从生成到模拟：世界模型距离真正的模拟器还有多远？

摘要

随着扩散模型与大规模视频生成的快速进展，生成式世界模型日益被期待取代传统模拟器（包括物理引擎、游戏引擎和强化学习环境）。然而，从生成到模拟的差距仍有待系统性评估。我们提出了一项基于能力的研究，采用外部标尺：传统模拟器的八项能力，即资产构建、物理引擎、交互性、可控性、稳定性、状态反馈、多样性和评估指标。我们梳理了三条主要技术路线——潜在动力学、视频生成和联合嵌入预测——并将2018年至2026年6月间发表的200篇代表性工作映射到这些能力上。我们的分析表明，世界模型在特定场景的交互性和可控性方面已实现功能替代，但在物理定律的形式化保证、结构化状态反馈和可复现的长时程演化方面仍不及传统模拟器。状态反馈是各路线中最受忽视的跨路线短板：在163篇实现论文中，仅6篇提供用于查询实体状态或物理参数的运行时接口。我们指出了六个研究方向：形式化物理、统一动作接口、一等状态反馈、长时程稳定性、下游效用评估和跨路线融合。项目页面：https://github.com/AtongWang/world-model-simulators

English

With the rapid progress of diffusion models and large-scale video generation, generative world models are increasingly expected to replace traditional simulators, including physics engines, game engines, and reinforcement-learning environments. Yet the remaining distance from generation to simulation lacks a systematic assessment. We present a capability-based study using an external yardstick: eight capabilities of a traditional simulator, namely asset construction, physics engine, interaction, controllability, stability, state feedback, diversity, and evaluation metrics. We trace three main technical routes--latent dynamics, video generation, and joint-embedding prediction--and map exactly 200 representative works published from 2018 to June 2026 onto these capabilities. Our analysis shows that world models have achieved functional substitution in interaction and controllability for specific scenarios, but remain short of traditional simulators in formal guarantees of physical laws, structured state feedback, and reproducible long-horizon evolution. State feedback is the most neglected cross-route shortcoming: only 6 of 163 implementation papers expose a runtime interface for querying entity states or physical parameters. We identify six research directions: formalized physics, a unified action interface, first-class state feedback, long-horizon stability, downstream-utility evaluation, and cross-route hybridization. Project page: https://github.com/AtongWang/world-model-simulators