# PaperFit: Vision-in-the-Loop Typesetting Optimization for Scientific Documents

Page: https://stenobird.com/podcast/daily-paper-cast-7079649/paperfit-vision-in-the-loop-typesetting-optimization-for-scientific-documents
Text version: https://stenobird.com/podcast/daily-paper-cast-7079649/paperfit-vision-in-the-loop-typesetting-optimization-for-scientific-documents.md
Podcast: [Daily Paper Cast](https://stenobird.com/podcast/daily-paper-cast-7079649)
Published: 2026-05-13T04:33:08+00:00
Episode link: https://share.transistor.fm/s/2be242df
Audio file: https://media.transistor.fm/2be242df/96913589.mp3
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JSON: https://stenobird.com/v1/public/podcasts/daily-paper-cast-7079649/episodes/paperfit-vision-in-the-loop-typesetting-optimization-for-scientific-documents
Duration seconds: 1375

## Resource

🤗 Upvotes: 29 | cs.AI, cs.SE Authors: Bihui Yu, Xinglong Xu, Junjie Jiang, Jiabei Cheng, Caijun Jia, Siyuan Li, Conghui He, Jingxuan Wei, Cheng Tan Title: PaperFit: Vision-in-the-Loop Typesetting Optimization for Scientific Documents Arxiv: http://arxiv.org/abs/2605.10341v1 Abstract: A LaTeX manuscript that compiles without error is not necessarily publication-ready. The resulting PDFs frequently suffer from misplaced floats, overflowing equations, inconsistent table scaling, widow and orphan lines, and poor page balance, forcing authors into repetitive compile-inspect-edit cycles. Rule-based tools are blind to rendered visuals, operating only on source code and log files. Text-only LLMs perform open-loop text editing, unable to predict or verify the two-dimensional layout consequences of their changes. Reliable typesetting optimization therefore requires a visual closed loop with verification after every edit. We formalize this problem as Visual Typesetting Optimization (VTO), the task of transforming a compilable LaTeX paper into a visually polished, page-budget-compliant PDF through iterative visual verification and source-level revision, and introduce a five-category taxonomy of typesetting defects to guide diagnosis. We present PaperFit, a vision-in-the-loop agent that iteratively renders pages, diagnoses defects, and applies constrained repairs. To benchmark VTO, we construct PaperFit-Bench with 200 papers across 10 venue templates and 13 defect types at different difficulty. Extensive experiments show that PaperFit outperforms all baselines by a large margin, establishing that bridging the gap from compilable source to publication-ready PDF requires vision-in-the-loop optimization and that VTO constitutes a critical missing stage in the document automation pipel…

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