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English(EN) Explainable Iterative Data Visualization Refinement via an LLM Agent

AI代理自动化数据可视化算法设计与精炼

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了代理式AI流程，以自动化数据可视化算法的设计和精炼。例如，AutoManifold使用LLM编排，从基本组件组合新的流形嵌入算法，并根据用户指定的结构优先级进行定制，其性能优于t-SNE和UMAP等传统方法。另一个系统将基于LLM的定性判断与定量评估相结合，以迭代地精炼可视化图表，提供解释指标并建议配置改进以更好地发现模式的报告。 AI

影响 这些代理式系统可以显著加快研究人员和分析师的数据探索和洞察发现过程。

排序理由 该集群包含两篇arXiv论文，详细介绍了AI驱动的数据可视化新研究。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

AI代理自动化数据可视化算法设计与精炼

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该集群包含两篇arXiv论文，详细介绍了AI驱动的数据可视化新研究。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Burak Susam, Ruiyuan Kang, Tingting Mu ·

    AutoManifold：通过流形嵌入实现数据可视化算法的智能体设计

    arXiv:2611.13710v1 Announce Type: new Abstract: Manifold embedding for data visualisation is dominated by a small set of hand-designed state-of-the-art (SOTA) algorithms. Their design targets are fixed at development time, resulting in inflexible algorithmic behaviour that cannot…

  2. arXiv cs.AI TIER_1 English(EN) · Burak Susam, Tingting Mu ·

    通过LLM代理实现可解释的迭代数据可视化精炼

    arXiv:2604.15319v3 Announce Type: replace-cross 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 dis…

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