Rohan Paul@rohanpaul_ai · X美团LongCat发布视频世界模型评测基准WBench。该基准将测试重点从画面美观转向控制、多轮记忆、指令遵循和物理合理性等核心能力。它包含289个案例、1058个交互轮次，评估了20个模型在导航、主体动作、事件编辑等5个维度的表现，共使用22项自动指标。研究发现，没有任何模型能在所有维度上占据主导，这表明现有系统尚未将高质量渲染、可靠控制、长期记忆与物理规则遵循整合为稳定能力。WBench的设计能区分失败是源于渲染、场景设置、控制还是物理问题，并指出导航能力与视觉质量基本无关。
Most video models look better than they understand and Video quality is only the easiest thing to notice.
LongCat just released WBench， it turned video world model testing from a beauty contest into a stress test for control， multi-turn memory， instruction-following， and physical plausibility.
It exposed the gap between beautiful video generation and controllable world simulation.
A pretty clip is not enough， because a usable world model must keep the same scene， obey later actions， move the camera correctly， preserve objects， and avoid impossible cause-and-effect.
WBench tests this with 289 cases， 1，058 interaction turns， 20 models， 5 dimensions， and 22 automatic metrics， covering navigation， subject actions， event edits， perspective switches， and both viewpoints.