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arXiv Computer Vision By Linhan Wang, Zijian An, Mingyuan Zhang, Chen Dai, Yi Xu, Can Cui, Zichong Yang, Yinlin Chen, Lifeng Zhou, Chang-Tien Lu

GlanceWAM: Sparse Test-Time Imagination for World-Action Models

Read the original on arXiv Computer Vision →

GlanceWAM introduces a sparse test‑time imagination approach for world‑action models that decouples visual imagination from control. By asynchronously generating a single lookahead frame on a slow clock and decoding action chunks at a 48 ms control rate purely in latent space, it avoids latency while maintaining high success. The method achieves 72.2 % on the RoboCasa kitchen benchmark and 99.0 % on LIBERO, running 24× faster than synchronous baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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