Vision2Web: A Hierarchical Benchmark for Visual Website Development with Agent Verification
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908930071855104 |
|---|---|
| author | He, Zehai Hong, Wenyi Yang, Zhen Pan, Ziyang Liu, Mingdao Gu, Xiaotao Tang, Jie |
| author_facet | He, Zehai Hong, Wenyi Yang, Zhen Pan, Ziyang Liu, Mingdao Gu, Xiaotao Tang, Jie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26648 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Vision2Web: A Hierarchical Benchmark for Visual Website Development with Agent Verification He, Zehai Hong, Wenyi Yang, Zhen Pan, Ziyang Liu, Mingdao Gu, Xiaotao Tang, Jie Software Engineering Artificial Intelligence 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. |
| title | Vision2Web: A Hierarchical Benchmark for Visual Website Development with Agent Verification |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2603.26648 |