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SteerQuant: Steering Quantization Error with Action-Guided Scaling in World-Action Models
World-action models (WAMs) jointly generate future world states and actions through iterative denoising, using shared weights to process heterogeneous semantic streams of video, proprioceptive, and action tokens. Quantization reduces inference cost, but comparable numerical errors in different strea
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