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gmnet/conclusions/local_results/20260712/README.md

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+# GmNet Local Results 2026-07-12
+
+This directory contains persistent, data-free copies of the reportable local
+experiment artifacts. Raw checkpoints, per-sample outputs, logs, cached data,
+and exported ONNX binaries remain under `/tmp/gmnet_runs` or
+`/tmp/gmnet_data`.
+
+The consolidated Chinese report is:
+
+`/nfs/ywang29/GmNet/journal_exp/docs/LOCAL_EXPERIMENT_CONCLUSIONS.md`
+
+Artifact map:
+
+- `e0_smoke/`: single-GPU and 8-GPU execution evidence.
+- `e1_operator/`: operator and basis spectral audit.
+- `e1_trained/`: trained-feature audit and nonfinite diagnostics.
+- `e2_synthetic/`: controlled synthetic mechanism results.
+- `e3_cifar100/`: three-seed corruption and paired-bootstrap summary.
+- `e4_cifar100/`: three-seed frozen intervention summary.
+- `legacy_imagenet_clean/`: historical full-BN 50k clean result.
+- `e6_imagenet_partial/`: fixed 5k corruption protocol.
+- `e8_imagenet_partial/`: fixed 1k PGD protocol.
+- `e12/`: five-process aggregate and per-process JSON/Markdown, excluding ONNX binaries.
+
+These artifacts do not contain dataset files.

gmnet/conclusions/local_results/20260712/e12/AGGREGATE.md

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+# E12 Five-Process Aggregate
+
+Each table entry is mean +/- sample standard deviation across five independent processes on an otherwise idle host.
+
+| Device | Precision | Batch | Process p50 (ms) | Process p95 (ms) | Throughput (image/s) | Peak CUDA MiB |
+|---|---|---:|---:|---:|---:|---:|
+| cpu | fp32 | 1 | 23.084 +/- 0.138 | 23.543 +/- 0.249 | 43.20 +/- 0.23 | n/a |
+| cpu | fp32 | 32 | 501.900 +/- 23.229 | 554.875 +/- 10.161 | 63.99 +/- 2.40 | n/a |
+| cuda:0 | bf16 | 1 | 8.277 +/- 0.101 | 8.417 +/- 0.098 | 120.51 +/- 1.20 | 59.6 |
+| cuda:0 | bf16 | 32 | 9.703 +/- 0.007 | 9.715 +/- 0.006 | 3296.79 +/- 2.21 | 226.3 |
+| cuda:0 | fp32 | 1 | 6.357 +/- 0.031 | 6.499 +/- 0.033 | 157.22 +/- 0.70 | 49.8 |
+| cuda:0 | fp32 | 32 | 10.681 +/- 0.008 | 10.694 +/- 0.008 | 2995.60 +/- 2.25 | 348.1 |
+| onnxruntime-cpu | fp32 | 1 | 6.090 +/- 0.118 | 6.366 +/- 0.166 | 163.41 +/- 3.40 | n/a |

gmnet/conclusions/local_results/20260712/e12/aggregate.json

@@ -0,0 +1,391 @@
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+      },
+      "precision": "fp32",
+      "process_count": 5
+    }
+  ],
+  "experiment": "E12",
+  "int8": {
+    "reason": "No validated full-model INT8 calibration and quantization pipeline is available. Linear-only dynamic quantization is not reported as whole-model INT8.",
+    "status": "blocked"
+  },
+  "process_count": 5,
+  "schema_version": 1,
+  "source_results": [
+    "/tmp/gmnet_runs/e12_official/process0/results.json",
+    "/tmp/gmnet_runs/e12_official/process1/results.json",
+    "/tmp/gmnet_runs/e12_official/process2/results.json",
+    "/tmp/gmnet_runs/e12_official/process3/results.json",
+    "/tmp/gmnet_runs/e12_official/process4/results.json"
+  ],
+  "status": "aggregated_five_independent_processes"
+}

gmnet/conclusions/local_results/20260712/e1_operator/REPORT.md

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+# E1 Spectral Measurement Audit
+
+## Protocol
+
+- Seeds: [0, 1, 2, 3, 4]
+- Gates: relu6_self, relu_self, gelu_self, identity, no_gate, smooth_clipped_static
+- Subjects: gate output, complete random-initialized block output, and residual branch delta.
+- Bases: orthonormal FFT, orthonormal DCT-II, and a manual orthonormal 2-D Haar transform.
+- All energy metrics remove each map's spatial mean before measuring spectrum.
+- frequency_auc is the integral of surviving spectral energy over normalized radius; it equals energy-weighted mean radius. It is not classification accuracy AUC.
+
+## Numerical Validation
+
+| Check | Maximum error |
+|---|---:|
+| fft:full_inverse | 8.52651e-14 |
+| dct:full_inverse | 8.52651e-14 |
+| haar:full_inverse | 5.68434e-14 |
+| fft:sum_disjoint_radial_bands | 8.52651e-14 |
+| dct:sum_disjoint_radial_bands | 8.52651e-14 |
+| haar:sum_all_subbands | 5.68434e-14 |
+
+## Gate-Only Frequency Shift
+
+Each seed first averages its paired change across the three bases. The table reports mean and sample standard deviation across five seeds.
+
+| Input | Gate | Frequency AUC delta | High/low log-gain |
+|---|---|---:|---:|
+| smooth_random_std1 | gelu_self | 0.0700737 +/- 0.00226205 | 0.622362 +/- 0.0229976 |
+| smooth_random_std1 | identity | 0.110623 +/- 0.00108796 | 0.878214 +/- 0.00613565 |
+| smooth_random_std1 | no_gate | -2.23291e-12 +/- 3.02665e-11 | -1.63202e-10 +/- 2.25653e-10 |
+| smooth_random_std1 | relu6_self | 0.0590163 +/- 0.00203315 | 0.53451 +/- 0.0208965 |
+| smooth_random_std1 | relu_self | 0.0590163 +/- 0.00203315 | 0.53451 +/- 0.0208965 |
+| smooth_random_std1 | smooth_clipped_static | 0.0589837 +/- 0.0020332 | 0.534224 +/- 0.0208964 |
+| smooth_random_std4 | gelu_self | 0.0624361 +/- 0.00165256 | 0.560627 +/- 0.013676 |
+| smooth_random_std4 | identity | 0.111777 +/- 0.000696289 | 0.889797 +/- 0.00608595 |
+| smooth_random_std4 | no_gate | -3.15729e-13 +/- 1.94846e-11 | 6.44457e-11 +/- 9.33107e-11 |
+| smooth_random_std4 | relu6_self | 0.0524827 +/- 0.00114872 | 0.490054 +/- 0.0100555 |
+| smooth_random_std4 | relu_self | 0.0622034 +/- 0.00165009 | 0.558618 +/- 0.0136644 |
+| smooth_random_std4 | smooth_clipped_static | 0.0524483 +/- 0.00114826 | 0.489694 +/- 0.010056 |
+
+## Cross-Basis Consistency
+
+Spearman correlations use all seed/input/gate combinations within each subject.
+
+| Subject | Metric | Pair | rho | p |
+|---|---|---|---:|---:|
+| gate_output | frequency_auc_delta | fft/dct | 0.994513 | 1.50498e-117 |
+| gate_output | frequency_auc_delta | fft/haar | 0.943986 | 1.1802e-58 |
+| gate_output | frequency_auc_delta | dct/haar | 0.933374 | 2.41081e-54 |
+| block_output | frequency_auc_delta | fft/dct | 0.997889 | 5.6099e-142 |
+| block_output | frequency_auc_delta | fft/haar | 0.986026 | 1.06164e-93 |
+| block_output | frequency_auc_delta | dct/haar | 0.98163 | 9.55354e-87 |
+| block_delta | frequency_auc_delta | fft/dct | 0.992117 | 2.70719e-108 |
+| block_delta | frequency_auc_delta | fft/haar | 0.990957 | 8.61761e-105 |
+| block_delta | frequency_auc_delta | dct/haar | 0.98545 | 1.13395e-92 |
+
+## Conclusions
+
+1. The transform and disjoint-band reconstruction audit passes numerically; the worst recorded error is 8.52651e-14.
+2. Directional agreement is quantified rather than assumed. The minimum cross-basis Spearman rho for paired frequency shift is 0.933374; method-specific values remain in consistency.csv.
+3. In the std=4 direct-gate condition, 6.68167% of values cross 6. ReLU6 reduces the frequency-AUC increase by 0.0097207 (15.6273%) relative to ReLU.
+4. The static smooth-clipped approximation tracks hard ReLU6 closely: its absolute frequency-AUC delta gap is 3.44125e-05 in that condition.
+5. The random block's pre-gate clip-crossing rate is only 0.00427246%, and the ReLU/ReLU6 residual-branch gap contracts to 3.30551e-05. Gate behavior therefore depends on the learned activation distribution.
+6. Identity self-gating (x squared) produces the largest gate-only frequency shift, while no_gate is the zero-change control. Smoothness alone does not order the results; the composite mapping and clipping both matter.
+7. Gate-output energy expansion is a spatial-feature statistic, not evidence of label relevance or model function frequency.
+
+## Limitations
+
+- Blocks are random-initialized and are used only to audit measurement behavior. No statement about ImageNet accuracy follows from these rows.
+- Haar octave bands are not geometrically identical to radial FFT/DCT bands; rank agreement is the defensible cross-basis comparison.
+- Frequency-Accuracy AUC requires a trained checkpoint and labeled evaluation set. This run reports spectral energy AUC and low-pass reconstruction AUC only.
+
+The machine-readable source tables are metrics.csv, summary.csv, paired_seed_summary.csv, paired_gate_summary.csv, distributions.csv, distribution_summary.csv, lowpass.csv, lowpass_auc.csv, reconstruction.csv, consistency.csv, and results.json.

gmnet/conclusions/local_results/20260712/e1_operator/config.yaml

@@ -0,0 +1,63 @@
+experiment_id: E1-spectral-gate-block-audit
+description: Orthogonal FFT, DCT-II, and Haar audit on controlled spatial fields.
+seeds:
+- 0
+- 1
+- 2
+- 3
+- 4
+device: cuda:0
+shape:
+  batch_size: 4
+  channels: 8
+  resolution: 64
+gates:
+- relu6_self
+- relu_self
+- gelu_self
+- identity
+- no_gate
+- smooth_clipped_static
+inputs:
+- name: smooth_random_std1
+  kind: random_field
+  target_std: 1.0
+  spectral_alpha: 3.0
+  knee: 0.15
+- name: smooth_random_std4
+  kind: random_field
+  target_std: 4.0
+  spectral_alpha: 3.0
+  knee: 0.15
+- name: white_random_std1
+  kind: random_field
+  target_std: 1.0
+  spectral_alpha: 0.0
+  knee: 0.15
+- name: tone_mix_std2
+  kind: tone_mix
+  target_std: 2.0
+spectral:
+  high_low_split: 0.5
+  haar_levels: 3
+  lowpass_cutoffs:
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+  - butterworth
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+  band_edges:
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+  - 0.125
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+  kernel_size: 7
+  layer_scale: 0.0
+  f12_bn: false
+  projection_bn: true
+  second_dw_bn: false

gmnet/conclusions/local_results/20260712/e1_operator/consistency.csv

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gmnet/conclusions/local_results/20260712/e1_operator/distribution_summary.csv

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gmnet/conclusions/local_results/20260712/e1_operator/distributions.csv

@@ -0,0 +1,121 @@
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gmnet/conclusions/local_results/20260712/e1_operator/paired_gate_summary.csv

@@ -0,0 +1,73 @@
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+4,white_random_std1,smooth_clipped_static,gate_output,3,0.00021047841829302527,0.0021304506854409465

gmnet/conclusions/local_results/20260712/e1_operator/results.json

@@ -0,0 +1,1407 @@
+{
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+}

gmnet/conclusions/local_results/20260712/e1_operator/summary.csv

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gmnet/conclusions/local_results/20260712/e1_trained/REPORT.md

@@ -0,0 +1,194 @@
+# E1 Trained CIFAR-100 Feature Audit
+
+Status: complete formal 6-gate x 3-seed matrix.
+
+## Frozen Protocol
+
+- Exactly 18 `checkpoint_last.pt` files at fixed epoch 100.
+- `checkpoint_best.pt` is excluded to avoid CIFAR-100 test-selection leakage.
+- Every run uses the same 10,000 ordered test samples; index hash `e393d61f0732b6bc8eeb99e1e9877ab128e6f0d747e946df4336f07d9364cd38`.
+- Butterworth order 4, cutoffs 0, 0.125, 0.25, 0.5, 0.75, and identity endpoint 1.
+
+## Frequency-Accuracy AUC
+
+| Gate | Seeds | Identity Top-1 | Top-1 AUC |
+|---|---:|---:|---:|
+| gelu_self | 3 | 57.04 +/- 0.474658 | 43.5246 +/- 0.327513 |
+| identity | 3 | 56.9067 +/- 0.734325 | 43.1623 +/- 0.473477 |
+| no_gate | 3 | 43.0667 +/- 0.308923 | 32.7606 +/- 0.14498 |
+| relu6_self | 3 | 57.0667 +/- 1.61856 | 43.1742 +/- 1.2365 |
+| relu_self | 3 | 57.5333 +/- 0.965729 | 43.416 +/- 0.362802 |
+| smooth_clipped_self | 3 | 57.15 +/- 1.44406 | 43.3798 +/- 0.764532 |
+
+## Accuracy Curve
+
+| Gate | Cutoff | Top-1 | Top-5 |
+|---|---:|---:|---:|
+| gelu_self | 0.0 | 1.67667 +/- 0.210792 | 7.87 +/- 0.777367 |
+| gelu_self | 0.125 | 13.91 +/- 0.52 | 32.13 +/- 0.663023 |
+| gelu_self | 0.25 | 34.9967 +/- 1.40607 | 60.96 +/- 1.30296 |
+| gelu_self | 0.5 | 55.0533 +/- 0.228108 | 79.6 +/- 0.182483 |
+| gelu_self | 0.75 | 56.9033 +/- 0.509346 | 81.0767 +/- 0.391067 |
+| gelu_self | 1.0 | 57.04 +/- 0.474658 | 81.1567 +/- 0.416813 |
+| identity | 0.0 | 1.71667 +/- 0.442757 | 7.62667 +/- 0.650103 |
+| identity | 0.125 | 12.66 +/- 0.584123 | 29.9367 +/- 0.791981 |
+| identity | 0.25 | 34.5067 +/- 0.306159 | 60.47 +/- 0.401497 |
+| identity | 0.5 | 54.7333 +/- 0.804322 | 79.7633 +/- 0.456545 |
+| identity | 0.75 | 56.8233 +/- 0.721411 | 81.1433 +/- 0.44658 |
+| identity | 1.0 | 56.9067 +/- 0.734325 | 81.21 +/- 0.481352 |
+| no_gate | 0.0 | 1.39333 +/- 0.221886 | 10.5967 +/- 0.0305505 |
+| no_gate | 0.125 | 15 +/- 0.314802 | 36.7333 +/- 0.360185 |
+| no_gate | 0.25 | 24.4767 +/- 0.453248 | 50.7 +/- 0.340441 |
+| no_gate | 0.5 | 40.4633 +/- 0.0986577 | 70.0733 +/- 0.0152753 |
+| no_gate | 0.75 | 42.84 +/- 0.311929 | 72.44 +/- 0.315119 |
+| no_gate | 1.0 | 43.0667 +/- 0.308923 | 72.58 +/- 0.29 |
+| relu6_self | 0.0 | 1.82333 +/- 0.0416333 | 8.23667 +/- 0.612726 |
+| relu6_self | 0.125 | 12.2667 +/- 0.558241 | 29.7133 +/- 0.523864 |
+| relu6_self | 0.25 | 34.3167 +/- 1.77094 | 60.8033 +/- 1.91756 |
+| relu6_self | 0.5 | 54.9 +/- 1.39072 | 80.4133 +/- 0.986729 |
+| relu6_self | 0.75 | 56.9367 +/- 1.60724 | 81.8733 +/- 0.957096 |
+| relu6_self | 1.0 | 57.0667 +/- 1.61856 | 81.9167 +/- 0.935432 |
+| relu_self | 0.0 | 1.48 +/- 0.0519615 | 7.77 +/- 0.28 |
+| relu_self | 0.125 | 12.7 +/- 0.3005 | 30.8367 +/- 0.960902 |
+| relu_self | 0.25 | 33.6233 +/- 0.803264 | 60.43 +/- 0.419047 |
+| relu_self | 0.5 | 55.51 +/- 0.497293 | 81.0567 +/- 0.392726 |
+| relu_self | 0.75 | 57.45 +/- 1.0095 | 82.5667 +/- 0.382143 |
+| relu_self | 1.0 | 57.5333 +/- 0.965729 | 82.6733 +/- 0.356978 |
+| smooth_clipped_self | 0.0 | 1.71 +/- 0.160935 | 7.86333 +/- 0.306649 |
+| smooth_clipped_self | 0.125 | 12.72 +/- 1.43377 | 30.3967 +/- 2.46812 |
+| smooth_clipped_self | 0.25 | 34.7867 +/- 0.273008 | 61.3467 +/- 0.338575 |
+| smooth_clipped_self | 0.5 | 55.0433 +/- 1.25516 | 80.8567 +/- 0.637913 |
+| smooth_clipped_self | 0.75 | 57.0233 +/- 1.35648 | 82.2133 +/- 0.455009 |
+| smooth_clipped_self | 1.0 | 57.15 +/- 1.44406 | 82.2933 +/- 0.529748 |
+
+## Identity-Input Feature PSD
+
+| Gate | Layer | Size | Centroid | High/low | Entropy |
+|---|---|---|---:|---:|---:|
+| gelu_self | input | 32x32 | 0.126286 | 0.0253251 | 0.576297 |
+| gelu_self | stage1 | 8x8 | 0.31174 | 0.221731 | 0.693987 |
+| gelu_self | stage2 | 4x4 | 0.446954 | 0.537094 | 0.568791 |
+| gelu_self | stage3 | 2x2 | 0.789987 | NA | 0.542176 |
+| gelu_self | stage4 | 1x1 | NA | NA | NA |
+| gelu_self | pre_classifier | 1x1 | NA | NA | NA |
+| identity | input | 32x32 | 0.126286 | 0.0253251 | 0.576297 |
+| identity | stage1 | 8x8 | 0.308302 | 0.203921 | 0.689831 |
+| identity | stage2 | 4x4 | 0.453024 | 0.59963 | 0.591995 |
+| identity | stage3 | 2x2 | 0.778494 | NA | 0.499558 |
+| identity | stage4 | 1x1 | NA | NA | NA |
+| identity | pre_classifier | 1x1 | NA | NA | NA |
+| no_gate | input | 32x32 | 0.126286 | 0.0253251 | 0.576297 |
+| no_gate | stage1 | 8x8 | 0.362828 | 0.415815 | 0.7792 |
+| no_gate | stage2 | 4x4 | 0.488939 | 0.86083 | 0.688396 |
+| no_gate | stage3 | 2x2 | 0.775782 | NA | 0.495683 |
+| no_gate | stage4 | 1x1 | NA | NA | NA |
+| no_gate | pre_classifier | 1x1 | NA | NA | NA |
+| relu6_self | input | 32x32 | 0.126286 | 0.0253251 | 0.576297 |
+| relu6_self | stage1 | 8x8 | 0.303656 | 0.197728 | 0.676901 |
+| relu6_self | stage2 | 4x4 | 0.45412 | 0.591928 | 0.590483 |
+| relu6_self | stage3 | 2x2 | 0.793015 | NA | 0.55056 |
+| relu6_self | stage4 | 1x1 | NA | NA | NA |
+| relu6_self | pre_classifier | 1x1 | NA | NA | NA |
+| relu_self | input | 32x32 | 0.126286 | 0.0253251 | 0.576297 |
+| relu_self | stage1 | 8x8 | 0.300957 | 0.186726 | 0.672499 |
+| relu_self | stage2 | 4x4 | 0.448387 | 0.552181 | 0.574091 |
+| relu_self | stage3 | 2x2 | 0.795018 | NA | 0.556111 |
+| relu_self | stage4 | 1x1 | NA | NA | NA |
+| relu_self | pre_classifier | 1x1 | NA | NA | NA |
+| smooth_clipped_self | input | 32x32 | 0.126286 | 0.0253251 | 0.576297 |
+| smooth_clipped_self | stage1 | 8x8 | 0.301045 | 0.187613 | 0.670554 |
+| smooth_clipped_self | stage2 | 4x4 | 0.449272 | 0.562829 | 0.574149 |
+| smooth_clipped_self | stage3 | 2x2 | 0.79281 | NA | 0.549814 |
+| smooth_clipped_self | stage4 | 1x1 | NA | NA | NA |
+| smooth_clipped_self | pre_classifier | 1x1 | NA | NA | NA |
+
+## Cutoff 0.25 Feature Transfer
+
+| Gate | Layer | Centroid delta | Entropy delta |
+|---|---|---:|---:|
+| gelu_self | input | -0.0393482 | -0.154839 |
+| gelu_self | stage1 | -0.00529682 | -0.0124451 |
+| gelu_self | stage2 | -0.00122154 | -0.00432722 |
+| gelu_self | stage3 | 0.000370816 | 0.00109249 |
+| gelu_self | stage4 | NA | NA |
+| gelu_self | pre_classifier | NA | NA |
+| identity | input | -0.0393482 | -0.154839 |
+| identity | stage1 | -0.00633309 | -0.0150772 |
+| identity | stage2 | -0.000902526 | -0.00295943 |
+| identity | stage3 | 0.0319227 | -0.318292 |
+| identity | stage4 | NA | NA |
+| identity | pre_classifier | NA | NA |
+| no_gate | input | -0.0393482 | -0.154839 |
+| no_gate | stage1 | -0.0128938 | -0.0217263 |
+| no_gate | stage2 | -0.00595234 | -0.0159898 |
+| no_gate | stage3 | -0.00242431 | -0.00907469 |
+| no_gate | stage4 | NA | NA |
+| no_gate | pre_classifier | NA | NA |
+| relu6_self | input | -0.0393482 | -0.154839 |
+| relu6_self | stage1 | -0.00641732 | -0.0152166 |
+| relu6_self | stage2 | -0.00172978 | -0.00542685 |
+| relu6_self | stage3 | -0.000394625 | -0.000996186 |
+| relu6_self | stage4 | NA | NA |
+| relu6_self | pre_classifier | NA | NA |
+| relu_self | input | -0.0393482 | -0.154839 |
+| relu_self | stage1 | -0.0054136 | -0.0130357 |
+| relu_self | stage2 | -0.000585044 | -0.00166278 |
+| relu_self | stage3 | 0.00201187 | 0.00511652 |
+| relu_self | stage4 | NA | NA |
+| relu_self | pre_classifier | NA | NA |
+| smooth_clipped_self | input | -0.0393482 | -0.154839 |
+| smooth_clipped_self | stage1 | -0.0069377 | -0.0173007 |
+| smooth_clipped_self | stage2 | -0.00116242 | -0.00444759 |
+| smooth_clipped_self | stage3 | 0.00147762 | 0.00424477 |
+| smooth_clipped_self | stage4 | NA | NA |
+| smooth_clipped_self | pre_classifier | NA | NA |
+
+## Identity-Input Gate Regions
+
+| Gate | Stage | Negative | Active [0,6) | Above 6 | Clip mean | Actual clip crossing |
+|---|---|---:|---:|---:|---:|---:|
+| gelu_self | stage1 | 0.514026 | 0.485974 | 2.17014e-09 | NA | NA |
+| gelu_self | stage2 | 0.710207 | 0.289793 | 0 | NA | NA |
+| gelu_self | stage3 | 0.633088 | 0.366912 | 0 | NA | NA |
+| gelu_self | stage4 | 0.52144 | 0.47856 | 0 | NA | NA |
+| identity | stage1 | 0.500977 | 0.498975 | 4.727e-05 | NA | NA |
+| identity | stage2 | 0.50451 | 0.49549 | 2.99479e-07 | NA | NA |
+| identity | stage3 | 0.501049 | 0.498947 | 3.21875e-06 | NA | NA |
+| identity | stage4 | 0.502376 | 0.497608 | 1.61979e-05 | NA | NA |
+| no_gate | stage1 | 0.50166 | 0.49834 | 0 | NA | NA |
+| no_gate | stage2 | 0.511161 | 0.488839 | 0 | NA | NA |
+| no_gate | stage3 | 0.5 | 0.5 | 0 | NA | NA |
+| no_gate | stage4 | 0.502083 | 0.497917 | 0 | NA | NA |
+| relu6_self | stage1 | 0.54704 | 0.448658 | 0.00430189 | 6 | 0.00430189 |
+| relu6_self | stage2 | 0.650293 | 0.349706 | 6.46701e-07 | 6 | 6.46701e-07 |
+| relu6_self | stage3 | 0.572158 | 0.427842 | 0 | 6 | 0 |
+| relu6_self | stage4 | 0.509875 | 0.490125 | 0 | 6 | 0 |
+| relu_self | stage1 | 0.530347 | 0.469501 | 0.000151799 | NA | NA |
+| relu_self | stage2 | 0.656495 | 0.343504 | 4.12326e-07 | NA | NA |
+| relu_self | stage3 | 0.559396 | 0.440604 | 0 | NA | NA |
+| relu_self | stage4 | 0.503299 | 0.496701 | 0 | NA | NA |
+| smooth_clipped_self | stage1 | 0.698753 | 0.301167 | 8.06901e-05 | 1.19116 | 0.0563794 |
+| smooth_clipped_self | stage2 | 0.757515 | 0.242485 | 0 | 1.2111 | 0.0051715 |
+| smooth_clipped_self | stage3 | 0.581153 | 0.418847 | 0 | 0.950191 | 0.00313891 |
+| smooth_clipped_self | stage4 | 0.532625 | 0.467375 | 0 | 0.806449 | 0.00486513 |
+
+## Nonfinite Diagnostics
+
+- Nonfinite numeric cells: 36.
+- Affected table observations: 6.
+- Aggregates exclude nonfinite values. If no finite observation remains, the statistic is JSON null and report NA; values are never replaced by zero.
+
+| Table | Gate | Seed | Cutoff | Layer | Nonfinite fields |
+|---|---|---:|---:|---|---|
+| feature_metrics | identity | 0 | 0.125 | stage3 | centroid_delta_vs_identity;high_energy_fraction;low_energy_fraction;mean_energy_per_map;spectral_centroid;total_energy |
+| feature_metrics | identity | 0 | 0.5 | stage3 | centroid_delta_vs_identity;high_energy_fraction;low_energy_fraction;mean_energy_per_map;spectral_centroid;total_energy |
+| feature_metrics | identity | 0 | 0.75 | stage3 | centroid_delta_vs_identity;high_energy_fraction;low_energy_fraction;mean_energy_per_map;spectral_centroid;total_energy |
+| feature_metrics | identity | 1 | 0.25 | stage3 | centroid_delta_vs_identity;high_energy_fraction;low_energy_fraction;mean_energy_per_map;spectral_centroid;total_energy |
+| feature_metrics | identity | 1 | 0.5 | stage3 | centroid_delta_vs_identity;high_energy_fraction;low_energy_fraction;mean_energy_per_map;spectral_centroid;total_energy |
+| feature_metrics | identity | 2 | 0.25 | stage3 | centroid_delta_vs_identity;high_energy_fraction;low_energy_fraction;mean_energy_per_map;spectral_centroid;total_energy |
+
+## Interpretation Limits
+
+- Stage3 is 2x2 and has no non-DC FFT cell below normalized radius 0.5; its high/low ratio is NA.
+- Stage4 and pre_classifier are 1x1, so their spatial PSD values are NA.
+- Frequency-Accuracy AUC measures causal sensitivity to this input filter protocol, not model function frequency.

gmnet/conclusions/local_results/20260712/e1_trained/accuracy_auc.csv

@@ -0,0 +1,19 @@
+gate,seed,frequency_accuracy_auc_top1,frequency_accuracy_auc_top5,identity_top1,identity_top5,dc_top1,cutoff_count
+gelu_self,0,43.85375,66.52687499999999,57.15,81.07,1.91,6
+gelu_self,1,43.198750000000004,65.923125,56.52,80.79,1.5,6
+gelu_self,2,43.521249999999995,66.30562499999999,57.45,81.61,1.62,6
+identity,0,42.615625,65.621875,56.06,80.69,2.07,6
+identity,1,43.4425,66.17999999999999,57.37,81.64,1.22,6
+identity,2,43.428749999999994,66.0025,57.29,81.3,1.86,6
+no_gate,0,32.855625,59.36375,43.24,72.29,1.21,6
+no_gate,1,32.59375,59.48375,42.71,72.58,1.64,6
+no_gate,2,32.832499999999996,59.535625,43.25,72.87,1.33,6
+relu6_self,0,41.796875,65.36375000000001,55.2,80.84,1.81,6
+relu6_self,1,44.18875,67.24000000000001,58.08,82.53,1.79,6
+relu6_self,2,43.536875,66.71875,57.92,82.38,1.87,6
+relu_self,0,43.340625,67.0375,57.16,82.63,1.54,6
+relu_self,1,43.810625,67.10125,58.63,83.05,1.45,6
+relu_self,2,43.096875,66.59375,56.81,82.34,1.45,6
+smooth_clipped_self,0,42.821875,66.82375,55.86,81.85,1.89,6
+smooth_clipped_self,1,43.066250000000004,66.515625,56.88,82.15,1.66,6
+smooth_clipped_self,2,44.25125,67.20375,58.71,82.88,1.58,6

gmnet/conclusions/local_results/20260712/e1_trained/accuracy_auc_summary.csv

@@ -0,0 +1,7 @@
+gate,count,identity_top1_finite_count,identity_top1_nonfinite_count,identity_top1_missing_count,identity_top1_mean,identity_top1_std,identity_top5_finite_count,identity_top5_nonfinite_count,identity_top5_missing_count,identity_top5_mean,identity_top5_std,frequency_accuracy_auc_top1_finite_count,frequency_accuracy_auc_top1_nonfinite_count,frequency_accuracy_auc_top1_missing_count,frequency_accuracy_auc_top1_mean,frequency_accuracy_auc_top1_std,frequency_accuracy_auc_top5_finite_count,frequency_accuracy_auc_top5_nonfinite_count,frequency_accuracy_auc_top5_missing_count,frequency_accuracy_auc_top5_mean,frequency_accuracy_auc_top5_std
+gelu_self,3,3,0,0,57.04,0.4746577714522321,3,0,0,81.15666666666668,0.4168133075290799,3,0,0,43.52458333333334,0.3275127223991938,3,0,0,66.251875,0.30544281379662075
+identity,3,3,0,0,56.906666666666666,0.7343250869562675,3,0,0,81.21,0.4813522618623508,3,0,0,43.16229166666667,0.47347713667962005,3,0,0,65.93479166666667,0.28515643550221775
+no_gate,3,3,0,0,43.06666666666666,0.30892285984260437,3,0,0,72.58,0.28999999999999915,3,0,0,32.760625,0.14497979385072957,3,0,0,59.46104166666667,0.08815897690157977
+relu6_self,3,3,0,0,57.06666666666666,1.6185590299193071,3,0,0,81.91666666666667,0.9354321639399236,3,0,0,43.17416666666667,1.2365009119318646,3,0,0,66.44083333333334,0.9685074965292368
+relu_self,3,3,0,0,57.53333333333333,0.9657294307068288,3,0,0,82.67333333333333,0.35697805721546993,3,0,0,43.41604166666667,0.3628023164663295,3,0,0,66.91083333333333,0.2764460088938368
+smooth_clipped_self,3,3,0,0,57.150000000000006,1.4440567855870494,3,0,0,82.29333333333334,0.5297483679383377,3,0,0,43.37979166666667,0.7645322133228475,3,0,0,66.84770833333334,0.3446875472197002

gmnet/conclusions/local_results/20260712/e1_trained/accuracy_curve.csv

@@ -0,0 +1,109 @@
+gate,seed,run_name,checkpoint_epochs_completed,cutoff,filter,samples,top1,top5
+gelu_self,0,e3_c100_s1_gelu_seed0,100,0.0,dc_only,10000,1.91,7.94
+gelu_self,0,e3_c100_s1_gelu_seed0,100,0.125,butterworth,10000,14.19,32.59
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+gelu_self,0,e3_c100_s1_gelu_seed0,100,0.5,butterworth,10000,55.01,79.39
+gelu_self,0,e3_c100_s1_gelu_seed0,100,0.75,butterworth,10000,56.98,81.11
+gelu_self,0,e3_c100_s1_gelu_seed0,100,1.0,identity,10000,57.15,81.07
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+gelu_self,1,e3_c100_s1_gelu_seed1,100,0.125,butterworth,10000,13.31,31.37
+gelu_self,1,e3_c100_s1_gelu_seed1,100,0.25,butterworth,10000,35.06,60.65
+gelu_self,1,e3_c100_s1_gelu_seed1,100,0.5,butterworth,10000,54.85,79.69
+gelu_self,1,e3_c100_s1_gelu_seed1,100,0.75,butterworth,10000,56.36,80.67
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+gelu_self,2,e3_c100_s1_gelu_seed2,100,0.0,dc_only,10000,1.62,8.61
+gelu_self,2,e3_c100_s1_gelu_seed2,100,0.125,butterworth,10000,14.23,32.43
+gelu_self,2,e3_c100_s1_gelu_seed2,100,0.25,butterworth,10000,33.56,59.84
+gelu_self,2,e3_c100_s1_gelu_seed2,100,0.5,butterworth,10000,55.3,79.72
+gelu_self,2,e3_c100_s1_gelu_seed2,100,0.75,butterworth,10000,57.37,81.45
+gelu_self,2,e3_c100_s1_gelu_seed2,100,1.0,identity,10000,57.45,81.61
+identity,0,e3_c100_s1_identity_seed0,100,0.0,dc_only,10000,2.07,8.12
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+identity,0,e3_c100_s1_identity_seed0,100,0.25,butterworth,10000,34.34,60.65
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+identity,1,e3_c100_s1_identity_seed1,100,0.125,butterworth,10000,13.28,30.82
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+identity,1,e3_c100_s1_identity_seed1,100,0.5,butterworth,10000,54.96,80.24
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+identity,2,e3_c100_s1_identity_seed2,100,0.75,butterworth,10000,57.0,81.26
+identity,2,e3_c100_s1_identity_seed2,100,1.0,identity,10000,57.29,81.3
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+no_gate,2,e3_c100_s1_no_gate_seed2,100,0.5,butterworth,10000,40.53,70.06
+no_gate,2,e3_c100_s1_no_gate_seed2,100,0.75,butterworth,10000,43.01,72.75
+no_gate,2,e3_c100_s1_no_gate_seed2,100,1.0,identity,10000,43.25,72.87
+relu6_self,0,e3_c100_s1_relu6_seed0,100,0.0,dc_only,10000,1.81,7.66
+relu6_self,0,e3_c100_s1_relu6_seed0,100,0.125,butterworth,10000,11.91,29.23
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gmnet/conclusions/local_results/20260712/e1_trained/accuracy_summary.csv

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gmnet/conclusions/local_results/20260712/e1_trained/checkpoint_manifest.csv

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gmnet/conclusions/local_results/20260712/e1_trained/feature_summary.csv

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gmnet/conclusions/local_results/20260712/e1_trained/feature_transfer_summary.csv

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gmnet/conclusions/local_results/20260712/e1_trained/gate_seed_summary.csv

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gmnet/conclusions/local_results/20260712/e1_trained/gate_summary.csv

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gmnet/conclusions/local_results/20260712/e1_trained/nonfinite_cells.csv

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gmnet/conclusions/local_results/20260712/e1_trained/nonfinite_summary.csv

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gmnet/conclusions/local_results/20260712/e1_trained/results.json

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+  }
+}

gmnet/conclusions/local_results/20260712/e4_cifar100/AGGREGATE.md

@@ -0,0 +1,13 @@
+# E4 Three-Seed Aggregate
+
+Frozen-weight interventions on the full CIFAR-100 test set. Values are mean +/- sample standard deviation across seed checkpoints.
+
+| Mode | Top-1 (%) | Top-5 (%) | NLL | ECE (%) |
+|---|---:|---:|---:|---:|
+| baseline | 57.073 +/- 1.624 | 81.917 +/- 0.935 | 2.1970 +/- 0.1071 | 21.431 +/- 0.929 |
+| batch_shuffle | 2.017 +/- 0.186 | 8.850 +/- 0.847 | 31.1469 +/- 8.3466 | 83.728 +/- 1.967 |
+| channel_shuffle | 2.173 +/- 0.207 | 8.930 +/- 0.646 | 19.8468 +/- 2.1090 | 74.511 +/- 1.450 |
+| mean_gate | 3.007 +/- 0.811 | 13.123 +/- 1.517 | 13.0528 +/- 1.8467 | 65.728 +/- 12.719 |
+| spatial_shuffle | 2.520 +/- 1.018 | 10.010 +/- 2.833 | 48.2429 +/- 19.5383 | 84.070 +/- 6.033 |
+
+This is a frozen-checkpoint diagnostic, not matched retraining.

gmnet/conclusions/local_results/20260712/e4_cifar100/aggregate.json

@@ -0,0 +1,330 @@
+{
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+  "schema_version": 1,
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+  "seeds": [
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+    2
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+  "source_results": [
+    "/tmp/gmnet_runs/e4_cifar100_relu6_official/seed0/results.json",
+    "/tmp/gmnet_runs/e4_cifar100_relu6_official/seed1/results.json",
+    "/tmp/gmnet_runs/e4_cifar100_relu6_official/seed2/results.json"
+  ],
+  "status": "aggregated_full_validation_seed_results"
+}

gmnet/conclusions/local_results/20260712/e8_imagenet_partial/e6_metrics.csv

@@ -0,0 +1 @@
+condition,samples,top1,top5,nll,ece

gmnet/conclusions/local_results/20260712/e8_imagenet_partial/e8_metrics.csv

@@ -0,0 +1,7 @@
+condition,samples,top1,top5,nll,ece
+clean,1000,77.2,94.1,1.0859487056732178,16.648103338852525
+pgd_linf,1000,0.0,0.0,35.463584899902344,91.92493071444335
+pgd_band_low,1000,0.3,2.1,17.212669372558594,83.36780958827586
+pgd_band_mid,1000,0.0,0.0,30.186555862426758,91.00427110753954
+pgd_band_high,1000,0.0,0.0,31.903894424438477,91.4560706783086
+worst_band,1000,0.0,0.0,34.70122146606445,92.6302170559764

gmnet/conclusions/local_results/20260712/e8_imagenet_partial/results.json

@@ -0,0 +1,201 @@
+{
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+    "compat_model_kwargs": {
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+    "parameter_count": 7822744,
+    "path": "/nfs/ywang29/GmNet/gmnet_s3.npy",
+    "selected_topology": "historical_full_bn_topology",
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+    "PGD-10 is not AutoAttack and is not claimed to be attack-complete.",
+    "The checkpoint uses the strictly matching historical full-BN topology, not the frozen paper-default topology."
+  ],
+  "corruption_specs": {
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+        1.0
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+    "worst_band_retention": 0.0,
+    "worst_band_selection": "per-sample lexicographic top5 failure, top1 failure, then maximum NLL across low/mid/high attacks"
+  },
+  "historical_full_bn_checkpoint": true,
+  "metadata": {
+    "config": "/nfs/ywang29/GmNet/journal_exp/configs/e6_e8/imagenet_partial.yaml",
+    "data_root": "/tmp/gmnet_data/imagenet-1k-val/val",
+    "device": "cuda:0",
+    "duration_seconds": 101.417071,
+    "finished_at_utc": "2026-07-12T22:10:03.237289+00:00",
+    "python": "3.12.10",
+    "started_at_utc": "2026-07-12T22:08:21.820218+00:00",
+    "torch": "2.9.0+cu130"
+  },
+  "not_autoattack": true,
+  "partial_protocol": true,
+  "protocol_version": "e6-e8-imagenet-partial-v1",
+  "smoke_truncation": false,
+  "subset": {
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+    "e8_samples": 1000,
+    "seed": 250322841,
+    "selection": "per-class-sha256"
+  }
+}

gmnet/conclusions/local_results/20260712/e8_imagenet_partial/run.log

@@ -0,0 +1,7 @@
+E8 clean {"ece": 16.648103338852525, "nll": 1.0859487056732178, "samples": 1000, "top1": 77.2, "top5": 94.1}
+E8 pgd_linf {"ece": 91.92493071444335, "nll": 35.463584899902344, "samples": 1000, "top1": 0.0, "top5": 0.0}
+E8 pgd_band_low {"ece": 83.36780958827586, "nll": 17.212669372558594, "samples": 1000, "top1": 0.3, "top5": 2.1}
+E8 pgd_band_mid {"ece": 91.00427110753954, "nll": 30.186555862426758, "samples": 1000, "top1": 0.0, "top5": 0.0}
+E8 pgd_band_high {"ece": 91.4560706783086, "nll": 31.903894424438477, "samples": 1000, "top1": 0.0, "top5": 0.0}
+E8 worst_band {"ece": 92.6302170559764, "nll": 34.70122146606445, "samples": 1000, "top1": 0.0, "top5": 0.0}
+/tmp/gmnet_runs/e6_e8_imagenet_partial/e8

gmnet/conclusions/local_results/20260712/legacy_imagenet_clean/RESULTS.md

@@ -0,0 +1,17 @@
+# E4 Frozen Gate Intervention Audit
+
+## Scope
+
+- Checkpoint: `/nfs/ywang29/GmNet/gmnet_s3.npy` (`ed974e2e8ffe7f96c51187f08a519478ab345c7f3972efea768e445ef13f7377`).
+- Selected topology: `historical_full_bn_topology`.
+- Data: deterministic random 50000-image subset of 50000 imagenet validation images.
+- Data source: `s3://snap-research-cv-code/ywang29/datasets/imagenet-1k/val` via `local_staging_cache` at `/tmp/gmnet_data/imagenet-1k-val/val`.
+- This is a frozen-checkpoint sensitivity diagnostic. It is not a matched-retraining causal accuracy result.
+
+## Results
+
+| Intervention | Top-1 (%) | Top-5 (%) | NLL | ECE (%) | Baseline agreement (%) | KL from baseline | Reference Pearson |
+|---|---:|---:|---:|---:|---:|---:|---:|
+| baseline | 76.292 | 93.222 | 1.11211 | 15.836 | 100.000 | 0.000000 | 1.0 |
+
+`stop_gradient` is intentionally excluded: it is forward-identical during inference and can only be evaluated through retraining. `mean_gate` uses the per-channel mean gate value over batch and spatial dimensions at every block.

gmnet/conclusions/local_results/20260712/legacy_imagenet_clean/results.json

@@ -0,0 +1,191 @@
+{
+  "checkpoint": {
+    "claim_boundary": "The checkpoint is not compatible with the frozen paper-default topology. It is used only with the exactly matching historical full-BN topology.",
+    "compat_model_kwargs": {
+      "f12_bn": true,
+      "projection_bn": true,
+      "second_dw_bn": true
+    },
+    "format": "numpy_object_state_dict",
+    "historical_full_bn_topology": {
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+      "missing_key_count": 0,
+      "missing_key_sample": [],
+      "model_key_count": 535,
+      "shape_mismatch_count": 0,
+      "shape_mismatch_sample": [],
+      "strict_compatible": true,
+      "unexpected_key_count": 0,
+      "unexpected_key_sample": []
+    },
+    "paper_default_topology": {
+      "checkpoint_key_count": 535,
+      "missing_key_count": 0,
+      "missing_key_sample": [],
+      "model_key_count": 365,
+      "shape_mismatch_count": 0,
+      "shape_mismatch_sample": [],
+      "strict_compatible": false,
+      "unexpected_key_count": 170,
+      "unexpected_key_sample": [
+        "stages.0.1.dwconv2.bn.bias",
+        "stages.0.1.dwconv2.bn.num_batches_tracked",
+        "stages.0.1.dwconv2.bn.running_mean",
+        "stages.0.1.dwconv2.bn.running_var",
+        "stages.0.1.dwconv2.bn.weight",
+        "stages.0.1.f1.bn.bias",
+        "stages.0.1.f1.bn.num_batches_tracked",
+        "stages.0.1.f1.bn.running_mean",
+        "stages.0.1.f1.bn.running_var",
+        "stages.0.1.f1.bn.weight"
+      ]
+    },
+    "parameter_count": 7822744,
+    "path": "/nfs/ywang29/GmNet/gmnet_s3.npy",
+    "selected_topology": "historical_full_bn_topology",
+    "sha256": "ed974e2e8ffe7f96c51187f08a519478ab345c7f3972efea768e445ef13f7377"
+  },
+  "claim_scope": "Frozen-checkpoint forward intervention only. Accuracy differences do not establish matched-retraining causality.",
+  "data": {
+    "class_count": 1000,
+    "dataset": "imagenet",
+    "evaluated_samples": 50000,
+    "full_validation_samples": 50000,
+    "preprocess": "resize256_bicubic_centercrop224_imagenet_normalization",
+    "provenance": {
+      "access_mode": "local_staging_cache",
+      "access_path": "/tmp/gmnet_data/imagenet-1k-val/val",
+      "source_uri": "s3://snap-research-cv-code/ywang29/datasets/imagenet-1k/val",
+      "staging_manifest": {
+        "classes": 1000,
+        "images": 50000,
+        "method": "tar_plus_s3_class_mapping",
+        "source": "/s3-code/ywang29/datasets/imagenet-1k/val"
+      },
+      "staging_manifest_path": "/tmp/gmnet_data/imagenet-1k-val/.READY",
+      "staging_manifest_sha256": "033454c11cbd7d7454eb93e18794648ae2cd8849d66fc841a2c866832fde927b"
+    },
+    "root": "/tmp/gmnet_data/imagenet-1k-val/val",
+    "subset_indices_sha256": "20d955083da5e4d754ae1b14007c11708069624d60d6177d37831503bd7fc543",
+    "subset_method": "full_validation_set_in_canonical_order",
+    "subset_seed": 20260712
+  },
+  "device": "cuda:0",
+  "elapsed_seconds": 27.856019735336304,
+  "experiment": "E4",
+  "installed_gate_count": 17,
+  "intervention_definitions": {
+    "batch_shuffle": "fixed seeded batch permutation per gate block",
+    "channel_shuffle": "fixed seeded channel permutation per gate block",
+    "mean_gate": "per-channel gate-value mean over batch and spatial dimensions",
+    "spatial_shuffle": "fixed seeded spatial permutation per gate block",
+    "stop_gradient": "training-only; deliberately not evaluated"
+  },
+  "intervention_scope": "all_self_gate_blocks_jointly",
+  "interventions": {
+    "baseline": {
+      "ece_percent": 15.836061141088607,
+      "intervention_reference_coherence": {
+        "mean_gate_input_pearson": 1.0,
+        "measured_gate_count": 17,
+        "per_gate": [
+          {
+            "gate_index": 0,
+            "pearson": 1.0,
+            "sampled_values": 65536
+          },
+          {
+            "gate_index": 1,
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+          {
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+          {
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+          {
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+          {
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+          {
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+          {
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+          {
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+          {
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+          {
+            "gate_index": 16,
+            "pearson": 1.0,
+            "sampled_values": 65536
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+        ]
+      },
+      "logit_rmse_from_baseline": 0.0,
+      "mean_kl_from_baseline": 0.0,
+      "nll": 1.1121102255249022,
+      "prediction_agreement_with_baseline_percent": 100.0,
+      "samples": 50000,
+      "top1_percent": 76.292,
+      "top5_percent": 93.222
+    }
+  },
+  "schema_version": 1,
+  "status": "completed_frozen_checkpoint_diagnostic",
+  "torch_version": "2.9.0+cu130"
+}

gmnet/conclusions/local_results/e4_mechanism_followup_20260718/REPORT.md

@@ -0,0 +1,97 @@
+# GmNet ImageNet Mechanism Evidence
+
+Frozen on 2026-07-18 after certificate-validated fixed-last evaluation.
+All mechanism comparisons are seed-0 descriptive effect sizes.
+
+## Artifact Acceptance
+
+All listed runs have a COMPLETE marker, a passed official certificate,
+50,000 validation samples, a fixed-final checkpoint, finite metrics, and
+complete step accounting. Mechanism arms use 8 GPUs, 300 epochs, and
+187,500 optimizer steps.
+
+## Mechanism Panel
+
+| Arm | Top-1 | Delta | Top-5 | Delta | NLL | ECE |
+|---|---:|---:|---:|---:|---:|---:|
+| B: Current-code ReLU6 baseline | 78.798 | 0.000 | 94.400 | 0.000 | 1.064004 | 19.735691 |
+| H: Historical paper-recipe ReLU6 | 78.746 | -0.052 | 94.380 | -0.020 | 1.081215 | 20.657784 |
+| S: Stop-gradient | 76.774 | -2.024 | 93.358 | -1.042 | 1.138777 | 18.896074 |
+| C: Channel derangement | 77.794 | -1.004 | 93.906 | -0.494 | 1.120781 | 20.019571 |
+| D: Batch derangement | 54.362 | -24.436 | 77.340 | -17.060 | 2.326874 | 20.164200 |
+| SC: Stop-gradient + channel derangement | 69.508 | -9.290 | 88.894 | -5.506 | 1.501795 | 18.994035 |
+
+The current-code baseline differs from the historical baseline by +0.052 Top-1 points.
+
+## Descriptive Combined Interaction
+
+The raw-scale definition is `prediction = S + C - B` and `interaction = SC - prediction`.
+
+| Metric | Additive prediction | Observed SC | Interaction |
+|---|---:|---:|---:|
+| top1 | 75.770000 | 69.508000 | -6.262000 |
+| top5 | 92.864000 | 88.894000 | -3.970000 |
+| nll | 1.195555 | 1.501795 | 0.306240 |
+| ece | 19.179954 | 18.994035 | -0.185919 |
+
+Top-1 interaction is descriptive, not a population-level factorial test.
+
+## Gate Operating Regime
+
+Coherence is the mean of 17 block-level Pearson proxies computed on the
+first canonical validation sample. Region fractions are element-weighted
+over all 50,000 validation images.
+
+| Arm | Coherence proxy | Negative | Active [0,6) | Clip crossing |
+|---|---:|---:|---:|---:|
+| B | 1.0000 | 58.630% | 40.698% | 0.6725% |
+| S | 1.0000 | 62.649% | 37.340% | 0.0105% |
+| C | 0.1304 | 52.064% | 47.761% | 0.1754% |
+| D | 0.7627 | 81.001% | 16.378% | 2.6213% |
+| SC | -0.0007 | 50.847% | 48.465% | 0.6883% |
+
+Batch derangement learns a high cross-sample coherence proxy but shifts
+most values into the negative region and remains 24.436 Top-1 points
+below baseline. This is partial adaptation to shared/marginal gate
+statistics, not preservation of same-sample modulation.
+
+Channel derangement alone retains 77.794 Top-1 and develops increasing
+deep-stage coherence. The combined arm uses the identical channel
+permutation, keeps coherence near zero, and falls to 69.508. This supports
+the gate-gradient path as part of adaptation to a fixed mismatch.
+
+## Gate Shape And Clipping Context
+
+| Arm | Top-1 | Delta vs H | Top-5 | NLL | ECE |
+|---|---:|---:|---:|---:|---:|
+| H: Historical paper-recipe ReLU6 | 78.746 | 0.000 | 94.380 | 1.081215 | 20.657784 |
+| RELU: ReLU self-gate | 78.568 | -0.178 | 94.360 | 1.076494 | 20.249924 |
+| GELU: GELU self-gate | 78.664 | -0.082 | 94.346 | 1.078274 | 20.152088 |
+| LS: Learned smooth cap | 78.670 | -0.076 | 94.440 | 1.067423 | 19.791915 |
+| F6: Fixed smooth cap c=6 | 78.798 | 0.052 | 94.328 | 1.083846 | 20.786922 |
+| A: ReLU6 only (no self-multiplication) | 78.122 | -0.624 | 94.012 | 1.133459 | 21.133415 |
+| N: No complete gated operator | 14.376 | -64.370 | 30.154 | 5.090746 | 9.589707 |
+| R: Release-paper-BN recipe audit | 79.158 | 0.412 | 94.500 | 1.040718 | 18.908918 |
+
+ReLU, GELU, learned smooth, and fixed-c6 variants remain close to the
+historical ReLU6 result at seed 0. Clip-crossing frequency is not monotonic
+with accuracy. Clipping is therefore supported as an operating-range and
+spectral-spill control, not as a uniquely accuracy-improving activation.
+The release-paper-BN row is a different 310-epoch recipe and is context only.
+
+## Frozen Claim
+
+> GmNet does not benefit from indiscriminate high-frequency amplification.
+> Its self-gate performs input-conditioned multiplicative spectral mixing,
+> whose task value depends on sample-conditioned carrier-gate coupling and
+> an optimizable gate-gradient path. Exact channel correspondence is helpful
+> but adaptable. Clipping bounds the operating range and spectral spill
+> rather than uniquely determining clean accuracy.
+
+## Claim Boundaries
+
+- These are independently trained single-seed effect sizes; no significance, equivalence, or population-level interaction is claimed.
+- Batch derangement changes the forward donor, cross-sample gradients, and batch/order dependence. It is not a pure forward-alignment intervention.
+- Coherence is a one-sample-per-block proxy, not a full-dataset semantic-alignment measure or a causal mediator.
+- Historical B/S/C launch-time provenance is not identical to the current batch, although certificates pass and B reproduces H within 0.052 points.
+- Accuracy and gate-region diagnostics alone do not establish that total spectral mixing is matched across arms; a common post-hoc spectral audit is reported separately when available.

gmnet/conclusions/local_results/e4_mechanism_followup_20260718/gate_diagnostics_by_block.csv

@@ -0,0 +1,86 @@
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gmnet/conclusions/local_results/e4_mechanism_followup_20260718/gate_diagnostics_by_stage.csv

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gmnet/conclusions/local_results/e4_mechanism_followup_20260718/official_metrics.csv

@@ -0,0 +1,14 @@
+key,label,family,role,reference,run_name,run_dir,checkpoint_sha256,epochs,global_step,samples,checks_status,top1,delta_top1,top5,delta_top5,nll,delta_nll,ece,delta_ece
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gmnet/conclusions/local_results/e4_mechanism_followup_20260718/results.json

@@ -0,0 +1,623 @@
+{
+  "channel_permutation_manifest_sha256": "0219d0a289ae2e3c2bde90568af4b05d23ca629522f1b19b6a1d09473d87fb8b",
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gmnet/conclusions/local_results/imagenet_v2_pregate/CONCLUSIONS.md

@@ -0,0 +1,21 @@
+# ImageNet v2 CIFAR-100 Pre-Gate Evidence
+
+Decision: **PASS**. This is a technical/controller pre-gate, not an accuracy-selection rule.
+
+All runs completed 100 fixed epochs and full clean plus five-corruption evaluation. Intervals use the three paired training seeds; n=3 intervals are necessarily wide.
+
+| Variant | Clean Top-1 effect | Mean-corruption Top-1 effect | Clean NLL effect | Corruption NLL effect |
+|---|---:|---:|---:|---:|
+| smooth_corrected | +0.587 [-0.522, +1.695] | -0.342 [-4.068, +3.384] | -0.165 [-0.299, -0.031] | -0.168 [-0.239, -0.098] |
+| relu6_only | +1.137 [-1.130, +3.403] | +0.853 [-2.326, +4.033] | -0.067 [-0.189, +0.055] | -0.087 [-0.252, +0.079] |
+
+## Controller Audit
+
+The learned-smooth minimum cap across seeds/blocks is `1.6864`; the predefined severe-collapse boundary is `0.6000`. Controller non-collapse: `True`.
+The fixed-c6 controller remains at 6.0: `True`.
+
+The old learned-cap collapse cannot be interpreted as adaptation because `raw_clip` received weight decay. With zero controller weight decay, caps remain finite and stage-dependent, so corrected learned-smooth is eligible for ImageNet.
+
+## Design Consequence
+
+Activation-only does not show the locally expected material loss. Its ImageNet test must therefore not be an early fixed-sequence gate that suppresses the ReLU and smooth hypotheses. Treat the four predeclared primary contrasts with simultaneous family-wise correction, and interpret activation-only as an operator-replacement test rather than unique causal evidence for multiplication.

gmnet/conclusions/local_results/imagenet_v2_pregate/paired_seed_effects.csv

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+variant,reference,metric,difference,ci_low,ci_high,seed_differences,inference_unit,seeds
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gmnet/conclusions/local_results/imagenet_v2_pregate/results.json

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