Paper page - Bibby AI: An Editor-Native Agentic Platform for Academic Research, Writing, and Publishing

Source: https://huggingface.co/papers/2607.05435

Abstract

Bibby AI is an editor-native platform that consolidates the academic writing workflow into a unified Research-Write-Publish pipeline, streamlining literature management, citation insertion, and formatting through integrated tools and agents operating on document syntax representations.

Academic output is produced across a fragmented toolchain: literature discovery in one application, reference management in another, writing in aLaTeX 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 cloudLaTeX editor. Unlike assistants that attach to an existing editor through a browser extension, Bibby AI owns the fulldocument state,compilation pipeline, andrevision history, which allows its agents to performretrieval-grounded citation insertion,structural edits, andtemplate-compliant reformattingas 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 overscholarly metadataenriched withpatent-to-paper citation signalsderived fromUSPTO PatentsViewand theMarx-Fuegi citation corpus, surfacing the translational impact of candidate references; and (iii) task-scoped agents forliterature triage,drafting,revision, andvenue formattingthat operate directly on the document’sabstract 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.

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