让企业数据智能分析自动运行，还能持续进化。
QwenPaw-Data: Bridging Facts, Methodology, and Execution for Autonomous Enterprise Data Analytics
- 用三个子系统整合数据、方法和执行流程
- 在真实业务场景中提升数据获取与分析质量
- 适合需要可追溯、自进化数据智能的团队
企业数据分析正成为自主智能体的新前沿。相比通用交互与软件工程，它处于开放、模糊且持续演化的环境中，亟需将语义、方法、执行与演化作为核心系统设计。为此，我们提出 QwenPaw-Data，一个面向企业智能数据分析的代理式数据系统。该系统将数据仓库、看板、文档、交互日志及历史任务等异构资产，整合为可复用、可管理、可演进的分析资产，并将自然语言请求转化为涵盖数据理解、检索、分析、报告生成与决策支持的端到端工作流。其架构分解为三个协同子系统：DataBridge 通过互联的元数据、知识与追踪图实现可信语义定位；Skill-Hub 将专家分析方法编码为可复用、可验证的技能；Host 则将这些证据与方法资产转化为可控、以产物为中心的运行时执行。各子系统间持续回流语义、方法、追踪与反馈，形成自我演进的资产飞轮。在公开基准与真实工业商业智能负载上的实验表明，QwenPaw-Data 显著提升了可验证的数据访问能力与更高层次的分析质量，为企业数据智能体提供了可靠、可追溯、持续优化的实践基础。
原文摘要 · Abstract (English)
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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