基于 LLM 智能体的可解释迭代式数据可视化优化方法
Explainable Iterative Data Visualization Refinement via an LLM Agent
AI 导读
研究者提出一个 LLM 智能体（Agent）AI 流水线，用于自动优化高维数据嵌入可视化的超参数配置。系统将可视化评估与超参数优化作为语义任务处理，把量化指标与描述性总结结合生成多维度报告，并给出可操作的算法配置建议。通过迭代优化循环，该系统可全自动快速产出高质量可视化图。
正文 · 原文
Abstract:Exploratory analysis of high-dimensional data relies on embedding the data into a low-dimensional space, based on which a visualization plot is produced to uncover meaningful structures and to communicate geometric and distributional data characteristics. Finding a suitable embedding algorithm configuration, particularly the hyperparameter setting, to produce a visualization plot that faithfully represents the underlying reality and encourages pattern discovery is labor-intensive and requires domain knowledge. To ease this burden, we propose an agentic AI pipeline that integrates rigorous quantitative assessment with large language model (LLM) based approximation of qualitative human judgment and insight. By treating visualization evaluation and hyperparameter optimization as a semantic task, our system generates a multi-faceted report that contextualizes quantitative metrics with descriptive summaries, and suggests actionable recommendations of algorithm configuration for refining data visualization. Through an iterative refinement loop, the system rapidly produces a high-quality visualization plot in full automation.
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2604.15319 [cs.HC] |
| (or arXiv:2604.15319v3 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2604.15319 arXiv-issued DOI via DataCite |
Submission history
From: Burak Susam [view email]
[v1]
Mon, 2 Mar 2026 17:51:04 UTC (1,426 KB)
[v2]
Wed, 22 Apr 2026 16:15:41 UTC (1,426 KB)
[v3]
Tue, 10 Nov 2026 07:47:44 UTC (3,055 KB)
来源：arXiv cs.AI · arxiv.org