collaborators

26 papers

cs.SE2026

Does It Render Everywhere? A Study of Cross-Environment Compatibility in MLLM-Generated Webpages

Ziyun Guo, Jingyu Xiao, Yuqiang Sun +1

Multimodal Large Language Models (MLLMs) have been increasingly adopted to automate webpage generation from visual designs (e.g., screenshots). However, existing evaluations are li…

cs.CR2026

Convergent Detour Hijacking: Task-Preserving Resource Amplification in Skill-Based LLM Agents

Junliang Liu, Ruoyu Li, Wenxin Tang +4

LLM agents increasingly rely on third-party skills, using natural-language descriptions for selection and instruction bodies for planning. This progressive-disclosure design expose…

cs.CV2026

CodeShrink: Adaptive Visual Compression for Efficient Multimodal Code Understanding

Wenxin Tang, Jingyu Xiao, Zhenyu Liu +6

Rendering source code as images offers a promising way to reduce the input costs of Multimodal Large Language Models (MLLMs). Adjusting image resolution can trade visual token cost…

cs.SE2026

VisualRepair: Dynamic Tool Calling and Region Focusing for Visual Software Issue Repair

Jingyu Xiao, Zhongyi Zhang, Haoran Hou +4

VisualRepair is a multimodal LLM framework that classifies bug‑related images, dynamically calls specialized tools, and focuses on relevant screenshot regions to improve automated…

cs.SE2026

EfficientUICoder: A Bidirectional Token Compression Framework for Efficient MLLM-Based UI Code Generation

Jingyu Xiao, Zhongyi Zhang, Yuxuan Wan +3

Multimodal Large Language Models have demonstrated exceptional performance in UI2Code tasks, significantly enhancing website development efficiency. However, these tasks incur subs…

cs.CL2026

Beyond NL2Code: A Structured Survey of Multimodal Code Intelligence

Xuanle Zhao, Qiushi Sun, Jingyu Xiao +16

While Large Language Models (LLMs) have substantially advanced text-to-code synthesis, many real programming tasks specify intent through visual artifacts such as screenshots, char…