DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios
University of the Chinese Academy of Sciences · Institute of automation, Chinese academy of science, Chinese Academy of Sciences · Institute of automation, Chinese academy of science · University of Science and Technology Beijing · National University of Singapore · University of California, San Diego · Renmin University of China & Qwen, Alibaba
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摘要
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. Data and code are available at \url{https://anonymous.4open.science/r/DV-World-50D2}.