游戏AI终于能看见游戏状态，而不是瞎演
现在的游戏世界模型生成NPC行为，靠的是把“理解状态、做决策、渲染画面”全揉在一起，结果NPC经常做出不合时宜的动作——因为它根本没一个明确的“当前游戏状态”接口。WorldMind把这拆成四层：先从画面提炼出紧凑状态，再基于状态规划NPC下一步，然后转成控制条件，最后生成画面，四层闭环循环。配合新造的BOSS-140K数据集(带丰富内部状态的游戏视频)，在约70%的成对对比中，人类更偏好WorldMind生成的NPC行为，认为更符合战术、更连贯。它不是你明天能玩到的游戏，但这是游戏AI从“背台词”走向“看剧本”的关键一步。
📄 原文摘要(英文)
Game world models have recently demonstrated promising capabilities in generating visually coherent and action-controllable gameplay videos. However, non-player character (NPC) behavior in existing models is either implicitly entangled with video generation or explicitly prescribed through external control signals. Consequently, a game world model has to jointly understand the state, plan the NPC's response and render its visual outcome, limiting its ability to produce responsive and state-aware NPC behavior. The challenge lies in the lack of an explicit interface for state-grounded decision-making. To this end, we introduce WorldMind, to our knowledge the first decoupled framework for state-aware NPC behavior in game world models. WorldMind separates interactive world modeling into four layers: an Understanding Layer that constructs a compact state from generated frames; a Decision Layer that reasons over the compact state to plan the NPC's next action; a Control Layer that translates the actions into temporally aligned conditions; and a Generation Layer that synthesizes their visual outcomes. By reconnecting layers in a closed interaction loop, WorldMind grounds NPC behavior in the evolving game state. We further introduce BOSS-140K, a dataset of gameplay videos paired with rich internal game states, together with an agent that automates the collection at scale. Experiments on BOSS-140K demonstrate reliable compact state reconstruction and mechanics-grounded planning, with WorldMind preferred over the baselines in approximately 70% of pairwise comparisons for its more tactically appropriate and coherent NPC behavior. Project page: https://teawhite.cn/worldmind_projectpage/