Under the wave of digital transformation, disciplinary inspection and supervision work has also welcomed a "smart brain." On April 21, the first large model in the specialized field of disciplinary inspection and supervision in China, called "Qingjian," developed by Southeast University, was officially unveiled. This achievement marks the deep integration of cutting-edge artificial intelligence technology with national major strategic needs, providing strong technical support for the professionalization and intelligent transformation of disciplinary inspection and supervision work.

"Interdisciplinary Collaboration" Produces Smart Tools

The "Qingjian" model is not the product of a single discipline, but rather the result of in-depth interdisciplinary collaboration. It was jointly developed by the Institute of Disciplinary Inspection and Supervision, School of Law at Southeast University, and the Key Laboratory of New Generation Artificial Intelligence Technology and Interdisciplinary Application, as well as the Laboratory of Future Law and Digital Intelligence Innovation. This "interdisciplinary collaboration" research model ensures that the model has a solid legal foundation and can accurately apply the most advanced AI algorithms.

Five Core Functions Reengineer Operational Efficiency

According to the development team, "Qingjian" has five core application scenarios: authoritative policy interpretation, analysis of integrity risks in typical cases, customized integrity education, academic research data support, and intelligent Q&A for frequent business operations. In practical office work, it can assist staff in intelligently drafting reports, reviewing the integrity of evidence chains, and automatically recommending applicable legal provisions, greatly enhancing the standardization and processing efficiency of disciplinary inspection and supervision practices.

A Precisely Adapted and Secure Technical Foundation

To ensure "professionalism and reliability," "Qingjian" integrates a massive amount of internal party regulations, national laws, academic achievements, and practical case studies. Notably, the model has meticulously annotated local normative documents from all 31 provinces across the country, enabling it to flexibly adapt to differences in disciplinary enforcement and law enforcement across regions.

In terms of technical architecture, the team adopted a full-process technical approach combining "on-premises deployment" and "retrieval-augmented generation," and built a multi-dimensional rule validator that includes the accuracy of legal provisions and logical consistency. This design not only ensures data security and compliance but also guarantees that the model can present clear, rigorous, and logically consistent reasoning chains when dealing with complex case determinations.