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Intel Labs
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UQ rankings for GUI grounding are stable within model classes but falter across different models and interfaces, highlighting the need for tailored calibration in practical applications.
Decomposing uncertainty into aleatoric and epistemic types lets robots recover from errors 21% more effectively than treating all uncertainty the same.
