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Most test-time adaptations in vision-language models are either redundant or actively destructive鈥攁nd skipping up to 85% of them via simple consistency checks actually boosts final accuracy.
Eliciting confidence at multiple stages reveals that the final retrieval step's uncertainty signal can dramatically enhance the reliability of AI-generated answers.
