MM-WebAgent: A Hierarchical Multimodal Web Agent for Webpage Generation
Authors: name: Yan Li, name: Zezi Zeng, name: Yifan Yang, name: Yuqing Yang, name: Ning Liao, name: Weiwei Guo, name: Lili Qiu, name: Mingxi Cheng, name: Qi Dai, name: Zhendong Wang, name: Zhengyuan Yang, name: Xue Yang, name: Ji Li, name: Lijuan Wang, name: Chong Luo
Organization: Microsoft Research

The rapid progress of Artificial Intelligence Generated Content (AIGC) tools enables images, videos, and visualizations to be created on demand for webpage design, offering a flexible and increasingly adopted paradigm for modern UI/UX. However, directly integrating such tools into automated webpage generation often leads to style inconsistency and poor global coherence, as elements are generated in isolation. We propose MM-WebAgent, a hierarchical agentic framework for multimodal webpage generation that coordinates AIGC-based element generation through hierarchical planning and iterative self-reflection. MM-WebAgent jointly optimizes global layout, local multimodal content, and their integration, producing coherent and visually consistent webpages. We further introduce a benchmark for multimodal webpage generation and a multi-level evaluation protocol for systematic assessment. Experiments demonstrate that MM-WebAgent outperforms code-generation and agent-based baselines, especially on multimodal element generation and integration. Code & Data: https://aka.ms/mm-webagent.