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Исследования2026年9月9日
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…
Chung-En Johnny Yu, David Garcia, Brian Jalaian, Nathaniel D. Bastian·arXiv — cs.AI
Original SourceKey Takeaways
- 01.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
- 02.We present CUSP…
This material is provided as an extended analytical summary.
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