@inproceedings{zhang-etal-2026-artifactsbench,
title = "{A}rtifacts{B}ench: Bridging the Visual-Interactive Gap in {LLM} Code Generation Evaluation",
author = "Zhang, Chenchen and
Li, Yuhang and
Liu, Jiaheng and
Xu, Can and
Liu, Ao and
Zhou, Changzhi and
Deng, Ken and
Wu, Dengpeng and
Huang, Guanhua and
Li, Kejiao and
Yi, Qi and
Lv, Ruilin and
Hu, Shihui and
Zhang, Yue and
Jiang, Yuhao and
Xu, Zenan and
Zhang, Yuanxing and
Zhou, Wiggin and
Zhou, Bo and
Lian, Fengzong",
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.2229/",
pages = "50735--50765",
ISBN = "979-8-89176-459-0",
abstract = "Large Language Models (LLMs) increasingly generate executable visual artifacts, yet established code benchmarks largely overlook the visual fidelity and interaction behavior that shape user experience. We introduce ArtifactsBench, a benchmark and automated multimodal evaluation framework for visual code generation. The framework executes each artifact and records three staged screenshots{---}before, during, and after a scripted interaction{---}then scores the evidence and source code with a checklist-guided Multimodal LLM (MLLM) referee. ArtifactsBench contains 1,825 tasks across nine domains and evaluates more than 30 LLMs. On a 280-query, six-model expert study, three staged screenshots yield 90.95{\%} referee{--}human Pair ACC. Across the models shared with WebDev Arena, ArtifactsBench reaches 94.4{\%} normalized Footrule consistency with that public human-preference ranking. We release the benchmark, evaluation harness, and baseline results at https://artifactsbenchmark.github.io/."
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<abstract>Large Language Models (LLMs) increasingly generate executable visual artifacts, yet established code benchmarks largely overlook the visual fidelity and interaction behavior that shape user experience. We introduce ArtifactsBench, a benchmark and automated multimodal evaluation framework for visual code generation. The framework executes each artifact and records three staged screenshots—before, during, and after a scripted interaction—then scores the evidence and source code with a checklist-guided Multimodal LLM (MLLM) referee. ArtifactsBench contains 1,825 tasks across nine domains and evaluates more than 30 LLMs. On a 280-query, six-model expert study, three staged screenshots yield 90.95% referee–human Pair ACC. Across the models shared with WebDev Arena, ArtifactsBench reaches 94.4% normalized Footrule consistency with that public human-preference ranking. We release the benchmark, evaluation harness, and baseline results at https://artifactsbenchmark.github.io/.</abstract>
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%0 Conference Proceedings
%T ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code Generation Evaluation
%A Zhang, Chenchen
%A Li, Yuhang
%A Liu, Jiaheng
%A Xu, Can
%A Liu, Ao
%A Zhou, Changzhi
%A Deng, Ken
%A Wu, Dengpeng
%A Huang, Guanhua
%A Li, Kejiao
%A Yi, Qi
%A Lv, Ruilin
%A Hu, Shihui
%A Zhang, Yue
%A Jiang, Yuhao
%A Xu, Zenan
%A Zhang, Yuanxing
%A Zhou, Wiggin
%A Zhou, Bo
%A Lian, Fengzong
%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 zhang-etal-2026-artifactsbench
%X Large Language Models (LLMs) increasingly generate executable visual artifacts, yet established code benchmarks largely overlook the visual fidelity and interaction behavior that shape user experience. We introduce ArtifactsBench, a benchmark and automated multimodal evaluation framework for visual code generation. The framework executes each artifact and records three staged screenshots—before, during, and after a scripted interaction—then scores the evidence and source code with a checklist-guided Multimodal LLM (MLLM) referee. ArtifactsBench contains 1,825 tasks across nine domains and evaluates more than 30 LLMs. On a 280-query, six-model expert study, three staged screenshots yield 90.95% referee–human Pair ACC. Across the models shared with WebDev Arena, ArtifactsBench reaches 94.4% normalized Footrule consistency with that public human-preference ranking. We release the benchmark, evaluation harness, and baseline results at https://artifactsbenchmark.github.io/.
%U https://aclanthology.org/2026.emnlp-main.2229/
%P 50735-50765
Markdown (Informal)
[ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code Generation Evaluation](https://aclanthology.org/2026.emnlp-main.2229/) (Zhang et al., EMNLP 2026)
ACL
- Chenchen Zhang, Yuhang Li, Jiaheng Liu, Can Xu, Ao Liu, Changzhi Zhou, Ken Deng, Dengpeng Wu, Guanhua Huang, Kejiao Li, Qi Yi, Ruilin Lv, Shihui Hu, Yue Zhang, Yuhao Jiang, Zenan Xu, Yuanxing Zhang, Wiggin Zhou, Bo Zhou, and Fengzong Lian. 2026. ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code Generation Evaluation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 50735–50765, Budapest, Hungary. Association for Computational Linguistics.