State of World Models 2026 : Taxonomy, Benchmarks and Open Challenges

Engineering notes

It introduces a practical definition, proposes a multidimensional taxonomy, maps major model families, reviews emerging benchmark approaches, and discusses open challenges including physical consistency, long-horizon coherence, action conditioning, causal structure, sim-to-real transfer, safety, and functional utility.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

World models are becoming an increasingly important, but still loosely defined, area of artificial intelligence. They include systems designed to learn predictive representations of environments, simulate possible futures, support planning, evaluate actions, or generate temporally and spatially coherent environments. This report provides a structured overview of the world models landscape as of 2026. It introduces a practical definition, proposes a multidimensional taxonomy, maps major model families, reviews emerging benchmark approaches, and discusses open challenges including physical consistency, long-horizon coherence, action conditioning, causal structure, sim-to-real transfer, safety, and functional utility. The report does not propose a universal ranking or a final definition of the field. Its purpose is to offer a neutral and reusable framework for researchers, engineers, journalists, analysts, and technical decision-makers who need to compare systems developed across reinforcement learning, robotics, embodied AI, autonomous driving, generative video, spatial intelligence, simulation, and autonomous agents.

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