首个开源具身场景视频生成模型LingBot-Video发布

A first-of-its-kind open-source video generation model for embodied scenarios, using a Mixture-of-Experts design.

LingBot-Video from @robbyant_brain, a Diffusion Transformer video foundation model built for embodied AI.

• The flagship 30B model activates only 3B parameters per generation, at 1M-token sequences
• At the same total parameter scale it reaches ~3X the inference efficiency of a Dense architecture at 1M-token sequences (3.18× vs. Dense 30B)
• Aesthetic-only reward models are why video generators fake physics, and this paper basically says so. Physical plausibility is not treated as vibes here; it gets scored through causality, object permanence, non-penetration and material realism.
• The model was not trained only on regular internet videos. Its pretraining data also includes 70,000+ hours of embodied footage, covering robot manipulation, navigation, and first-person action videos, so the model sees examples where movement actually changes the world.
• On RBench, it reports a 0.620 average, ahead of every listed open and closed model, while still losing some columns to Wan 2.6.

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