MM-WebAgent: A Hierarchical Multimodal Web Agent for Webpage Generation
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.
69e19370860e47828575b3ed
Yan Li
69e19370860e47828575b3ee
Zezi Zeng
69e19370860e47828575b3ef
Yifan Yang
claimed_verified
2026-04-17T09:34:04.318Z
62d18eb81e36881a57f29bf4
/avatars/104851421b4ee9641daaf15942fa7ea1.svg
Yif Yang
Yif29
user
Yif29
69e19370860e47828575b3f0
Yuqing Yang
69e19370860e47828575b3f1
Ning Liao
69e19370860e47828575b3f2
Weiwei Guo
69e19370860e47828575b3f3
Lili Qiu
69e19370860e47828575b3f4
Mingxi Cheng
69e19370860e47828575b3f5
Qi Dai
69e19370860e47828575b3f6
Zhendong Wang
69e19370860e47828575b3f7
Zhengyuan Yang
69e19370860e47828575b3f8
Xue Yang
69e19370860e47828575b3f9
Ji Li
69e19370860e47828575b3fa
Lijuan Wang
69e19370860e47828575b3fb
Chong Luo
MM-WebAgent: A Hierarchical Multimodal Web Agent for Webpage Generation
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.
MM-WebAgent is a hierarchical agentic framework that coordinates AIGC-based element generation for coherent and visually consistent webpage design through joint optimization of layout and multimodal content.
multimodal webpage generation hierarchical agentic framework AIGC-based element generation hierarchical planning iterative self-reflection global layout local multimodal content multimodal element generation agent-based baselines