Hierarchical agent coordinates multimodal content for coherent webpage generation
MM-WebAgent, a hierarchical agentic framework, generates coherent webpages by coordinating AIGC-based element creation through planning and self-reflection, outperforming code-generation and agent baselines.
Score breakdown
Developers building automated webpage generation systems can now use hierarchical agentic coordination to maintain visual consistency and global coherence when integrating AI-generated multimodal content, moving beyond isolated element generation.
- 01MM-WebAgent is a hierarchical agentic framework for multimodal webpage generation that coordinates AIGC-based element generation through hierarchical planning and iterative self-reflection
- 02The framework jointly optimizes global layout, local multimodal content, and their integration to produce coherent and visually consistent webpages
- 03The authors introduce a benchmark for multimodal webpage generation and a multi-level evaluation protocol for systematic assessment
MM-WebAgent addresses a key challenge in automated webpage generation: integrating AI-generated content (AIGC) tools for images, videos, and visualizations while maintaining visual consistency and global coherence. The framework uses a hierarchical agentic approach that coordinates element generation through hierarchical planning and iterative self-reflection, jointly optimizing global layout, local multimodal content, and their integration to produce coherent and visually consistent webpages.
The authors introduce both a benchmark for multimodal webpage generation and a multi-level evaluation protocol to systematically assess webpage quality. Experimental results demonstrate that MM-WebAgent outperforms existing code-generation and agent-based baselines, with particularly strong performance on multimodal element generation and integration. Code and data are made available at https://aka.ms/mm-webagent.
Key facts
- 01MM-WebAgent is a hierarchical agentic framework for multimodal webpage generation that coordinates AIGC-based element generation through hierarchical planning and iterative self-reflection
- 02The framework jointly optimizes global layout, local multimodal content, and their integration to produce coherent and visually consistent webpages
- 03The authors introduce a benchmark for multimodal webpage generation and a multi-level evaluation protocol for systematic assessment
- 04MM-WebAgent outperforms code-generation and agent-based baselines, especially on multimodal element generation and integration tasks
- 05Code and data are available at https://aka.ms/mm-webagent
Topics
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