@inproceedings{kartha-etal-2026-dashboardqa,
title = "{D}ashboard{QA}: Benchmarking Multimodal Agents for Question Answering on Interactive Dashboards",
author = "Kartha, Aaryaman and
Masry, Ahmed and
Islam, Mohammed Saidul and
Lang, Thinh and
Rahman, Shadikur and
Mahbub, Ridwan and
Rahman, Mizanur and
Ahmed, Mahir and
Parvez, Md Rizwan and
Hoque, Enamul and
Joty, Shafiq",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-eacl.177/",
pages = "3385--3407",
ISBN = "979-8-89176-386-9",
abstract = "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 292 tasks on 112 interactive dashboards, encompassing 405 question answer pairs overall. These questions span 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. We release DashboardQA at .."
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<abstract>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 292 tasks on 112 interactive dashboards, encompassing 405 question answer pairs overall. These questions span 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. We release DashboardQA at ..</abstract>
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%0 Conference Proceedings
%T DashboardQA: Benchmarking Multimodal Agents for Question Answering on Interactive Dashboards
%A Kartha, Aaryaman
%A Masry, Ahmed
%A Islam, Mohammed Saidul
%A Lang, Thinh
%A Rahman, Shadikur
%A Mahbub, Ridwan
%A Rahman, Mizanur
%A Ahmed, Mahir
%A Parvez, Md Rizwan
%A Hoque, Enamul
%A Joty, Shafiq
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F kartha-etal-2026-dashboardqa
%X 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 292 tasks on 112 interactive dashboards, encompassing 405 question answer pairs overall. These questions span 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. We release DashboardQA at ..
%U https://aclanthology.org/2026.findings-eacl.177/
%P 3385-3407
Markdown (Informal)
[DashboardQA: Benchmarking Multimodal Agents for Question Answering on Interactive Dashboards](https://aclanthology.org/2026.findings-eacl.177/) (Kartha et al., Findings 2026)
ACL
- Aaryaman Kartha, Ahmed Masry, Mohammed Saidul Islam, Thinh Lang, Shadikur Rahman, Ridwan Mahbub, Mizanur Rahman, Mahir Ahmed, Md Rizwan Parvez, Enamul Hoque, and Shafiq Joty. 2026. DashboardQA: Benchmarking Multimodal Agents for Question Answering on Interactive Dashboards. In Findings of the Association for Computational Linguistics: EACL 2026, pages 3385–3407, Rabat, Morocco. Association for Computational Linguistics.