arXiv Machine Learning July 7, 2026 By Zelin Zhao, Min Shi, Bo Yuan, Haotian Xue, Jialuo Li, Lama Moukheiber, Humphrey Shi, Yongxin Chen

WorldBagel: Uncovering the Power of Unified Multimodal Models for Vision-Language-Action-World Modeling

Read the original on arXiv Machine Learning →

arXiv:2607. 03461v1 Announce Type: cross Abstract: World models aim to capture environment dynamics in ways that support perception, reasoning, and action, and have recently become a central direction in Vision-Language-Action-World (VLAW) modeling.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data

RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis

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