{
  "id": 336,
  "url": "https://arxiv.org/abs/2607.01647v1",
  "title": "AgenticDataBench: A Comprehensive Benchmark for Data Agents",
  "summary": "Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society. Automating this process is essential to reducing labor-intensive efforts for data scientists and enabling scalable data-driven applications. Recently, large language model (LLM)-based data agents have emerged as a promising solution to automate data science workflows. However, the field lacks comprehensive benchmarks to rigorously evaluate t",
  "authors": "Zhaoyan Sun, Shan Zhong, Daizhou Wen, Jiaxing Han, Guoliang Li, Ying Yan et al.",
  "category": "research",
  "topics": "jobs-economy,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-02T03:18:59.000Z",
  "fetched_at": "2026-07-14T14:14:28.435Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/336",
  "original_url": "https://arxiv.org/abs/2607.01647v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}