Recursive Bellman Variance Targets / 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": {
  8    "best_cfg": {
  9      "lr": 0.01,
 10      "epochs": 8
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 27      {
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 32        "mean": 0.005194205295993015
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 35    "full": {
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 48      "n": 8
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 64    "n": 8,
 65    "selected_cfg": {
 66      "lr": 0.01,
 67      "epochs": 8
 68    }
 69  },
 70  "comparison": {
 71    "delta_mean": -0.0001843137142714113,
 72    "idea_wins": 2,
 73    "n_pairs": 8,
 74    "per_seed_diffs": [
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 84    "p_value": 0.9399,
 85    "mde": 0.003068421703670571,
 86    "mde_rel_pct": 72.81164504693035,
 87    "verdict": "no significant win",
 88    "system_worked": false
 89  },
 90  "mechanism_signature": {
 91    "prediction": "recursive predicted variance should calibrate observed squared error near 1 and track heteroscedasticity",
 92    "predicted_vs_observed": {
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103        0.005378848268044676,
104        0.00668907491812904
105      ],
106      "q_error_correlation_mean": -0.09872341295990021
107    },
108    "confirmed": false
109  },
110  "protocol_notes": {
111    "structural_match": "multi-step stochastic actuated pendulum rollout; recursive uncertainty applies to transition branches",
112    "baseline": "standard MSE training",
113    "idea": "mean/variance RNN with detached recursive child q plus between-child variance",
114    "n_train": 400,
115    "n_test": 200,
116    "branch_count": 4,
117    "grid_union": [
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119        "lr": 0.001,
120        "epochs": 8
121      },
122      {
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124        "epochs": 8
125      },
126      {
127        "lr": 0.01,
128        "epochs": 8
129      }
130    ]
131  }
132}