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20242026
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cs.CV2026

VGI-Bench: Probing Visual Intelligence in Video Generation Models

Xuan He, Cong Wei, Yuhao Cheng +20

Recent studies suggest that video generation models can exhibit certain forms of zero-shot visual reasoning through generated frames. Yet reliable evaluation remains challenging: b…

cs.CV2026

Beyond SFT-to-RL: Pre-alignment via Black-Box On-Policy Distillation for Multimodal RL

Sudong Wang, Weiquan Huang, Xiaomin Yu +9

The standard post-training recipe for large multimodal models (LMMs) applies supervised fine-tuning (SFT) on curated demonstrations followed by reinforcement learning with verifiab…

cs.CV2026

Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling

Keming Wu, Zuhao Yang, Kaichen Zhang +24

Recent visual generation models have made major progress in photorealism, typography, instruction following, and interactive editing, yet they still struggle with spatial reasoning…

cs.CV2026

ParaVT: Taming the Tool Prior Paradox for Parallel Tool Use in Agentic Video Reinforcement Learning

Zuhao Yang, Kaichen Zhang, Sudong Wang +7

Training large multimodal models (LMMs) via reinforcement learning (RL) to natively invoke video-processing tools (e.g., cropping) has become a promising route to long-video unders…

cs.CV2026

LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling

Zuhao Yang, Sudong Wang, Kaichen Zhang +8

Large multimodal models (LMMs) have shown great potential for video reasoning with textual Chain-of-Thought. However, they remain vulnerable to hallucinations, especially when proc…

cs.CV2026

WorldReasonBench: Human-Aligned Stress Testing of Video Generators as Future World-State Predictors

Keming Wu, Yijing Cui, Wenhan Xue +11

Commercial video generation systems such as Seedance2.0 and Veo3.1 have rapidly improved, strengthening the view that video generators may be evolving into "world simulators." Yet…