LongWebBench: Evaluating Structural and Functional Webpage Generation in Long-Horizon Settings
arXiv:2606.17727v1 Announce Type: new Abstract: Recent vision-language models (VLMs) have shown promising progress in generating webpages from visual inputs, yet existing evaluations mainly focus on short, single-screen, and largely static webpages. We introduce LongWebBench, a benchmark for evaluating long-horizon webpage generation from both structural and functional perspectives. LongWebBench contains 490 real-world long webpages for structural fidelity evaluation and 507 goal-oriented interaction tasks over 129 webpages for functional evaluation. It employs two complementary protocols: a m
The rapid advancement of vision-language models necessitates more robust and comprehensive evaluation benchmarks for complex real-world applications like webpage generation.
This benchmark addresses a crucial gap in evaluating the practical utility of AI models in generating functional and long-form web content, moving beyond static, simple designs.
The focus of VLM evaluation for web generation shifts from simple visual fidelity to complex structural and functional interaction, pushing models towards more sophisticated capabilities.
- · AI model developers specializing in web generation
- · Companies requiring automated web content creation
- · Users interacting with AI-generated web interfaces
- · AI models performing poorly on complex web tasks
- · Manual web design and development for simple interfaces
Improved AI models capable of generating more complex and interactive webpages will emerge.
The cost and time associated with web development, particularly for dynamic and context-aware sites, could significantly decrease.
The proliferation of highly personalized and dynamically generated web experiences could fundamentally alter online interaction and content consumption.
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Read at arXiv cs.AI