Paper Detail
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
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.
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@article{zeng2026qwenpaw,
title = {QwenPaw-Data: Bridging Facts, Methodology, and Execution for Autonomous Enterprise Data Analytics},
author = {Tianjing Zeng and Yuntao Hong and Zhongjun Ding and Dandan Liu and Yinan Mei and Yunxiang Su and Yiming Wang and Xiaojian Zhang and Jingyu Zhu and Junhao Zhu and Zhuowen Liang and Jiazhen Peng and Lianggui Weng and Zhihao Ding and Kerui Yi and Qifeng Wang and Rong Zhu and Bolin Ding and Liyu Mou and Jingren Zhou},
year = {2026},
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-Dat},
url = {https://arxiv.org/abs/2607.11019},
keywords = {cs.AI},
eprint = {2607.11019},
archiveprefix = {arXiv},
}
{}