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Southern University of Science and Technology
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Audio deepfake detectors trained on diffusion-reconstructed "hard" examples generalize far better to unseen attacks, slashing error rates compared to standard training.
Similarity alone is a poor guide for LLM depth pruning: jointly considering representational similarity *and* transformation difference unlocks significantly better compression.
