Bibby AI：用于学术研究、写作与出版的原生编辑器智能体平台

摘要

學術產出的生成過程散落在破碎的工具鏈中：文獻探索使用一個應用、參考文獻管理使用另一個、在 LaTeX 編輯器中撰寫、手動套用會議格式模板，再透過另一個入口網站提交。每個工具之間的銜接都強迫研究者進行上下文切換、格式轉換或手動複製貼上，而這些累積的成本佔據了研究人員花在非研究活動上的大部分時間。我們提出 Bibby AI，這是一個以編輯器為核心的平台，將此工具鏈壓縮成一條以雲端 LaTeX 編輯器為基礎的「研究-撰寫-發表」單一管線。不同於透過瀏覽器擴充功能附掛在既有編輯器上的輔助工具，Bibby AI 掌握完整的文件狀態、編譯管線與修訂歷史，使其智能代理能將基於檢索的引用插入、結構編輯以及符合模板格式的重排，當作一級、可驗證的操作，而非單純的文字建議。該平台整合了：(i) 可將 PDF、DOCX 與手寫數學式轉換為乾淨 LaTeX 的導入管線；(ii) 基於學術元數據的檢索層，該層並納入來自 USPTO PatentsView 與 Marx-Fuegi 引用語料庫的專利-論文引用訊號，以呈現候選參考文獻的轉譯影響力；以及 (iii) 針對文獻篩選、草稿撰寫、修訂與會議格式等任務範圍的智能代理，這些代理直接作用於文件的抽象語法表示上。Bibby AI 已正式上線部署，服務超過 5,000 名活躍研究人員，涵蓋 50 多所簽約大學。我們將說明其架構、以編輯器為核心所帶來的設計決策，以及用來評估該平台相較於破碎基線的工作流程層級時間節省框架。

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.