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

TL;DR

PaperFit introduces a vision-in-the-loop system for optimizing LaTeX documents, enabling iterative visual verification and correction to produce publication-ready PDFs, addressing a critical gap in document automation.

Contribution

The paper formalizes Visual Typesetting Optimization (VTO) and presents PaperFit, a novel agent that iteratively diagnoses and repairs layout issues using visual feedback, outperforming baselines.

Findings

PaperFit significantly outperforms baseline methods in VTO tasks.

Constructed PaperFit-Bench with 200 papers across multiple templates and defect types.

Bridging source code and visual layout is essential for publication-ready document automation.

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 pipeline.