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Research PaperResearchia:202603.30003[Artificial Intelligence > AI]

Vision2Web: A Hierarchical Benchmark for Visual Website Development with Agent Verification

Zehai He

Abstract

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.


Source: arXiv:2603.26648v1 - http://arxiv.org/abs/2603.26648v1 PDF: https://arxiv.org/pdf/2603.26648v1 Original Link: http://arxiv.org/abs/2603.26648v1

Submission:3/30/2026
Comments:0 comments
Subjects:AI; Artificial Intelligence
Original Source:
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arXiv: This paper is hosted on arXiv, an open-access repository
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Vision2Web: A Hierarchical Benchmark for Visual Website Development with Agent Verification | Researchia