WALL-WM is a World Action Model that shifts video-action learning from chunk-centric optimization to event-grounded Vision-Language-Action pretraining, using semantically coherent action events as the atomic unit of learning. Existing WAMs commonly initialize from multimodal or video foundation models and then optimize fixed-length action chunks conditioned directly on the current observation and instruction.
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By Alonso Urbano, David W. Romero, Max Zimmer, Sebastian Pokutta
arXiv:2606. 02852v1 Announce Type: new Abstract: Accurate short-term forecasting of residential energy load and indoor temperature is essential for home energy management systems, grid-level demand response, and community energy efficiency efforts.
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By Jinwen Wang, Youfang Lin, Xiaobo Hu, Shuo Wang, Kai Lv
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By Yichen Zhang, Yixiong Xiao, Congxi Xiao, Jingbo Zhou