Bibby AI：一个面向学术研究、写作与出版的编辑器原生智能代理平台

摘要

学术产出依赖一套分散的工具链完成：文献发现用一个应用，参考文献管理用另一个，写作在LaTeX编辑器中进行，手动按会议模板排版，最后通过另一个门户提交。工具之间的每一次切换都迫使研究者进行上下文转换、格式转换或手动复制粘贴，而这些累积成本占据了研究人员本应用于研究本身的大量时间。我们提出Bibby AI——一个以编辑器为核心的平台，它将这套工具链整合为围绕云端LaTeX编辑器构建的"研究-写作-发表"一体化流水线。与通过浏览器扩展附着于现有编辑器的辅助工具不同，Bibby AI拥有完整的文档状态、编译流水线和修订历史，这使得其智能体能够将基于检索的引文插入、结构化编辑以及符合模板要求的重新排版作为独立、可验证的操作来执行，而非仅仅作为文本建议。该平台集成了：（i）将PDF、DOCX及手写数学公式转换为干净LaTeX的摄入流水线；（ii）基于学术元数据的检索层，该元数据融入了来自USPTO PatentsView及Marx-Fuegi引文语料库的专利-论文引用信号，从而揭示候选参考文献的转化影响力；（iii）面向文献筛选、草稿撰写、修订及会议格式排版的任务型智能体，这些智能体直接在文档的抽象语法表示上操作。Bibby AI已投入生产环境，服务于50余所订阅高校的5000多名活跃研究者。本文描述其架构、编辑器原生特性所催生的设计决策，以及用于将平台与分散式基线方案进行对比评估的工作流级时间节省框架。

English

Academic output is produced across a fragmented toolchain: literature discovery in one application, reference management in another, writing in a LaTeX editor, formatting against venue templates by hand, and submission through yet another portal. Each boundary between tools forces a context switch, a format conversion, or a manual copy-paste step, and the cumulative cost dominates the time researchers spend on activities that are not research. We present Bibby AI, an editor-native platform that collapses this toolchain into a single Research-Write-Publish pipeline built around a cloud LaTeX editor. Unlike assistants that attach to an existing editor through a browser extension, Bibby AI owns the full document state, compilation pipeline, and revision history, which allows its agents to perform retrieval-grounded citation insertion, structural edits, and template-compliant reformatting as first-class, verifiable operations rather than text suggestions. The platform integrates (i) ingestion pipelines that convert PDF, DOCX, and handwritten mathematics into clean LaTeX; (ii) a retrieval layer over scholarly metadata enriched with patent-to-paper citation signals derived from USPTO PatentsView and the Marx-Fuegi citation corpus, surfacing the translational impact of candidate references; and (iii) task-scoped agents for literature triage, drafting, revision, and venue formatting that operate directly on the document's abstract syntax representation. Bibby AI is deployed in production and serves more than 5,000 active researchers across more than 50 subscribing universities. We describe the architecture, the design decisions that editor-nativeness makes possible, and the workflow-level time-savings framework we use to evaluate the platform against fragmented baselines.