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About this paper
Yu-Cheng Chou, Shihao Wang, Jin Huang, Chi-Pin Huang, Chiao-An Yang, Xianzhe Fan, Jing Wang, Zhiqi Li and 11 more
VFlash generates spatial plans for vision-language models by drafting complete structured primitives, such as trajectories, in parallel, while retaining target-verified decoding for text. On MolmoSpaces, a world action model using VFlash plans reaches 70.2% success versus 61.0% with autoregressive plans. On Jetson Thor, batch-one model-pipeline latency is 703.3 ms versus 3,552.7 ms; this is not full robot response time.
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