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+# Main figure captions
+
+**Figure 1 | Accuracy–cost trade-offs for local learning.** Large points and error bars show mean ±
+sample standard deviation across five initialization seeds; small translucent points show the
+individual seeds. Wall time was measured on a single GTX 1080. **a,** CIFAR-10 test accuracy versus
+wall time for width-64 residual MLPs trained for
+five epochs. The black line is the mean-based nondominated frontier among local-learning methods.
+BP is a nonlocal reference and is excluded from frontier construction. Labels give hidden depth.
+**b,** SDIL versus canonical equilibrium propagation (EP) on exact 784–500–10 (d1) and
+784–500–500–10 (d2) forward architectures and the same first 50,000 MNIST training examples.
+Within each matched architecture, every method uses the same budget: 25 epochs for d1 and 60 epochs
+for d2. EP retains its published raw-pixel, hard-sigmoid energy dynamics and relaxation
+schedule; SDIL retains its z-scored, tanh feedforward dynamics. Thus architecture, examples, and
+epochs are matched, but preprocessing and state dynamics are intentionally method-native. EP
+training particles persist across presentations as published; test particles are freshly
+zero-initialized and run for the full free-phase schedule at each evaluation.
+
+**Figure 2 | Credit-assignment scaling in deep local-learning networks.** Mean ± sample standard
+deviation across five seeds on the same CIFAR-10 width-64 residual MLP protocol. **a,** Test
+accuracy versus hidden depth. **b,** Paired accuracy gap from the exact-BP model with the same
+depth and initialization seed. **c,** Mean per-sample cosine between the teaching signal and exact
+descent direction, averaged over the earliest third of hidden layers. Exact gradients are used only
+for this diagnostic, never for local learning. “Scaling” here means preserving useful accuracy and
+credit assignment as depth grows; flattened-CIFAR accuracy itself does not increase with depth.