Small-Gain Constrained Neural Modules / bench_report.json
Beats tuned baseline
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": 0.005,
10 "rho_cap": null
11 },
12 "sweep": [
13 {
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20 {
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27 {
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31 },
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33 }
34 ],
35 "full": {
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45 0.0002475073270034045,
46 0.00037175044417381287
47 ],
48 "n": 8
49 }
50 },
51 "idea": {
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63 ],
64 "n": 8
65 },
66 "comparison": {
67 "delta_mean": -0.00022008368887327379,
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69 "n_pairs": 8,
70 "per_seed_diffs": [
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79 ],
80 "p_value": 0.0081,
81 "mde": 0.0001347953881261106,
82 "mde_rel_pct": 35.3003159435018,
83 "verdict": "idea better (significant)",
84 "system_worked": true
85 },
86 "mechanism_signature": {
87 "signature": {
88 "prediction": "projected recurrent operator-gain proxy stays <= 0.90",
89 "predicted_max": 0.7689717411994934,
90 "observed": [
91 {
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101 {
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106 {
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110 }
111 ],
112 "confirmed": true
113 },
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128 "n": 8
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143 "n": 8
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158 "n": 8
159 }
160 },
161 "selected_idea_lr": 0.005,
162 "structure": "GRU recurrent dynamics; spectral-gain projection is the only intervention"
163 },
164 "baseline_sweep_union_note": "Baseline evaluated all idea learning rates; baseline method has no additional decisive knob."
165}