DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios
Authors: name: Jinxiang Meng, name: Shaoping Huang, name: Fangyu Lei, name: Jingyu Guo, name: Haoxiang Liu, name: Jiahao Su, name: Sihan Wang, name: Yao Wang, name: Enrui Wang, name: Ye Yang, name: Hongze Chai, name: Jinming Lv, name: Anbang Yu, name: Huangjing Zhang, name: Yitong Zhang, name: Yiming Huang, name: Zeyao Ma, name: Shizhu He, name: Jun Zhao, name: Kang Liu

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 https://github.com/DA-Open/DV-World{this project page}.