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20242026
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cs.CV2026

VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding

Shihao Wang, Guo Chen, De-an Huang +6

While Video Large Language Models (Video-LLMs) have shown significant potential in multimodal understanding and reasoning tasks, how to efficiently select the most informative fram…

cs.CV2026

Towards Multimodal Lifelong Understanding: A Dataset and Agentic Baseline

Guo Chen, Lidong Lu, Yicheng Liu +17

While datasets for video understanding have scaled to hour-long durations, they typically consist of densely concatenated clips that differ from natural, unscripted daily life. To…

cs.CV2026

Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning

Chi-Pin Huang, Yunze Man, Zhiding Yu +4

Vision-Language-Action (VLA) tasks require reasoning over complex visual scenes and executing adaptive actions in dynamic environments. While recent studies on reasoning VLAs show…

cs.CV2026

LocateAnything3D: Vision-Language 3D Detection with Chain-of-Sight

Yunze Man, Shihao Wang, Guowen Zhang +7

To act in the world, a model must name what it sees and know where it is in 3D. Today's vision-language models (VLMs) excel at open-ended 2D description and grounding, yet multi-ob…

cs.CV2026

PhyCritic: Multimodal Critic Models for Physical AI

Tianyi Xiong, Shihao Wang, Guilin Liu +5

With the rapid development of large multimodal models, reliable judge and critic models have become essential for open-ended evaluation and preference alignment, providing pairwise…

cs.CV2025

STORM: Token-Efficient Long Video Understanding for Multimodal LLMs

Jindong Jiang, Xiuyu Li, Zhijian Liu +13

Recent advances in video-based multimodal large language models (Video-LLMs) have significantly improved video understanding by processing videos as sequences of image frames. Howe…