Vision2Web: 一个用于视觉网站开发的分层基准，包含代理验证
Vision2Web: A Hierarchical Benchmark for Visual Website Development with Agent Verification
AI总结：
本文提出Vision2Web基准，用于评估视觉网站开发能力，包含193个任务和16类，通过GUI代理验证器和基于VLM的裁判器评估多个视觉语言模型，揭示了在全栈开发中现有模型的性能差距。
摘要：
近年来，大型语言模型的进步提升了编码代理的能力，但对复杂、端到端网站开发的系统评估仍有限。为解决这一差距，我们引入了Vision2Web，一个用于视觉网站开发的分层基准，涵盖从静态UI到代码生成、交互多页面前端复现到长周期全栈网站开发。该基准由真实世界网站构建，包含193个任务，共16类，918个原型图像和1,255个测试用例。为支持灵活、全面和可靠的评估，我们提出基于工作流的代理验证范式，基于两个互补组件：GUI代理验证器和基于VLM的裁判器。我们评估了多个在不同编码代理框架下实例化的视觉语言模型，揭示了在所有任务级别上存在显著的性能差距，最先进的模型在全栈开发上仍面临挑战。
英文摘要：
Recent advances in large language models have improved the capabilities of coding agents, yet systematic evaluation of complex, end-to-end website development remains limited. To address this gap, we introduce Vision2Web, a hierarchical benchmark for visual website development, spanning from static UI-to-code generation, interactive multi-page frontend reproduction, to long-horizon full-stack website development. The benchmark is constructed from real-world websites and comprises a total of 193 tasks across 16 categories, with 918 prototype images and 1,255 test cases. To support flexible, thorough and reliable evaluation, we propose workflow-based agent verification paradigm based on two complementary components: a GUI agent verifier and a VLM-based judge. We evaluate multiple visual language models instantiated under different coding-agent frameworks, revealing substantial performance gaps at all task levels, with state-of-the-art models still struggling on full-stack development.
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。
