游戏AI终于能看见游戏状态，而不是瞎演
现在的游戏NPC看似会动，其实只是视频生成模型在“演”——它把画面、动作、反应全搅在一起，根本不知道游戏里发生了什么。WorldMind把这件事拆成了四层：先看懂当前画面里的状态，再基于这个状态决定NPC下一步该干嘛，然后把决定翻译成画面指令，最后才生成画面。关键在“闭环”:NPC的每个动作都会改变游戏状态，下一帧再基于新状态重新决策，而不是照着脚本演完。测试里，玩家在约70%的对比中觉得它的NPC行为更合理、更有战术意识。它不是你明天能玩到的游戏，但这是游戏AI从“会演”走向“会玩”的一个真分岔口。
📄 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/