Yujun Cai

Diffusion large language models (dLLMs) offer bidirectional attention and parallel generation, enabling them to exploit global context and naturally support format-constrained tasks like parseable JSON or reasoning templates. While straightforward fixed anchors can enforce such constraints, they...

Scaling multimodal large language models (MLLMs) to long videos is constrained by limited context windows. While retrieval-augmented generation (RAG) is a promising remedy by organizing query-relevant visual evidence into a compact context, most existing methods (i) flatten videos into independen...

While Multimodal Large Language Models (MLLMs) excel at general vision-language tasks, precise coordinate prediction remains a significant challenge, particularly as high-resolution inputs cause visual positional encodings (VPEs) to degrade. We demonstrate that these encoding failures do not resu...

Zero-Shot Composed Image Retrieval (ZS-CIR) retrieves target images using a reference image and modification text without task-specific training. Existing methods typically rely on MLLMs to generate query vectors with pre-trained models like CLIP. However, those constructed queries suffer from in...

Large Vision-Language Models (LVLMs) confront an escalating threat from sophisticated multimodal jailbreak attacks. However, existing defense strategies suffer from three critical limitations: (1) the neglect of visual threats; (2) a lack of fine-grained specificity regarding specific attack sema...