arXiv cs.MA· Wenbo Hu, Ken Li, Jiazhe Wei, Yukang Cao, Weiyi Hong, Jiayi Dai, Chenjun Bai, Jiajun Liang, Yucheng Liao, Ruichuan An, Zeyu Lou, Haofan Wang, Yueming Lyu, Jingkang Yang, Ziwei Liu, Chenyang Si·· 12 小时前AI 评分52
VibeGame：用 AI 原生引擎与自进化对抗智能体团队实现提示词到游戏开发
VibeGame: Prompt-to-Game Development with AI-Native Engine and Self-Evolving Adversarial Agent Team
AI 导读
VibeGame 是一个从自然语言提示词和可选参考图生成完整 2D 游戏项目的框架，其 AI 原生引擎把项目状态表示为 schema 校验的结构化文本，并提供帧同步运行时控制，让智能体自行检查状态、执行动作并验证行为。
正文
Authors:Wenbo Hu, Ken Li, Jiazhe Wei, Yukang Cao, Weiyi Hong, Jiayi Dai, Chenjun Bai, Jiajun Liang, Yucheng Liao, Ruichuan An, Zeyu Lou, Haofan Wang, Yueming Lyu, Jingkang Yang, Ziwei Liu, Chenyang Si
Abstract:Large language model agents have enabled software development through natural-language interaction, but extending this paradigm to end-to-end game development remains challenging. Building a playable game requires coordinating code, scenes, assets, animations, physics, and configurations, while allowing developers to validate their integrated behavior through interactive testing. We introduce VibeGame, a framework for creating complete 2D game projects from natural-language prompts and optional reference images. Its AI-native game engine represents project state as schema-validated structured text and provides frame-synchronous runtime control, enabling agents to inspect game states, execute actions, and verify behavior at their own pace. Built upon this interface, we design an Adversarial Agent Team of eight specialized agents to separate implementation from review, so that problems are found and fixed through multi-round loops. For each conducted and accepted project, we further introduce a training-free self-evolution mechanism to distill it into reusable skeletons, modules, and collaboration contracts for future development. We evaluate prompt-assets-to-game (PA2G), where agents are given an asset library, and prompt-to-game (P2G), where agents generate their own art. On a GameCraft-Bench subset, VibeGame in P2G consistently outperforms a range of agent harnesses in either P2G or PA2G with the same underlying model, including GPT-6.1-Sol.
| Subjects: | Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2610.33106 [cs.MA] |
| (or arXiv:2610.33106v1 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33106 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Wenbo Hu [view email]
[v1]
Thu, 22 Oct 2026 18:03:17 UTC (4,351 KB)
来源：arXiv cs.MA · arxiv.org