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Current medical LLMs struggle to consistently follow clinical guidelines, revealing a critical gap in their decision-making capabilities.
Injecting physics-inspired spatial priors into video MLLMs drastically enhances temporal stability and segmentation accuracy without sacrificing image-level performance.
Bypassing geometric warping, this framework reveals that mining semantic consensus directly in feature space can dramatically enhance cross-view geo-localization accuracy.
Calibration errors that plague traditional methods are effectively neutralized, leading to unprecedented accuracy in 6-DOF motion estimation.
