让AI自己当游戏策划、程序员和测试员
做游戏最难的不是写代码，而是把代码、画面、动画、物理规则这些互相牵扯的东西拼成一个能玩的整体。VibeGame 让八个 AI 代理分工协作：几个负责写，几个专门挑毛病，挑出问题再改，循环几轮直到能玩。更狠的是，每做完一个游戏，它会把过程提炼成可复用的模板，下次做新游戏直接套。在测试集上，它用同一个底层模型，比各种现有方案都强。这不是你明天能用来做游戏的工具，但它指向一个方向：AI 开发软件不再是单打独斗，而是自己组队、自己验收。
📄 原文摘要(英文)
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.