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Long-horizon agents achieve state-of-the-art performance by decoupling planning credit from execution guidance via structured subtask trees.
By injecting symbolic reasoning into vision-language-action models, NS-VLA achieves remarkable gains in data efficiency and generalization for robotic manipulation.
