mlsanityData to 14 Oct 2026

VizGen: Data Exploration and Visualization from Natural Language via a Multi-Agent AI Architecture

arXiv 2509.22218 ↗First posted 26 Sep 2025cs.MA cs.AI cs.DBcited by 0

On the map: LLM agents and tool use → Multimodal and embodied agents

Abstract (arXiv)

Data visualization is essential for interpreting complex datasets, yet traditional tools often require technical expertise, limiting accessibility. VizGen is an AI-assisted graph generation system that empowers users to create meaningful visualizations using natural language. Leveraging advanced NLP and LLMs like Claude 3.7 Sonnet and Gemini 2.0 Flash, it translates user queries into SQL and recommends suitable graph types. Built on a multi-agent architecture, VizGen handles SQL generation, graph creation, customization, and insight extraction. Beyond visualization, it analyzes data for patterns, anomalies, and correlations, and enhances user understanding by providing explanations enriched with contextual information gathered from the internet. The system supports real-time interaction with SQL databases and allows conversational graph refinement, making data analysis intuitive and accessible. VizGen democratizes data visualization by bridging the gap between technical complexity and user-friendly design.

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 7 of its 16 references that are in the map. First posted on arXiv 26 Sep 2025.

Builds on

7 of 16 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025PlotGen: Multi-Agent LLM-based Scientific Data Visualization via Multimodal Feedback3 Feb

Other references · 6

  1. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,416
  2. 2024Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Studycited by 27
  3. 2015Text Understanding from Scratchcited by 25
  4. 2024Large Language Model Enhanced Text-to-SQL Generation: A Surveycited by 24
  5. 2022Natural Language Interfaces to Datacited by 14
  6. 2025Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunitiescited by 4

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025VizGenie: Toward Self-Refining, Domain-Aware Workflows for Next-Generation Scientific Visualization
  2. 2025nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow
  3. 2026Agentic Authoring of Interactive Multiview Visualizations in Genomics
  4. 2026Beyond Static Charts: Can Language and Vision Language Models Generate Interactive Data Visualization Interfaces?
  5. 2025CoDA: Agentic Systems for Collaborative Data Visualization
  6. 2025A2P-Vis: an Analyzer-to-Presenter Agentic Pipeline for Visual Insights Generation and Reporting
  7. 2025DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoning
  8. 2025YAC: Bridging Natural Language and Interactive Visual Exploration with Generative AI for Biomedical Data Discovery