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2026 · Conference paper

IWR-Bench: Can LVLMs reconstruct interactive webpage from a user interaction video?

Yang Chen; Minghao Liu; Yufan Shen; Yunwen Li; Tianyuan Huang; Xinyu Fang; Tianyu Zheng; Wenxuan Huang; Cheng Yang; Daocheng Fu; Jianbiao Mei; Rong Wu; Licheng Wen; Xuemeng Yang; Song Mao; Qunshu Lin; Zhi Yu; Yongliang Shen; Yu Qiao; Botian Shi

Tests reconstruction of interactive webpages from user-interaction videos and supplied static assets. Its 113 tasks replay annotated action sequences to assess visual fidelity and functional behavior across observed page states.

Scope and limitations

The required output is one HTML file; the paper explicitly instructs models to mock implied backend logic with static JavaScript data. Trajectory coverage and model-based judgments do not prove complete website or backend equivalence.

Why it fits this collection

Provides a direct behavioral reconstruction benchmark beyond static screenshots while making the frontend/backend boundary explicit.

Publication and review details

Venue
ICLR 2026
Status
Conference paper
Publication / posting date
2026 (exact date not verified)
Date source
Publication year stated by the official ICLR proceedings; no finer publication date is asserted.
First arXiv submission
September 29, 2025
arXiv
2509.24709
Reviewed arXiv version
v3

Authors and order follow the published PDF: 20 authors. The proceedings landing page orders some middle authors differently; arXiv v3 and the project page list 21, additionally including Yunfei Zhao. The 2025 preprint date is separate from ICLR 2026 publication.

Sources reviewed

Source review only; experiments were not rerun for this collection. Cite the original authors using the linked paper or BibTeX.

Primary sources

  1. https://proceedings.iclr.cc/paper_files/paper/2026/hash/55bfedfd31489e5ae83c9ce8eec7b0e1-Abstract-Conference.html
  2. https://proceedings.iclr.cc/paper_files/paper/2026/file/55bfedfd31489e5ae83c9ce8eec7b0e1-Paper-Conference.pdf
  3. https://arxiv.org/abs/2509.24709
  4. https://github.com/SIGMME/IWR-Bench
  5. https://sigmme.github.io/IWR-Bench/
  6. https://huggingface.co/datasets/IWR-Bench/IWR-Bench