arXiv — cs.AI preprintsInternational9 October 2026
PearlVLA: Progressive Embodied Action-Plan Refinement in Latent Space
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arXiv:2606.17924v2 Announce Type: replace-cross Abstract: Current Vision-Language-Action (VLA) models face a trade-off between efficient action generation and explicit deliberation. Directly decoding actions from vision-language backbone representations enables low-latency control, whereas textual reasoning, pixel-level subgoals, or world-model evaluation of decoded actions can improve planning but incur substantial latency and computational cost. We propose PearlVLA, a VLA framework that progressively refines a VLM-derived latent plan using feedback from the predicted consequence of each inte
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