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arXiv AI By Yuncheng Guo, Zhanqiu Zhang, Yiwen Guo, Weijia Li

GameWAM: A World Action Model for Video Games

Read the original on arXiv AI →

GameWAM is the first World-Action Model designed for native closed-loop gameplay and GUI control in modern video games. It jointly generates future visual observations and executable keyboard-mouse trajectories using parallel visual and action generative processes, block-causal conditioning, and flow matching. The model predicts gameplay/GUI mode at each step, handles heterogeneous native controls, and employs block-cycle control for long-horizon interaction, achieving competitive task success with fewer native actions than prior agents.

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 AI.

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