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This paper presents a collection of recent research papers and datasets focused on vision-language-action (VLA) models in robotics. Topics include recursive video in-context learning, simulation-guided world models, closed-loop benchmarks for driving, and methods for improving generalization and safety in VLA systems. The work highlights challenges such as task success vs. instruction following, the grounding gap, and the need for efficient and reliable adaptation in real-world scenarios.