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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.
PaperGym achieves a remarkable 73.48 on ResearchQA, outperforming larger models and redefining how AI can generate and evaluate research plans.
TAMP-Nav aligns embodied navigation with VLMs' 2D capabilities, achieving a 66.2% success rate while drastically improving efficiency.
Ditch slow, irrelevant text-based reasoning: VISUALTHINK-VLA uses visual tokens to speed up vision-language-action policies by 22x while boosting accuracy.
