baselines: complete native author-code audit

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@@ -586,6 +586,25 @@ strong modern EP variant remains necessary. DFA is also strong on easy MNIST, so
 comes from the five-depth CIFAR panel; this comparison establishes that SDIL need not trade away
 accuracy to obtain a large cost advantage over canonical EP.
 
+### Native author-code CIFAR-10 baselines
+
+These are executable-fidelity reproductions under the authors' architectures
+and schedules, not equal-compute comparisons with SDIL:
+
+| method | reproduced seed | primary test (%) | validation-selected test (%) | test-selected test (%) | published (%) | audited wall (s) |
+|:--|--:|--:|--:|--:|--:|--:|
+| BurstCCN | 0 | 80.070 | 80.100 | 80.250 | 82.97 +/- 0.21 | 15451.3 |
+| Dual Prop | 1988 | 92.460 | 92.460 | n/a | 92.41 +/- 0.07 | 23119.8 |
+
+BurstCCN's primary value is the final epoch and its best-test value is
+test-selected because native code evaluates test every epoch. Dual Prop
+restores the best-validation checkpoint and evaluates test once, so primary
+and validation-selected values coincide. Both strict records include source,
+environment, dataset, artifact, selection, and wall-definition audits. Dual
+Prop reproduces its published accuracy almost exactly, but uses VGG16 with 16
+nudged inference passes and about 6.42 hours of author-measured compute on a
+GTX 1080.
+
 ### Frozen standard-ResNet development funnel
 
 The prospective Oral-A funnel used a 45k/5k training-only CIFAR-10 split; no