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Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination
World-Action Models (WAMs) have emerged as a promising paradigm for embodied control by coupling future visual prediction with action generation. However, many existing WAMs rely on photorealistic future prediction, which incurs high inference latency and makes real-time robot deployment difficult.
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