Video temporal grounding supports applications such as video search, content review, and automated editing by localizing events described in natural language. Yet existing generative models typically output timestamps without explicit interval-level confidence scores to guide candidate selection. We separate candidate...
Jin-Hao Chen, Ben-Lei Cui, Rui-Jian Jia et al.· 0 citations
The safety of large vision-language models is increasingly stress-tested by multimodal jailbreaks, yet existing attacks remain largely static at the meta level: template-based attacks freeze the image-text layout, while iterative attacks adapt only the image-text content with fixed attack strategies and frozen attacker...
Ben-Lei Cui, Sheng-Yuan Pang, Yu-Ke Wang et al.· 0 citations
Long-form video understanding requires locating sparse, question-relevant evidence in long, multimodal videos. Real-world video distributions differ in modality-specific information density, content structure, and evidence patterns, causing fixed video-agent designs to incur redundant processing or fail when mismatched...
Ben-Lei Cui, Ruize Wang, Junjie Li et al.· 1 citation
EvoHarmBench is presented, the first dynamic adversarial evaluation framework for content moderation systems that employs an iterative optimization loop that evolves evasion strategies at the semantic-cluster level, while simultaneously optimizing for evasion success and human readability.
Ruijie Jian, Ben-Lei Cui, Ting Ma et al.· 0 citations
As large language models are increasingly deployed in real-world systems, safety failures can still lead to harmful outputs and dangerous misuse. We argue that the essence of safety is adversarial: many failures arise not from natural inputs alone, but from strategic attempts to evade model policies and safeguards. How...
Ting Ma, Xiufeng Huang, Benlei Cui et al.· 0 citations
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