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cs.CV2026

TimeLens2: Generalist Video Temporal Grounding with Multimodal LLMs

Yuhan Zhu, Changlian Ma, Xiangyu Zeng +12

Video multimodal large language models (MLLMs) can describe what happens in a video, but rarely identify when the supporting evidence occurs. We study generalist video temporal gro…

cs.CV2026

VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding

Xinhao Li, Yuhan Zhu, Xiangyu Zeng +24

VideoChat3 is a fully open, 4B-parameter video-centric multimodal large language model that combines an efficient Inflated 3D Vision Transformer and adaptive frame resolution with…

cs.CV2026

InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning

Ziang Yan, Sheng Xia, Jiashuo Yu +10

Recent progress in foundation models has shifted toward agentic behavior involving multi-step reasoning and tool use. However, open-source efforts largely focus on text-dominant se…

cs.CV2026

Imagine Before You Predict: Interleaved Latent Visual Reasoning for Video Event Prediction

Tianxiang Jiang, Linquan Wu, Sheng Xia +5

Video event prediction (VEP) requires models to infer unobserved future states from partial video evidence. Existing video MLLMs usually verbalize intermediate future reasoning in…

cs.CV2026

ViCuR: Visual Cues as Recoverable Privilege for Multimodal On-Policy Distillation

Kanghui Tian, Siyuan Liu, Ziang Yan +3

On-policy distillation (OPD) improves reasoning by training a student on trajectories sampled from its own policy under supervision from a teacher. In multimodal reasoning, a commo…

cs.CV2026

FreeRet: MLLMs as Training-Free Retrievers

Yuhan Zhu, Xiangyu Zeng, Chenting Wang +6

Multimodal large language models (MLLMs) are emerging as versatile foundations for mixed-modality retrieval. Yet, they often require heavy post-hoc training to convert them into co…