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    This summary is machine-generated.

    VizGenie is a novel framework that uses large language models (LLMs) to automatically generate scientific visualization scripts. This self-improving system enhances data exploration and reproducibility by dynamically adapting to user needs.

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    Area of Science:

    • Scientific Visualization
    • Computational Science
    • Data Analysis

    Background:

    • Traditional scientific visualization tools have limitations in handling complex, high-dimensional data.
    • Manual script generation for advanced visualizations is time-consuming and requires specialized expertise.

    Purpose of the Study:

    • To introduce VizGenie, an agentic framework that leverages large language models (LLMs) to automate and enhance scientific visualization.
    • To enable on-demand generation of visualization scripts and facilitate intuitive, natural language-based data exploration.

    Main Methods:

    • VizGenie orchestrates domain-specific modules and dynamically generates new visualization scripts using LLMs.
    • It employs natural language processing and visual question answering (VQA) for high-level query interpretation.
    • Retrieval-Augmented Generation (RAG) ensures reliability and provenance tracking.

    Main Results:

    • Automated generation and validation of visualization scripts, expanding system capabilities.
    • Significant reduction in cognitive overhead for iterative visualization tasks on complex datasets.
    • Successful interpretation of feature-based queries and interactive exploration via VQA.

    Conclusions:

    • VizGenie offers a sustainable, continuously evolving platform for scientific visualization.
    • It accelerates insight generation and supports reproducible research by integrating LLM flexibility with curated tools.
    • The framework enhances feature-centric exploration and adapts to user interactions.