collaborators

19 papers

cs.AI2026

Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models

Shulin Tian, Ziqi Huang, Fan Zhang +3

Recent advances in visual generative models have enabled high-quality image and video generation, but evaluating these models often demands sampling hundreds or thousands of images…

cs.CV2026

Apple-: Benchmarking Thinking with Video Towards Law-Grounded Physical Intelligence

Runmao Yao, Kairui Hu, Yukang Cao +11

Modern video generation models are increasingly hailed as emerging world models with an internalized grasp of physical law. Yet existing benchmarks largely evaluate physical plausi…

cs.AI2026

Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond

Meng Chu, Xuan Billy Zhang, Kevin Qinghong Lin +47

As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment dynamics becomes a central bottleneck. Agents that ma…

cs.CV2026

WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World

Ao Liang, Lingdong Kong, Tianyi Yan +19

Generative world models are reshaping embodied AI, enabling agents to synthesize realistic 4D driving environments that look convincing but often fail physically or behaviorally. D…

cs.CV2026

Demystifying Video Reasoning

Ruisi Wang, Zhongang Cai, Fanyi Pu +11

Recent advances in video generation have revealed an unexpected phenomenon: diffusion-based video models exhibit non-trivial reasoning capabilities. Prior work attributes this to a…

cs.CV2026

Is Your Driving World Model an All-Around Player?

Lingdong Kong, Ao Liang, Tianyi Yan +20

Today's driving world models can generate remarkably realistic dash-cam videos, yet no single model excels universally. Some generate photorealistic textures but violate basic phys…