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About this paper
Minghao Zhu, Bohan Hou, Kun Xiang, Zixuan Wang, Yinzhou Tang, Yifei Yang, Zhikai Wang, Tianyi Zhang and 15 more
RynnUnify uses visual prediction within ongoing reasoning for long-horizon tasks: a reasoner proposes subtasks, and a video generator predicts resulting scenes that inform later decisions. On Rynn-VP, it scores 54.80, versus 45.05 for Cosmos 3 Nano. A separate post-trained World-Action Model reaches 51.5% success on Selected-20, while these evaluations do not establish general-purpose robot capability.
Key insights
- Interleaving reasoning and predicted visual outcomes offers a way to maintain task context across multiple steps, rather than treating video generation as a standalone output.
- Generated video is useful selectively: generation-assisted reasoning improves seven of ten Rynn-GEU subsets, but slightly reduces performance on some fine-grained spatial measures.
- The model’s planning and video representations can support physical control, though current robot results are preliminary and repeated video generation carries computational cost.
Summary by alphaXiv.
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