": 0.00045470128861779813,
143      "std": 0.00014832940019044596,
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154    }
155  ],
156  "notes": "Matched dynamics task; baseline canonical train_model, idea same GRU backbone with differentiable factor-state regularizer."
157}