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Escaping the endless cat-and-mouse game of deepfake detection may be possible by shifting from static pattern recognition to physics-inspired dynamical stability analysis, where real images are stable and deepfakes are not.
Transformer-based models trained directly on high-resolution 7T MRI data can spot MS lesions that classical methods miss, but not without introducing new challenges in boundary variability and artifact-related false positives.
Instead of imitating reflections, LLM agents can be trained to reason about action quality by rewarding correct judgments between alternative actions, leading to improved performance and generalization.
