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Longitudinal medical visual reasoning is critically underexplored, with existing models failing to grasp temporal nuances, as evidenced by their poor performance on the new LoMeVQA benchmark.
ESPP not only enhances the fidelity of GenUI evaluations but also uncovers nuanced user group divergences that traditional methods miss.
Salience Bias in LLMs reveals that models often ignore commonsense reasoning in favor of misleading explicit cues, with lightweight prompting showing promise in addressing this issue.