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arXiv — cs.AI

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CUSP: Decomposable Collective Uncertainty for Multi-Agent Multimodal Reasoning

arXiv:2609.05708v1 Announce Type: new Abstract: 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…

arXiv — cs.AIBy: Chung-En Johnny Yu, David Garcia, Brian Jalaian, Nathaniel D. BastianSeptember 9, 2026 (Sep 9)
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  • arXiv:2609.05708v1 Announce Type: new Abstract: 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…
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CUSP: Decomposable Collective Uncertainty for Multi-Agent Multimodal Reasoning

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