{
  "id": 24,
  "url": "https://arxiv.org/abs/2607.11019v1",
  "title": "QwenPaw-Data: Bridging Facts, Methodology, and Execution for Autonomous Enterprise Data Analytics",
  "summary": "Enterprise data analysis is emerging as a distinct frontier for autonomous agents. Compared with general-purpose interaction and software engineering, it operates in an open, ambiguous, and continuously evolving environment. These characteristics call for a data-agent architecture that treats semantics, methodology, execution, and evolution as first-class system concerns. To this end, we introduce QwenPaw-Data, an agentic data system designed for enterprise intelligent data analysis. QwenPaw-Dat",
  "authors": "Tianjing Zeng, Yuntao Hong, Zhongjun Ding, Dandan Liu, Yinan Mei, Yunxiang Su et al.",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-13T02:38:00.000Z",
  "fetched_at": "2026-07-14T14:14:15.663Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/24",
  "original_url": "https://arxiv.org/abs/2607.11019v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}