WebRenderBench：通过布局样式一致性和强化学习增强Web界面生成
WebRenderBench: Enhancing Web Interface Generation through Layout-Style Consistency and Reinforcement Learning
AI总结：
提出WebRenderBench基准（45.1k真实网页）和布局样式一致性评估指标，并引入ALISA智能体将其作为强化学习奖励信号，显著提升Web界面生成性能。
摘要：
将UI图像自动转换为Web代码是前端开发和快速原型制作中的关键任务。多模态大语言模型（MLLMs）的进步使WebUI到代码的转换日益可行，然而现有基准在数据多样性和评估可靠性方面仍存在局限。为解决这些问题，我们提出了WebRenderBench，一个从真实门户网站收集的包含45.1k个网页的大规模基准，提供了比先前基准更高的多样性、复杂性和真实性。我们进一步提出了一种新颖的评估指标，从最终渲染页面衡量布局和样式一致性。与依赖昂贵LLM推理的基于视觉的方法或易受噪声和不对称性影响的结构化比较方法不同，我们的方法能够实现更高效、客观和可靠的UI质量评估。最后，我们引入了自动化布局与样式检查智能体（ALISA），将该指标作为奖励信号集成到强化学习中，以增强在爬取的非对称网页上的训练。实验表明，ALISA显著提升了生成性能，在多个指标上取得了最先进的结果。
英文摘要：
Automating the conversion of UI images into web code is a critical task for front-end development and rapid prototyping. Advances in multimodal large language models (MLLMs) have made WebUI-to-Code increasingly feasible, yet existing benchmarks remain limited in data diversity and evaluation reliability. To address these issues, we present WebRenderBench, a large-scale benchmark of 45.1k webpages collected from real-world portal sites, offering greater diversity, complexity, and realism than prior benchmarks. We further propose a novel evaluation metric that measures layout and style consistency from the final rendered pages. Unlike vision-based methods that rely on costly LLM reasoning or structure-based comparisons vulnerable to noise and asymmetry, our approach enables more efficient, objective, and reliable UI quality assessment. Finally, we introduce the Automated Layout and Style Inspection Agent (ALISA), which integrates this metric into reinforcement learning as a reward signal to enhance training on crawled asymmetric webpages. Experiments show that ALISA significantly boosts generation performance, achieving state-of-the-art results across multiple metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
