DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914514620907520 |
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| author | Meng, Jinxiang Huang, Shaoping Lei, Fangyu Guo, Jingyu Liu, Haoxiang Su, Jiahao Wang, Sihan Wang, Yao Wang, Enrui Yang, Ye Chai, Hongze Lv, Jinming Yu, Anbang Zhang, Huangjing Zhang, Yitong Huang, Yiming Ma, Zeyao He, Shizhu Zhao, Jun Liu, Kang |
| author_facet | Meng, Jinxiang Huang, Shaoping Lei, Fangyu Guo, Jingyu Liu, Haoxiang Su, Jiahao Wang, Sihan Wang, Yao Wang, Enrui Yang, Ye Chai, Hongze Lv, Jinming Yu, Anbang Zhang, Huangjing Zhang, Yitong Huang, Yiming Ma, Zeyao He, Shizhu Zhao, Jun Liu, Kang |
| contents | Real-world data visualization (DV) requires native environmental grounding, cross-platform evolution, and proactive intent alignment. Yet, existing benchmarks often suffer from code-sandbox confinement, single-language creation-only tasks, and assumption of perfect intent. To bridge these gaps, we introduce DV-World, a benchmark of 260 tasks designed to evaluate DV agents across real-world professional lifecycles. DV-World spans three domains: DV-Sheet for native spreadsheet manipulation including chart and dashboard creation as well as diagnostic repair; DV-Evolution for adapting and restructuring reference visual artifacts to fit new data across diverse programming paradigms and DV-Interact for proactive intent alignment with a user simulator that mimics real-world ambiguous requirements. Our hybrid evaluation framework integrates Table-value Alignment for numerical precision and MLLM-as-a-Judge with rubrics for semantic-visual assessment. Experiments reveal that state-of-the-art models achieve less than 50% overall performance, exposing critical deficits in handling the complex challenges of real-world data visualization. DV-World provides a realistic testbed to steer development toward the versatile expertise required in enterprise workflows. Our data and code are available at \href{https://github.com/DA-Open/DV-World}{this project page}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25914 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios Meng, Jinxiang Huang, Shaoping Lei, Fangyu Guo, Jingyu Liu, Haoxiang Su, Jiahao Wang, Sihan Wang, Yao Wang, Enrui Yang, Ye Chai, Hongze Lv, Jinming Yu, Anbang Zhang, Huangjing Zhang, Yitong Huang, Yiming Ma, Zeyao He, Shizhu Zhao, Jun Liu, Kang Computation and Language Real-world data visualization (DV) requires native environmental grounding, cross-platform evolution, and proactive intent alignment. Yet, existing benchmarks often suffer from code-sandbox confinement, single-language creation-only tasks, and assumption of perfect intent. To bridge these gaps, we introduce DV-World, a benchmark of 260 tasks designed to evaluate DV agents across real-world professional lifecycles. DV-World spans three domains: DV-Sheet for native spreadsheet manipulation including chart and dashboard creation as well as diagnostic repair; DV-Evolution for adapting and restructuring reference visual artifacts to fit new data across diverse programming paradigms and DV-Interact for proactive intent alignment with a user simulator that mimics real-world ambiguous requirements. Our hybrid evaluation framework integrates Table-value Alignment for numerical precision and MLLM-as-a-Judge with rubrics for semantic-visual assessment. Experiments reveal that state-of-the-art models achieve less than 50% overall performance, exposing critical deficits in handling the complex challenges of real-world data visualization. DV-World provides a realistic testbed to steer development toward the versatile expertise required in enterprise workflows. Our data and code are available at \href{https://github.com/DA-Open/DV-World}{this project page}. |
| title | DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2604.25914 |