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cs.AI2026

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure

Zhetong Zhang, Honghao Fu, Miao Xu +2

As Video Large Language Models are increasingly deployed in real-world applications, ensuring their safety alignment has become critical. Counterintuitively, we find that harmful v…

cs.AI2025

ContextNav: Towards Agentic Multimodal In-Context Learning

Honghao Fu, Yuan Ouyang, Kai-Wei Chang +3

Recent advances demonstrate that multimodal large language models (MLLMs) exhibit strong multimodal in-context learning (ICL) capabilities, enabling them to adapt to novel vision-l…

cs.AI2025

RefineShot: Rethinking Cinematography Understanding with Foundational Skill Evaluation

Hang Wu, Yujun Cai, Haonan Ge +3

Cinematography understanding refers to the ability to recognize not only the visual content of a scene but also the cinematic techniques that shape narrative meaning. This capabili…

cs.AI2025

DiMo-GUI: Advancing Test-time Scaling in GUI Grounding via Modality-Aware Visual Reasoning

Hang Wu, Hongkai Chen, Yujun Cai +4

Grounding natural language queries in graphical user interfaces (GUIs) poses unique challenges due to the diversity of visual elements, spatial clutter, and the ambiguity of langua…

cs.AI2025

BRIGHT+: Upgrading the BRIGHT Benchmark with MARCUS, a Multi-Agent RAG Clean-Up Suite

Liyang Chen, Yujun Cai, Jieqiong Dong +1

Retrieval-Augmented Generation (RAG) systems require corpora that are both structurally clean and semantically coherent. BRIGHT is a recent and influential benchmark designed to ev…

cs.AI2025

Process or Result? Manipulated Ending Tokens Can Mislead Reasoning LLMs to Ignore the Correct Reasoning Steps

Yu Cui, Bryan Hooi, Yujun Cai +1

Recent reasoning large language models (LLMs) have demonstrated remarkable improvements in mathematical reasoning capabilities through long Chain-of-Thought. The reasoning tokens o…