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Treating robot skill calls as runtime-verified execution proposals rather than blind action predictions enables self-correcting VLA autonomy that reaches 97.4% success on LIBERO.
An 8B model can now generate scientific graphics code that rivals or surpasses the output of much larger proprietary models, thanks to a new dataset, benchmark, and reinforcement learning approach.
A new unified LVLM, OmniCT, bridges the gap between slice-level detail and volumetric understanding in CT scans, outperforming existing methods by a significant margin.
