Abstract:

Purposes To address the limitations of commercial proofreading software， as well as systematic shortcomings in the current generative artificial intelligence （GAI） workflow， this study developed an agent tailored to copyediting-proofreading needs， in order to enhance the efficiency and quality of editorial work. Methods The agent called Déjà Lu （meaning “already read” in English） was independently developed using Python and JavaScript， and its performance was evaluated through a combination of quantitative analysis and questionnaire-based user surveys. Findings The Déjà Lu agent innovatively realizes automated and intelligent processing of proofreading， polishing， and translation for academic manuscripts， with an average processing speed of about 0.007 seconds per character. The proofreading function improved the error calibration rate by an average of 15%， with overall processing time varying across tasks. The polishing function enhanced language expression quality by an average of 7%， while the total processing time was significantly shortened by up to 92%. The translation function achieved good accuracy， with its overall processing time markedly reduced by up to 83%. Editors and authors generally gave positive evaluations of the agent’s performance. Conclusions The modular， hierarchical， and user-friendly Déjà Lu agent ensures strong maintainability， reusability， and extensibility. It can be effectively integrated into editorial workflows to enhance efficiency， providing a new tool and practical paradigm for the integration of artificial intelligence （AI） and academic publishing.

Key words: Academic publishing, Copyediting-proofreading agent, Modular and hierarchical architecture, Logical rule algorithm, Generative artificial intelligence, Quality enhancement and efficiency improvement

摘要：

目的 针对商业编校软件使用受限以及现有生成式人工智能（GAI）工作流系统性不足的问题，研发面向编校需求的智能体，以提升编校的效率和质量。 方法 研究使用Python和JavaScript等语言自主开发编校智能体“已读（Déjà Lu）”，并结合定量分析法和问卷调查法评估其性能。 结果 “已读”创新性地实现了论文校对、润色与翻译的自动化及智能化处理，平均处理速度约为0.007 s/字。校对功能的错误校准率平均可提高15%，总用时变化不一；润色功能的语言表达质量平均提升7%，总用时显著减少，可达92%；翻译功能的准确度评价良好，总用时显著减少，可达83%。编辑和作者普遍对“已读”的功能表现给予积极评价。 结论 模块化、分层化和用户友好化的“已读”智能体具备良好的维护性、复用性和扩展性，可与编辑实务场景深度融合，提升编辑工作效率，为“人工智能（AI）+出版”的融合发展提供了新的工具与实践范式。

关键词: 学术出版, 编校智能体, 模块化分层架构, 逻辑规则算法, 生成式人工智能, 提质增效