Explainable Iterative Data Visualisation Refinement via an LLM Agent
Exploratory analysis of high-dimensional data relies on embedding the data into a low-dimensional space (typically 2D or 3D), based on which visualization plot is produced to uncover meaningful structures and to communicate geometric and distributional data characteristics. However, finding a suitable algorithm configuration, particularly hyperparameter setting, to produce a visualization plot that faithfully represents the underlying reality and encourages pattern discovery remains challenging. To address this challenge, we propose an agentic AI pipleline that leverages a large language model (LLM) to bridge the gap between rigorous quantitative assessment and qualitative human insight. ...
| Authors | Burak Susam, Tingting Mu |
|---|---|
| Primary category | cs.HC |
| Published | 2 March 2026 |
| Last updated at source | 22 April 2026 |
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