CoDA: Agentic Systems for Collaborative Data Visualization
Deep research has revolutionized data analysis, yet data scientists still
devote substantial time to manually crafting visualizations, highlighting the
need for robust automation from natural language queries. However, current
systems struggle with complex datasets containing multiple files and iterative
refinement. Existing approaches, including simple single- or multi-agent
systems, often oversimplify the task, focusing on initial query parsing while
failing to robustly manage data complexity, code errors, or final visualization
quality. In this paper, we reframe this challenge as a collaborative
multi-agent problem. We introduce CoDA, a multi-agent system that employs
specialized LLM agents for metadata analysis, task planning, code generation,
and self-reflection. We formalize this pipeline, demonstrating how
metadata-focused analysis bypasses token limits and quality-driven refinement
ensures robustness. Extensive evaluations show CoDA achieves substantial gains
in the overall score, outperforming competitive baselines by up to 41.5%. This
work demonstrates that the future of visualization automation lies not in
isolated code generation but in integrated, collaborative agentic workflows.
68e33f3573e20ab577842038
Zichen Chen
claimed_verified
2025-10-06T12:48:59.817Z
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/avatars/ebe3732d6cc813d7aef1866f1e8757bf.svg
Zichen Chen
user
zcchen
68e33f3573e20ab577842039
Jiefeng Chen
68e33f3573e20ab57784203a
Sercan Ö. Arik
68e33f3573e20ab57784203b
Misha Sra
68e33f3573e20ab57784203c
Tomas Pfister
68e33f3573e20ab57784203d
Jinsung Yoon
CoDA: Agentic Systems for Collaborative Data Visualization
Deep research has revolutionized data analysis, yet data scientists still
devote substantial time to manually crafting visualizations, highlighting the
need for robust automation from natural language queries. However, current
systems struggle with complex datasets containing multiple files and iterative
refinement. Existing approaches, including simple single- or multi-agent
systems, often oversimplify the task, focusing on initial query parsing while
failing to robustly manage data complexity, code errors, or final visualization
quality. In this paper, we reframe this challenge as a collaborative
multi-agent problem. We introduce CoDA, a multi-agent system that employs
specialized LLM agents for metadata analysis, task planning, code generation,
and self-reflection. We formalize this pipeline, demonstrating how
metadata-focused analysis bypasses token limits and quality-driven refinement
ensures robustness. Extensive evaluations show CoDA achieves substantial gains
in the overall score, outperforming competitive baselines by up to 41.5%. This
work demonstrates that the future of visualization automation lies not in
isolated code generation but in integrated, collaborative agentic workflows.
CoDA, a multi-agent system using specialized LLM agents, enhances visualization automation by managing data complexity and ensuring high-quality visualizations through collaborative workflows.
LLM agents metadata analysis task planning code generation self-reflection token limits quality-driven refinement