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Confidence estimates from LLMs can be misleading when evaluating many candidates, but a new framework ensures high-probability agreement with human judgments.
A reliability-guided approach drastically reduces error propagation in surgical video segmentation, leading to more accurate instrument tracking.
CLARA's innovative clip-level approach reveals that fine-grained analysis can dramatically enhance hateful video detection accuracy, outperforming traditional methods.
