DashboardQA: Benchmarking Multimodal Agents for Question Answering on Interactive Dashboards

🤗Dataset | 🖥️Code | 📄Paper

The abstract of the paper states that:

Dashboards are powerful visualization tools for data-driven decision-making, integrating multiple interactive views that allow users to explore, filter, and navigate data. Unlike static charts, dashboards support rich interactivity, which is essential for uncovering insights in real-world analytical workflows. However, existing question-answering benchmarks for data visualizations largely overlook this interactivity, focusing instead on static charts. This limitation severely constrains their ability to evaluate the capabilities of modern multimodal agents designed for GUI-based reasoning. To address this gap, we introduce DashboardQA, the first benchmark explicitly designed to assess how vision-language GUI agents comprehend and interact with real-world dashboards. The benchmark includes 112 interactive dashboards from Tableau Public and 405 question-answer pairs with interactive dashboards spanning five categories: multiple-choice, factoid, hypothetical, multi-dashboard, and conversational. By assessing a variety of leading closed- and open-source GUI agents, our analysis reveals their key limitations, particularly in grounding dashboard elements, planning interaction trajectories, and performing reasoning. Our findings indicate that interactive dashboard reasoning is a challenging task overall for all the VLMs evaluated. Even the top-performing agents struggle; for instance, the best agent based on Gemini-Pro-2.5 achieves only 38.69% accuracy, while the OpenAI CUA agent reaches just 22.69%, demonstrating the benchmark's significant difficulty.

Evaluation Results

Evaluating New Model

Please follow the evaluation instructions in our github repository: https://github.com/vis-nlp/DashboardQA

Contact

If you have any questions about this work, please contact Ahmed Masry using the following email addresses: amasry17@ku.edu.tr, ahmed.elmasry24653@gmail.com, or masry20@yorku.ca.

Reference

Please cite our paper if you use our model in your research.

@misc{kartha2025dashboardqabenchmarkingmultimodalagents,
      title={DashboardQA: Benchmarking Multimodal Agents for Question Answering on Interactive Dashboards}, 
      author={Aaryaman Kartha and Ahmed Masry and Mohammed Saidul Islam and Thinh Lang and Shadikur Rahman and Ridwan Mahbub and Mizanur Rahman and Mahir Ahmed and Md Rizwan Parvez and Enamul Hoque and Shafiq Joty},
      year={2025},
      eprint={2508.17398},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.17398}, 
}