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A unified framework reveals critical insights into the effectiveness of U-Net, Transformer, and SAM architectures in medical image segmentation, addressing both accuracy and efficiency challenges.
Forgetting isn't a bug, it's a feature: selectively pruning LLM agent memories boosts efficiency by 8%, sharpens content quality by 29%, and eliminates security risks entirely.
