@inproceedings{meng-etal-2026-webrise,
title = "{W}eb{RISE}: Requirement-Induced State Evaluation for {MLLM}-Generated Web Artifacts",
author = "Meng, Yuxin and
Suo, Yuhan and
Wang, Junjie and
Sun, Yuhan and
Yu, Yiyao and
Zhang, Ruixu and
Hu, Ruining and
Wang, Yubin and
Ruan, Shouwei and
Wang, Bin and
Zhang, Yuxiang and
Yang, Yujiu",
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.537/",
pages = "12360--12385",
ISBN = "979-8-89176-459-0",
abstract = "Existing benchmarks for MLLM-generated web artifacts assess interaction through local evidence and miss the requirement-induced states and transitions that determine whether a page works. We introduce WebRISE, which compiles task requirements into Interaction Contract Graphs (ICGs) of observable states, user-intent transitions, and DOM/visual assertions for implementation-agnostic browser execution. WebRISE spans 442 tasks across five input modalities (Text, Markdown, Sketch, Image, Video), with $5{,}495$ transitions and $5{,}271$ requirement checks that separate user-stated functions from implicit product-level constraints. Across 14 MLLMs, even the strongest model reaches only 65.6{\%} transition validity and 66.3{\%} requirement coverage, and visual quality is no proxy for behavior (Qwen3.6-35B-A3B on Markdown: $V{=}80.8$ yet $T{=}15.5$). Video gives the strongest interaction signal ($+10.6$pp implicit coverage over Text), while implicit constraints persist; defect injection shows ICG-based scoring detects state errors at 2{--}$16\times$ the rate of checkpoint-style evaluation."
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<abstract>Existing benchmarks for MLLM-generated web artifacts assess interaction through local evidence and miss the requirement-induced states and transitions that determine whether a page works. We introduce WebRISE, which compiles task requirements into Interaction Contract Graphs (ICGs) of observable states, user-intent transitions, and DOM/visual assertions for implementation-agnostic browser execution. WebRISE spans 442 tasks across five input modalities (Text, Markdown, Sketch, Image, Video), with 5,495 transitions and 5,271 requirement checks that separate user-stated functions from implicit product-level constraints. Across 14 MLLMs, even the strongest model reaches only 65.6% transition validity and 66.3% requirement coverage, and visual quality is no proxy for behavior (Qwen3.6-35B-A3B on Markdown: V=80.8 yet T=15.5). Video gives the strongest interaction signal (+10.6pp implicit coverage over Text), while implicit constraints persist; defect injection shows ICG-based scoring detects state errors at 2–16\times the rate of checkpoint-style evaluation.</abstract>
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%0 Conference Proceedings
%T WebRISE: Requirement-Induced State Evaluation for MLLM-Generated Web Artifacts
%A Meng, Yuxin
%A Suo, Yuhan
%A Wang, Junjie
%A Sun, Yuhan
%A Yu, Yiyao
%A Zhang, Ruixu
%A Hu, Ruining
%A Wang, Yubin
%A Ruan, Shouwei
%A Wang, Bin
%A Zhang, Yuxiang
%A Yang, Yujiu
%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 meng-etal-2026-webrise
%X Existing benchmarks for MLLM-generated web artifacts assess interaction through local evidence and miss the requirement-induced states and transitions that determine whether a page works. We introduce WebRISE, which compiles task requirements into Interaction Contract Graphs (ICGs) of observable states, user-intent transitions, and DOM/visual assertions for implementation-agnostic browser execution. WebRISE spans 442 tasks across five input modalities (Text, Markdown, Sketch, Image, Video), with 5,495 transitions and 5,271 requirement checks that separate user-stated functions from implicit product-level constraints. Across 14 MLLMs, even the strongest model reaches only 65.6% transition validity and 66.3% requirement coverage, and visual quality is no proxy for behavior (Qwen3.6-35B-A3B on Markdown: V=80.8 yet T=15.5). Video gives the strongest interaction signal (+10.6pp implicit coverage over Text), while implicit constraints persist; defect injection shows ICG-based scoring detects state errors at 2–16\times the rate of checkpoint-style evaluation.
%U https://aclanthology.org/2026.emnlp-main.537/
%P 12360-12385
Markdown (Informal)
[WebRISE: Requirement-Induced State Evaluation for MLLM-Generated Web Artifacts](https://aclanthology.org/2026.emnlp-main.537/) (Meng et al., EMNLP 2026)
ACL
- Yuxin Meng, Yuhan Suo, Junjie Wang, Yuhan Sun, Yiyao Yu, Ruixu Zhang, Ruining Hu, Yubin Wang, Shouwei Ruan, Bin Wang, Yuxiang Zhang, and Yujiu Yang. 2026. WebRISE: Requirement-Induced State Evaluation for MLLM-Generated Web Artifacts. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 12360–12385, Budapest, Hungary. Association for Computational Linguistics.