# Agentic Authoring of Interactive Multiview Visualizations in Genomics

arXiv: https://arxiv.org/abs/2606.00370 · HTML: https://mlsanity.com/paper/2606.00370/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 27 of its 51 references that are in the map.
- First posted on arXiv 29 May 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 27 of 27)

- [LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models](https://mlsanity.com/paper/2303.02927/) (2023)
- [ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language](https://mlsanity.com/paper/2311.01920/) (2023)
- [Chat2VIS: Generating Data Visualisations via Natural Language using ChatGPT, Codex and GPT-3 Large Language Models](https://mlsanity.com/paper/2302.02094/) (2023)
- [VisEval: A Benchmark for Data Visualization in the Era of Large Language Models](https://mlsanity.com/paper/2407.00981/) (2024)
- [Collecting and Characterizing Natural Language Utterances for Specifying Data Visualizations](https://mlsanity.com/paper/2110.00680/) (2021)
- [nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow](https://mlsanity.com/paper/2502.05036/) (2025)
- [Natural Language Dataset Generation Framework for Visualizations Powered by Large Language Models](https://mlsanity.com/paper/2309.10245/) (2023)
- [How Good is ChatGPT in Giving Advice on Your Visualization Design?](https://mlsanity.com/paper/2310.09617/) (2023)
- [Are LLMs ready for Visualization?](https://mlsanity.com/paper/2403.06158/) (2024)
- [Prompt4Vis: Prompting Large Language Models with Example Mining and Schema Filtering for Tabular Data Visualization](https://mlsanity.com/paper/2402.07909/) (2024)
- [InterChat: Enhancing Generative Visual Analytics using Multimodal Interactions](https://mlsanity.com/paper/2503.04110/) (2025)
- [Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education](https://mlsanity.com/paper/2407.20570/) (2024)
- [DracoGPT: Extracting Visualization Design Preferences from Large Language Models](https://mlsanity.com/paper/2408.06845/) (2024)
- [Probing the Visualization Literacy of Vision Language Models: the Good, the Bad, and the Ugly](https://mlsanity.com/paper/2504.05445/) (2025)
- [Charts-of-Thought: Enhancing LLM Visualization Literacy Through Structured Data Extraction](https://mlsanity.com/paper/2508.04842/) (2025)
- [Tasks, Techniques, and Tools for Genomic Data Visualization](https://mlsanity.com/paper/1905.02853/) (2019)
- [Draco 2: An Extensible Platform to Model Visualization Design](https://mlsanity.com/paper/2308.14247/) (2023)
- [AuraGenome: An LLM-Powered Framework for On-the-Fly Reusable and Scalable Circular Genome Visualizations](https://mlsanity.com/paper/2507.02877/) (2025)
- [Understanding Why ChatGPT Outperforms Humans in Visualization Design Advice](https://mlsanity.com/paper/2508.01547/) (2025)
- [Too Many Cooks: Exploring How Graphical Perception Studies Influence Visualization Recommendations in Draco](https://mlsanity.com/paper/2308.14241/) (2023)
- [Qualitative Study for LLM-assisted Design Study Process: Strategies, Challenges, and Roles](https://mlsanity.com/paper/2507.10024/) (2025)
- [MultiVis-Agent: A Multi-Agent Framework with Logic Rules for Reliable and Comprehensive Cross-Modal Data Visualization](https://mlsanity.com/paper/2601.18320/) (2026)
- [Understanding the Research-Practice Gap in Visualization Design Guidelines](https://mlsanity.com/paper/2310.09614/) (2023)
- [YAC: Bridging Natural Language and Interactive Visual Exploration with Generative AI for Biomedical Data Discovery](https://mlsanity.com/paper/2509.19182/) (2025)
- [Talk Me Through It: Developing Effective Systems for Chart Authoring](https://mlsanity.com/paper/2601.14707/) (2026)
- [VegaChat: A Robust Framework for LLM-Based Chart Generation and Assessment](https://mlsanity.com/paper/2601.15385/) (2026)
- [Automatic Synthesis of Visualization Design Knowledge Bases](https://mlsanity.com/paper/2601.19237/) (2026)

## Abstract (arXiv)

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.