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cs.AI2026
CUSP: Decomposable Collective Uncertainty for Multi-Agent Multimodal Reasoning
Chung-En Johnny Yu, David Garcia, Brian Jalaian +1
Aggregating heterogeneous vision-language models (VLMs) can improve multimodal reasoning, but neither an individual model's confidence nor that of the aggregated answer measures re…
cs.AI2026
SCoOP: Semantic Consistent Opinion Pooling for Uncertainty Quantification in Multiple Vision-Language Model Systems
Chung-En Johnny Yu, Brian Jalaian, Nathaniel D. Bastian
Combining multiple Vision-Language Models (VLMs) can enhance multimodal reasoning and robustness, but aggregating heterogeneous models' outputs amplifies uncertainty and increases…