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Life sciences · Preprint
arXiv · September 8, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint proposes Oscillatory Predictive Learning (OPL), a neural architecture combining Artificial Kuramoto Oscillatory Neurons with self-supervised pretraining, and reports adversarial robustness results under a randomized attack protocol on CIFAR-10 and CIFAR-100. The authors claim emergent robustness without explicit adversarial training or purification, but the work has not undergone peer review and lacks quantitative comparison to standard adversarial training baselines.
Computational method development with empirical evaluation on standard benchmarks. Image classification tasks on CIFAR-10 and CIFAR-100 datasets; no natural population or clinical setting.. Intervention: Oscillatory Predictive Learning (OPL) with Artificial Kuramoto Oscillatory Neurons and X-PhiNet self-supervised pretraining. Compared with: Randomized adversarial defense methods with standardized attack protocols; no explicit quantitative comparison to adversarial training or purification stated in abstract..
OPL achieves 76.63±0.76% robust accuracy on CIFAR-10 under ℓ∞, ε=8/255, AutoAttack-rand with EoT K=20 OPL achieves 50.44% robust accuracy on CIFAR-100 under ℓ∞, ε=8/255, AutoAttack-rand with EoT K=20 Method combines Artificial Kuramoto Oscillatory Neurons with predictive self-supervised pretraining using X-PhiNet
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This is an unrefereed preprint introducing a novel architecture (OPL with AKOrN) and reporting adversarial robustness results on standard benchmarks; the work is technically sound but lacks peer review and independent validation.
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Adversarial robustness in computer vision is still largely achieved through adversarial training or test-time adversarial purification, both of which introduce significant computational overhead by generating adversarial examples during training or performing iterative denoising at test time. We study whether empirical robustness can instead emerge from architectural and representation-learning inductive biases. We introduce Oscillatory Predictive Learning (OPL), a two-stage framework that combines Artificial Kuramoto Oscillatory Neurons (AKOrN) with predictive self-supervised pretraining using X-PhiNet. Because our default checkpoint uses randomized initial oscillator states, we compare it with other randomized adversarial defense methods that provide precise, reproducible, and strong attack protocols. Experiments on CIFAR-10 and CIFAR-100, with additional corruption evaluation on CIFAR-10-C, demonstrate that our method achieves competitive results under the AutoAttack-rand evaluation protocol. On CIFAR-10 and CIFAR-100, OPL attains 76.63$\pm$0.76$\%$ and 50.44$\%$ robust accuracy, respectively, under $\ell_\infty$, $ε=8/255$, AutoAttack-rand with EoT $K=20$.
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