{"ID":6538295,"CreatedAt":"2026-07-14T02:54:43.516908796Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.11019","arxiv_id":"2607.11019","title":"QwenPaw-Data: Bridging Facts, Methodology, and Execution for Autonomous Enterprise Data Analytics","abstract":"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-Data consolidates heterogeneous assets from warehouses, dashboards, documents, interaction logs, and historical tasks into reusable, governable, and evolvable analysis assets, then turns natural-language requests into end-to-end analytical workflows spanning data understanding, retrieval, analysis, report generation, and decision support. Its architecture decomposes the problem into three collaborative subsystems: DataBridge provides trustworthy semantic grounding through interconnected metadata, knowledge, and trace graphs; Skill-Hub codifies expert analytical methodology into reusable and verifiable skills; and Host materializes these evidence and method assets into controllable, artifact-centric runtime execution. Across these subsystems, semantics, methods, traces, and feedback are continuously deposited back into the system, forming a self-evolving asset flywheel. Experiments on public benchmarks and real-world industrial BI workloads show that QwenPaw-Data improves both verifiable data access capability and higher-level analytical quality, offering a practical foundation for reliable, traceable, and continuously improving enterprise data agents.","short_abstract":"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, ex...","url_abs":"https://arxiv.org/abs/2607.11019","url_pdf":"https://arxiv.org/pdf/2607.11019v1","authors":"[\"Tianjing Zeng\",\"Yuntao Hong\",\"Zhongjun Ding\",\"Dandan Liu\",\"Yinan Mei\",\"Yunxiang Su\",\"Yiming Wang\",\"Xiaojian Zhang\",\"Jingyu Zhu\",\"Junhao Zhu\",\"Zhuowen Liang\",\"Jiazhen Peng\",\"Lianggui Weng\",\"Zhihao Ding\",\"Kerui Yi\",\"Qifeng Wang\",\"Rong Zhu\",\"Bolin Ding\",\"Liyu Mou\",\"Jingren Zhou\"]","published":"2026-07-13T02:38:00Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[]","has_code":false}