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Knowing When to Stop: Adaptive Action Chunking via Internal Cross-Attention Dynamics in VLAs

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

Action chunking is a standard execution strategy in modern Vision-Language-Action (VLA) frameworks, but fixed execution horizons impose a trade-off between efficiency and accuracy. Short chunks require frequent inference and may cause oscillatory behavior, whereas long chunks can become misaligned with newly observed states. We address this limitation with an adaptive action chunking approach based on internal cross-attention dynamics in the action expert. We observe that, as the prediction horizon extends, action-to-observation cross-attention becomes increasingly dispersed and its entropy ri

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