@inproceedings{zhao-etal-2026-longwebbench,
title = "{L}ong{W}eb{B}ench: Evaluating Structural and Functional Webpage Generation in Long-Horizon Settings",
author = "Zhao, Yi and
Yang, Zhen and
Chen, Mengpan and
Xu, Mingde and
Gong, ShangHui and
Liu, Xijun and
Gong, Jibing and
Tang, Jie",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2023/",
pages = "46064--46115",
ISBN = "979-8-89176-459-0",
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 multi-dimensional VLM-based metric for assessing long-range structural coherence, and a DOM-augmented agent-based pipeline for end-to-end functional verification. We further examine the automatic evaluation protocols through human agreement analysis. Experiments with state-of-the-art open-source and proprietary VLMs under single-image and multi-image settings reveal that structural fidelity degrades as webpage length increases, while visually plausible generations often fail to support executable multi-step interactions. These results highlight the need to evaluate long webpage generation beyond visual similarity, with executable interaction as a core criterion. Our code and data are available at https://github.com/zhaoyi-hb/LongWebBench."
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<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 multi-dimensional VLM-based metric for assessing long-range structural coherence, and a DOM-augmented agent-based pipeline for end-to-end functional verification. We further examine the automatic evaluation protocols through human agreement analysis. Experiments with state-of-the-art open-source and proprietary VLMs under single-image and multi-image settings reveal that structural fidelity degrades as webpage length increases, while visually plausible generations often fail to support executable multi-step interactions. These results highlight the need to evaluate long webpage generation beyond visual similarity, with executable interaction as a core criterion. Our code and data are available at https://github.com/zhaoyi-hb/LongWebBench.</abstract>
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%0 Conference Proceedings
%T LongWebBench: Evaluating Structural and Functional Webpage Generation in Long-Horizon Settings
%A Zhao, Yi
%A Yang, Zhen
%A Chen, Mengpan
%A Xu, Mingde
%A Gong, ShangHui
%A Liu, Xijun
%A Gong, Jibing
%A Tang, Jie
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhao-etal-2026-longwebbench
%X 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 multi-dimensional VLM-based metric for assessing long-range structural coherence, and a DOM-augmented agent-based pipeline for end-to-end functional verification. We further examine the automatic evaluation protocols through human agreement analysis. Experiments with state-of-the-art open-source and proprietary VLMs under single-image and multi-image settings reveal that structural fidelity degrades as webpage length increases, while visually plausible generations often fail to support executable multi-step interactions. These results highlight the need to evaluate long webpage generation beyond visual similarity, with executable interaction as a core criterion. Our code and data are available at https://github.com/zhaoyi-hb/LongWebBench.
%U https://aclanthology.org/2026.emnlp-main.2023/
%P 46064-46115
Markdown (Informal)
[LongWebBench: Evaluating Structural and Functional Webpage Generation in Long-Horizon Settings](https://aclanthology.org/2026.emnlp-main.2023/) (Zhao et al., EMNLP 2026)
ACL
- Yi Zhao, Zhen Yang, Mengpan Chen, Mingde Xu, ShangHui Gong, Xijun Liu, Jibing Gong, and Jie Tang. 2026. LongWebBench: Evaluating Structural and Functional Webpage Generation in Long-Horizon Settings. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 46064–46115, Budapest, Hungary. Association for Computational Linguistics.