让AI世界记住你打过的每一场仗
现在的AI视频世界模型，你让它生成一个游戏场景，它演几秒就忘了刚才发生了什么——角色死了又活、血条清零又满，因为它根本没有“记忆”，只有一帧帧画面。这篇把“世界状态”和“画面生成”拆开：AI先把你的自然语言指令翻译成可执行的规则程序，专门维护一份“账本”记录每个角色的位置、血量、状态，连屏幕外的敌人也算数；然后这份账本再被编译成画面生成的“提词器”，告诉视频模型该画什么。结果是，你可以在AI生成的游戏里设定规则、操控单个角色，而且世界状态从头到尾不崩。在自建的战斗场景测试里，它数人头准确率94%、状态准确率98%，远超现有交互式视频模型。这不是你明天能玩上的游戏引擎，但它指向一个关键方向：让AI生成的世界从“一段视频”变成“一个能玩、能记住规则的游戏”。
📄 原文摘要(英文)
Recent video world models generate increasingly realistic and interactive visual experiences, yet lack reliable mechanisms for maintaining persistent world state and enforcing programmable rules over extended interactions. We introduce Programmable World Model, a framework that decouples world-state evolution from visual observation generation. An agent translates natural-language instructions into executable programs that specify entity states and state-transition rules, enabling direct control over individual entities and their interactions. A lightweight engine executes these programs to update and maintain an explicit, persistent global world state, including off-screen entities and non-visual attributes. To connect world state with visual generation, we introduce state-augmented 3D oriented bounding boxes (OBBs) as an intermediate representation. This representation, together with the target camera trajectory, is deterministically compiled into pixel-aligned spatiotemporal conditioning signals for a pretrained video model serving as the generative renderer. This design allows users to create playable games with predefined mechanics, direct control over individual entities, and persistent world state throughout gameplay. We further introduce CombatStateBench, a benchmark for evaluating programmable world models. On CombatStateBench, our method achieves 94% Count Accuracy and 98% State Accuracy, substantially outperforming existing interactive video world models while supporting coherent long-horizon generation. These results demonstrate the effectiveness of separating explicit state evolution from generative rendering for building persistent, programmable worlds.