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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.CV2026

S-Agent: Spatial Tool-Use Elicits Reasoning for Spatial Intelligence

Yalun Dai, Hao Li, Shulin Tian +10

Real-world spatial intelligence requires reasoning over a continuous and evolving 3D world, yet existing VLMs and tool-augmented agents largely remain tied to static, stateless inf…

cs.CV2026

SpatialBench: Is Your Spatial Foundation Model an All-Round Player?

Haosong Peng, Hao Li, Jiaqi Chen +10

While spatial foundation models have demonstrated impressive performance on standard datasets, a critical question remains: are they truly all-round players capable of generalizing…

cs.CV2026

Rethinking VLM Representation for VLA Initialization

Weifeng Lin, Siyuan Huang, Hao Li +5

Vision-Language-Action (VLA) models widely adopt pretrained Vision-Language Models (VLMs) as policy backbones, yet it remains unclear what kind of pretrained VLM representation is…

cs.CV2026

Unified Multimodal Models as Auto-Encoders

Zhiyuan Yan, Kaiqing Lin, Zongjian Li +10

Image-to-text (I2T) understanding and text-to-image (T2I) generation are two fundamental, important yet traditionally isolated multimodal tasks. Despite their intrinsic connection,…

cs.CV2024

Exploring the Causality of End-to-End Autonomous Driving

Jiankun Li, Hao Li, Jiangjiang Liu +6

Deep learning-based models are widely deployed in autonomous driving areas, especially the increasingly noticed end-to-end solutions. However, the black-box property of these model…