English(EN) RelAgent: LLM Agents as Data Scientists for Relational Learning

LLM智能体RelAgent作为关系学习的自主数据科学家

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-22 04:00

研究人员推出RelAgent，这是一个基于LLM的自主数据科学家，专为关系学习任务设计。该系统分两个阶段运行：搜索阶段，LLM智能体使用各种工具构建SQL特征程序并选择预测模型；推理阶段，生成的程序在没有进一步LLM干预的情况下执行。最终的预测器由SQL查询和经典模型组成，提供快速、确定且可解释的预测，可在标准数据库系统上部署。 AI

影响这种方法可以通过自动化关系数据集的特征工程和模型选择来简化复杂的数据科学工作流程。

排序理由该集群包含一篇学术论文，详细介绍了使用LLM智能体进行关系学习的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 1 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇学术论文，详细介绍了使用LLM智能体进行关系学习的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 1 days old; Coverage has settled into its steady-state source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Xingyue Huang, Louis Tichelman, Jinwoo Kim, Krzysztof Olejniczak, \.Ismail \.Ilkan Ceylan · 2026-10-22 04:00

RelAgent: LLM Agents as Data Scientists for Relational Learning

arXiv:2605.07840v2 Announce Type: replace Abstract: Relational learning is a challenging problem that has motivated a wide range of approaches, including graph-based models (e.g., graph neural networks, graph transformers), tabular methods (e.g., tabular foundation models), and s…

本文如何被排名

报道来源 [1]

RelAgent: LLM Agents as Data Scientists for Relational Learning

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