titleWebRISE: Requirement-Induced State Evaluation for MLLM-Generated Web Artifacts
authorsYuxin Meng, Yuhan Suo, Junjie Wang, Yuhan Sun, Yiyao Yu, Ruixu Zhang, Ruining Hu, Yubin Wang, Shouwei Ruan, Bin Wang, Yuxiang Zhang, Yujiu Yang
date2026-06-02
venuearXiv (preprint, not peer reviewed)
idarXiv:2606.03220
affiliationTsinghua IIGroup
upvotes10 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf code
abstractExisting 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 2-16x the rate of checkpoint-style evaluation.

cite

@misc{meng2026webrise,
  title={WebRISE: Requirement-Induced State Evaluation for MLLM-Generated Web Artifacts},
  author={Yuxin Meng and Yuhan Suo and Junjie Wang and Yuhan Sun and Yiyao Yu and Ruixu Zhang and Ruining Hu and Yubin Wang and Shouwei Ruan and Bin Wang and Yuxiang Zhang and Yujiu Yang},
  year={2026},
  eprint={2606.03220},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2606.03220}
}