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Current scientific agents struggle to maintain a coherent narrative across evidence and calculations, with only 34.81% achieving strict accuracy in complex tasks.
MLLMs excel at precision but falter dramatically in extracting complete product specifications from multiple images, with a mere 49.9% recovery rate.
Current methods for on-the-fly category discovery are fundamentally flawed because they treat it as a static classification problem, but PACO demonstrates that a dynamic, calibrated approach yields substantial improvements.
Training-free change detection gets a serious boost: CoRegOVCD leverages posterior calibration and geometric consistency to identify semantic changes in remote sensing imagery with significantly improved accuracy.
