Diverse genomics data, scientific questions, and analysis tasks typically demand highly specialized visualizations. Therefore, users often must customize or author new ones tailored to their data. Existing tools are usually either limited in customization or require substantial learning or programming, and even expressive tools assume visualization expertise many users lack. Agentic and large language model (LLM) approaches are increasingly applied to complex scientific tasks, including visualization. Natural-language conversational interfaces offer a promising path to democratizing the authoring of complex visualizations. In the context of genomics, these approaches face additional challenges: genomics visualizations typically integrate heterogeneous data types and are composed of multiple linked interactive views. These challenges motivate more structured LLM-based schemes. We first characterize where vanilla LLM generation succeeds and fails for genomics visualization, identifying eight quality dimensions. We then compare six schemes--direct generation, a fixed pipeline, and four agentic configurations varying in the number of specialist agents and the presence of a reviewer--across 159 cases spanning three levels of query ambiguity and specification complexity. All schemes use the Gosling visualization grammar as structured output. Agentic iteration substantially improves perceived quality over both baselines, while more complex agent architectures yield no additional benefit. We discuss implications for designing agentic systems for domain-specific visualization authoring. All supplemental materials are available at https://osf.io/uqe83.

翻译：基因组学数据的多样性、科学问题以及分析任务通常需要高度专业化的可视化手段。因此，用户通常需要定制或创作适合其数据的新可视化方案。现有工具通常要么定制能力有限，要么需要大量的学习或编程，即便功能强大的工具也假设用户具备许多人所缺乏的可视化专业知识。智能体和大型语言模型方法正越来越多地应用于复杂的科学任务，包括可视化。自然语言对话界面为复杂可视化的创作平民化提供了一条有前景的路径。在基因组学背景下，这些方法面临额外挑战：基因组学可视化通常集成异构数据类型，并由多个关联的交互视图组成。这些挑战促使我们采用更结构化的基于大语言模型的方案。我们首先描述了普通大语言模型在基因组学可视化中成功与失败的场景，确定了八个质量维度。随后，我们比较了六种方案——直接生成、固定流程以及四种智能体配置（在专家智能体数量和是否有评审员方面有所变化）——涵盖三种查询模糊性和规范复杂性级别的159个案例。所有方案均使用Gosling可视化语法作为结构化输出。与两种基线相比，智能体迭代显著提高了感知质量，而更复杂的智能体架构并未带来额外收益。我们讨论了为特定领域可视化创作设计智能体系统的启示。所有补充材料可在 https://osf.io/uqe83 获取。