The October 2025 paper introduces **CoDA (Collaborative Data-visualization Agents)**, a novel multi-agent system designed to automate complex data visualization from natural language queries, addressing the limitations of existing rule-based and single Large Language Model (LLM) approaches. The core innovation of CoDA is its **collaborative paradigm**, where specialized LLM agents—focused on tasks like query analysis, data processing, design mapping, and self-reflection—work together through an **iterative refinement loop** to enhance output quality and robustness. Experimental results demonstrate that CoDA significantly **outperforms state-of-the-art baselines** (MatplotAgent, VisPath, CoML4VIS) on benchmarks like MatplotBench and Qwen Code Interpreter, achieving superior execution pass rates and visualization success rates, particularly when dealing with complex queries, multi-file data, and specific stylistic constraints. Ablation studies further validate the necessity of CoDA’s architectural components, such as the **Global TODO List** and the **Search Agent**, confirming that structured planning and external knowledge retrieval are crucial for overcoming ambiguity and ensuring high-fidelity code generation. The paper concludes that this agentic approach transforms visualization generation into a more **resilient and adaptive problem-solving process**, making it effective for real-world data science tasks.

Source:

https://arxiv.org/pdf/2510.03194