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University of Southern California
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LLMs struggle with affordance reasoning, lagging behind humans by 20 points, but a new method can boost their performance significantly.
Noisy label methods for frozen vision models vary wildly in performance across medical imaging datasets, with method choice becoming critical as noise increases, suggesting a need for regime-aware selection strategies.
