# Dashboard2Code: Evaluating Multimodal Models on Reconstructing Interactive Dashboards

> Evaluates agents that actively explore a running dashboard and reconstruct its appearance and interaction logic. DashboardMimic provides 180 Plotly+Dash examples, with code-semantic analysis and dynamic interaction tests for the generated implementations.

- Authors: Tianhao Niu; Ziyu Han; Qiguang Chen; Shiqi Zhou; Baocai Shan; Hengjie Fang; Qingfu Zhu; Wanxiang Che
- Venue: ACL 2026
- Status: Conference paper
- Publication / posting date: July 2026
- Date source: ACL proceedings publication month.
- First arXiv submission: July 6, 2026
- arXiv: 2607.04727
- Reviewed arXiv version: v1
- DOI: 10.18653/v1/2026.acl-long.1750
- Sources reviewed: 2026-10-16
- Added to the catalog: 2026-10-16
- Original paper: <https://aclanthology.org/2026.acl-long.1750/>
- PDF: <https://aclanthology.org/2026.acl-long.1750.pdf>
- Code: <https://github.com/nth2000/Dashboard2Code>
- BibTeX: <https://awesomerea.org/papers/citations/paper-dashboard2code.bib>
- Catalog page: <https://awesomerea.org/papers/paper-dashboard2code/>
- Record JSON: <https://awesomerea.org/papers/paper-dashboard2code/record.json>
- Chinese version: [简体中文](https://awesomerea.org/zh/papers/paper-dashboard2code/index.md)

## Scope and limitations

Limited to Plotly+Dash dashboards, including model-augmented examples. Some settings expose DOM information and evaluation uses model judges. This is not a general backend-recovery benchmark. The linked dataset requires accepting access conditions and sharing contact information.

## Why it fits this collection

Directly studies reconstruction through active observation, including callbacks and state dependencies rather than screenshot appearance alone.

## Publication and review notes

The paper links the public code repository directly. Only the public dataset access notice was inspected; gated files were not accessed. Publication metadata follows the final ACL record.

## Primary sources

1. <https://aclanthology.org/2026.acl-long.1750/>
2. <https://aclanthology.org/2026.acl-long.1750.pdf>
3. <https://arxiv.org/abs/2607.04727>
4. <https://arxiv.org/html/2607.04727v1>
5. <https://github.com/nth2000/Dashboard2Code>
6. <https://github.com/nth2000/Dashboard2Code/blob/284e154f775af798b843ca4eae26057d7fcf3d21/README.md>
7. <https://huggingface.co/datasets/nth2000/Dashboard2Code>

## About this entry

Reviewed against the paper and its primary sources. Cite the original authors using the linked paper or BibTeX.
Original editorial content: MIT. Linked projects, papers and assets retain their own rights and licenses.