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DV-World benchmark reveals AI data visualization agents struggle with real-world tasks

Researchers have introduced DV-World, a new benchmark comprising 260 tasks designed to evaluate data visualization agents in realistic professional settings. This benchmark addresses limitations of existing tools by incorporating native environmental grounding, cross-platform adaptation, and proactive intent alignment, moving beyond confined code-sandbox environments. Experiments using DV-World show that current state-of-the-art models perform below 50% overall, highlighting significant deficiencies in handling complex real-world data visualization challenges. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT This benchmark aims to drive development of more capable data visualization agents for enterprise workflows.

RANK_REASON The cluster describes a new academic paper introducing a benchmark for evaluating AI agents.

Read on arXiv cs.CL →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 · Jinxiang Meng, Shaoping Huang, Fangyu Lei, Jingyu Guo, Haoxiang Liu, Jiahao Su, Sihan Wang, Yao Wang, Enrui Wang, Ye Yang, Hongze Chai, Jinming Lv, Anbang Yu, Huangjing Zhang, Yitong Zhang, Yiming Huang, Zeyao Ma, Shizhu He, Jun Zhao, Kang Liu ·

    DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios

    arXiv:2604.25914v1 Announce Type: new Abstract: 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…

  2. arXiv cs.CL TIER_1 · Kang Liu ·

    DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios

    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 bri…