{"ID":3005011,"CreatedAt":"2026-06-03T03:09:48.883664427Z","UpdatedAt":"2026-06-04T19:14:31.964469513Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.03220","arxiv_id":"2606.03220","title":"WebRISE: Requirement-Induced State Evaluation for MLLM-Generated Web Artifacts","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 2-16x the rate of checkpoint-style evaluation.","short_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 tra...","url_abs":"https://arxiv.org/abs/2606.03220","url_pdf":"https://arxiv.org/pdf/2606.03220v1","authors":"[\"Yuxin Meng\",\"Yuhan Suo\",\"Junjie Wang\",\"Yuhan Sun\",\"Yiyao Yu\",\"Ruixu Zhang\",\"Ruining Hu\",\"Yubin Wang\",\"Shouwei Ruan\",\"Bin Wang\",\"Yuxiang Zhang\",\"Yujiu Yang\"]","published":"2026-06-02T06:29:40Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[\"Large Language Model\"]","has_code":false}