Gain-Rigid Sparse Attention / bench_report.json
Beats tuned baseline
1{
2 "bench_version": 1,
3 "track": "sequence",
4 "model": "transformer_tiny",
5 "metric_direction": "lower is better",
6 "n_seeds": 8,
7 "baseline": {
8 "best_cfg": {
9 "lr": 0.001
10 },
11 "sweep": [
12 {
13 "cfg": {
14 "lr": 0.001
15 },
16 "mean": 0.19863465800881386
17 },
18 {
19 "cfg": {
20 "lr": 0.003
21 },
22 "mean": 0.20291408896446228
23 },
24 {
25 "cfg": {
26 "lr": 0.01
27 },
28 "mean": 0.7486049979925156
29 }
30 ],
31 "full": {
32 "mean": 0.20585612207651138,
33 "std": 0.022614501649959134,
34 "per_seed": [
35 0.23615866899490356,
36 0.18743106722831726,
37 0.17751933634281158,
38 0.19342955946922302,
39 0.20024268329143524,
40 0.20917430520057678,
41 0.24740388989448547,
42 0.19548946619033813
43 ],
44 "n": 8
45 }
46 },
47 "idea": {
48 "mean": 0.14687445014715195,
49 "std": 0.018827814048492597,
50 "per_seed": [
51 0.17195841670036316,
52 0.1316526234149933,
53 0.12245547771453857,
54 0.15120451152324677,
55 0.135431170463562,
56 0.12997840344905853,
57 0.176153764128685,
58 0.15616123378276825
59 ],
60 "n": 8
61 },
62 "comparison": {
63 "delta_mean": -0.058981671929359436,
64 "idea_wins": 8,
65 "n_pairs": 8,
66 "per_seed_diffs": [
67 -0.0642002522945404,
68 -0.055778443813323975,
69 -0.05506385862827301,
70 -0.04222504794597626,
71 -0.06481151282787323,
72 -0.07919590175151825,
73 -0.07125012576580048,
74 -0.039328232407569885
75 ],
76 "p_value": 0.0081,
77 "mde": 0.011451699601831964,
78 "mde_rel_pct": 5.5629628530433815,
79 "verdict": "idea better (significant)",
80 "system_worked": true
81 },
82 "idea_sweep": [
83 {
84 "cfg": {
85 "lr": 0.001
86 },
87 "full": {
88 "mean": 0.14746149070560932,
89 "std": 0.012264365784412576,
90 "per_seed": [
91 0.16718325018882751,
92 0.12648990750312805,
93 0.13353104889392853,
94 0.15779778361320496,
95 0.14990615844726562,
96 0.14269356429576874,
97 0.15414929389953613,
98 0.14794091880321503
99 ],
100 "n": 8
101 }
102 },
103 {
104 "cfg": {
105 "lr": 0.003
106 },
107 "full": {
108 "mean": 0.14687445014715195,
109 "std": 0.018827814048492597,
110 "per_seed": [
111 0.17195841670036316,
112 0.1316526234149933,
113 0.12245547771453857,
114 0.15120451152324677,
115 0.135431170463562,
116 0.12997840344905853,
117 0.176153764128685,
118 0.15616123378276825
119 ],
120 "n": 8
121 }
122 },
123 {
124 "cfg": {
125 "lr": 0.01
126 },
127 "full": {
128 "mean": 0.23110334761440754,
129 "std": 0.04669852475794671,
130 "per_seed": [
131 0.23717589676380157,
132 0.19244495034217834,
133 0.21007037162780762,
134 0.2582976818084717,
135 0.203369140625,
136 0.19808459281921387,
137 0.3420470058917999,
138 0.2073371410369873
139 ],
140 "n": 8
141 }
142 }
143 ],
144 "shared_architecture": true,
145 "gain_graph_edges": 51,
146 "mechanism_signature": {
147 "prediction": "trained gain-rigid sparse attention should retain connected input influence, with nonzero gradient reach comparable to dense attention",
148 "observed": {
149 "seed": 0,
150 "baseline_test_mse": 0.22468174993991852,
151 "idea_test_mse": 0.17195841670036316,
152 "baseline_input_gradient_mean": 0.1916990876197815,
153 "idea_input_gradient_mean": 0.27051663398742676,
154 "baseline_input_gradient_active_fraction": 1.0,
155 "idea_input_gradient_active_fraction": 1.0,
156 "gain_edges": 51,
157 "nodes": 32
158 },
159 "confirmed": true
160 }
161}