WorldMind is a decoupled framework for state-aware NPC behavior in game world models, separating interactive world modeling into four layers: Understanding, Decision, Control, and Generation. It constructs a compact state from generated frames, reasons over it to plan NPC actions, translates actions into temporally aligned conditions, and synthesizes visual outcomes. Experiments on the newly introduced BOSS-140K dataset show that WorldMind achieves more tactically appropriate and coherent NPC behavior than baseline models in about 70% of pairwise comparisons.
By Zhiyang Deng, Boran Zhang, Danze Chen, Yeying Jin
arXiv:2609.10540v1 Announce Type: new
Abstract: Recent video world models generate increasingly realistic and interactive visual experiences, yet lack reliable mechanisms for maintaining persistent w...
By Zheng-Hui Huang, Guixu Lin, Jiacheng Lin, Yi-Chuan Huang, Ruihan Yu, Muyao Niu, Siqi Yang, Yu-Lun Liu, Yung-Yu Chuang, Kaipeng Zhang, Zhixiang Wang
arXiv:2609.16679v1 Announce Type: new
Abstract: Foundation models, alongside advances in learned game-world models, are reshaping AI across the game lifecycle. Beyond playing games, recent systems mo...
By Meng Luo, Yanlin Li, Hao Li, Hongzhan Lin, Pengfei Zhou, Tianjie Ju, Ran Zhang, Yeying Jin, Mong-Li Lee, Wynne Hsu
The paper introduces Code World Model, a framework that decouples world evolution from visual rendering by using a coding agent as a world brain. The agent reasons about events, generates executable code to maintain persistent state, and a proxy representation links this state to a video model for high‑fidelity visual output. Experiments with MiniMax‑H3 show that the system can follow proxy‑based spatiotemporal specifications while preserving rich visual dynamics, illustrating a new approach to open‑ended world modeling.
By Yiwen Chen, Guosheng Lin, Chi Zhang
Magpie is a real‑time generative world‑rendering system designed for interactive games. It decouples gameplay execution from visual generation, allowing designers to define scenes and rules in a game engine while a separate render server produces visual output from the engine’s white‑box frames. This approach preserves gameplay designability and reproducibility, and reduces early prototype dependence on finished visual assets.
By Xiaoyu Zhan, Xinyu Wang, Xiaohong Zhang, Huanjie Zhu, Tengjiao Sun, Pengcheng Fang, Jiaxing Yu, Yanwen Guo, Dongjie Fu
arXiv:2608.24680v1 Announce Type: new
Abstract: Video games provide a scalable source of training data for video world models, offering diverse environments, complex interactions, and abundant in-the...
By Wenxuan Shen, Dongna Jin, Dongping Chen
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.
By Yuncheng Guo, Zhanqiu Zhang, Yiwen Guo, Weijia Li
arXiv:2609.25001v1 Announce Type: new
Abstract: Modern video games provide a measurable testbed for AI models, combining abilities of visual understanding, instruction decomposition, goal planning, a...
By Yiran Wang, Xingyilang Yin, Junfu Pu, Guangzhi Wang, Kaifeng Li, Mingyu Ouyang, Huiqiang Sun, Lingen Li, Cheng Cheng, Wangbo Yu, Honghao Chen, Xiaodong Cun, Chi-Man Pun, Zhiguo Cao, Ying Shan
Magpie is a real‑time generative world‑rendering system that decouples gameplay execution from visual generation. Designers set up scenes and rules in a game engine, which handles player actions and world state, while a separate Render Server produces visual output from the engine’s white‑box frames. This approach lets developers create interactive games without needing fully finished visual assets early in the prototype stage.
arXiv:2609.22308v1 Announce Type: new
Abstract: Coding-agent benchmarks usually evaluate implementation after the target behavior has been specified in text, code, or demonstrations. Existing researc...
By Boyu Qiao, Zixin Tang, Xiaoshuai Hao, Wenbo Li
arXiv:2606. 16014v1 Announce Type: cross Abstract: Many games rely on storytelling combined with systems that track levelling, NPC behaviour, and consequence simulation; bridging tightly-authored narrative with deeply-simulated worlds -- most acute in sandbox and open-world settings -- has been prohibitively expensive.
By Yuhang Huang, Chenmiao Li, Chaowei Fang
A2Z GameSpec-Bench introduces a benchmark of 100 long‑form Game Design Documents (GDDs) to evaluate how faithfully coding agents can generate complete games from detailed specifications. The benchmark measures faithfulness by checking that the game satisfies the GDD requirements and preserves the relationships among them, using a dependency‑aware contract and a combination of source‑code inspection and agent‑generated test policies. Evaluations show that current agents struggle to meet interdependent requirements, but requirement‑specific feedback improves GDD fidelity by 10.9% after two revision rounds.
By Seonho Lee, Wonryeol Jeong, Alberto Cereser, Inha Kang, Hyeonjong Kim, Seungmin Kwak, Dongmin Park