collaborators

18 papers

cs.CV2026

BusterX++: Towards Unified Cross-Modal AI-Generated Content Detection and Explanation with MLLM

Haiquan Wen, Tianxiao Li, Zhenglin Huang +2

The rapid advancement of generative AI has substantially improved image and video synthesis, amplifying the risk of multimodal visual misinformation. Recent MLLMs have shown promis…

cs.CV2026

BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation

Haiquan Wen, Yiwei He, Zhenglin Huang +7

As generative video models become increasingly realistic, detecting AI-generated videos requires systems that offer both accuracy and interpretability. However, applying Multimodal…

cs.CV2026

Watch, Remember, Reason: Human-View Video Understanding with MLLMs

Jiahao Meng, Yue Tan, Qi Xu +12

Video understanding is being rapidly transformed by multimodal large language models (MLLMs), as research moves from short clips to long, multimodal, and knowledge-intensive video…

cs.AI2026

From Table to Cell: Attention for Better Reasoning with TABALIGN

Tung Sum Thomas Kwok, Zeyong Zhang, Xinyu Wang +6

Multi-step LLM reasoning over structured tables fails because planning and execution share no explicit cell-grounding contract. Existing methods constrain the planner to a left-to-…

cs.MA2026

Safe Multi-Agent Behavior Must Be Maintained, Not Merely Asserted: Constraint Drift in LLM-Based Multi-Agent Systems

Tianxiao Li, Yixing Ma, Haiquan Wen +4

Modern LLM based agents are no longer passive text generators. They read repositories, call tools, browse the web, execute code, maintain memory, communicate with other agents, and…

cs.CV2026

Omni-Fake: Benchmarking Unified Multimodal Social Media Deepfake Detection

Tianxiao Li, Zhenglin Huang, Haiquan Wen +10

Multimodal deepfakes are proliferating on social media and threaten authenticity, information integrity, and digital forensics. Existing benchmarks are constrained by their single-…