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CUSP: Decomposable Collective Uncertainty for Multi-Agent Multimodal Reasoning
Aggregating heterogeneous vision-language models (VLMs) can improve multimodal reasoning, but neither an individual model's confidence nor that of the aggregated answer measures reliability at the system level. We present CUSP (Collective Uncertainty through Semantic Opinion Pooling), a training-fre
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