PaperFit: Vision-in-the-Loop Typesetting Optimization for Scientific Documents
Researchers have introduced PaperFit, a novel vision-in-the-loop agent designed to automate the typesetting optimization of scientific documents. While LaTeX manuscripts may compile without errors, they often suffer from visual defects such as misplaced floats, overflowing equations, and poor page balance, requiring tedious manual corrections. Existing rule-based tools and text-only Large Language Models (LLMs) fail to address these issues effectively because they lack visual verification capabilities. PaperFit formalizes this challenge as Visual Typesetting Optimization (VTO), employing an iterative process that renders pages, diagnoses visual defects using a new five-category taxonomy, and applies constrained source-level repairs. To evaluate this approach, the team constructed PaperFit-Bench, a benchmark dataset comprising 200 papers across various venue templates and defect types. Extensive experiments demonstrate that PaperFit significantly outperforms existing baselines, proving that integrating visual feedback into the editing loop is essential for transforming compilable source code into publication-ready PDFs. This work highlights VTO as a critical, previously missing stage in document automation pipelines, offering a robust solution for researchers aiming to streamline the final formatting of academic papers.
Wire timeline
PaperFit: Vision-in-the-Loop Typesetting Optimization for Scientific Documents
Researchers have introduced PaperFit, a novel vision-in-the-loop agent designed to automate the typesetting optimization of scientific documents. While LaTeX manuscripts may compile without errors, they often suffer from visual defects such as misplaced floats, overflowing equations, and poor page balance, requiring tedious manual corrections. Existing rule-based tools and text-only Large Language Models (LLMs) fail to address these issues effectively because they lack visual verification capabilities. PaperFit formalizes this challenge as Visual Typesetting Optimization (VTO), employing an iterative process that renders pages, diagnoses visual defects using a new five-category taxonomy, and applies constrained source-level repairs. To evaluate this approach, the team constructed PaperFit-Bench, a benchmark dataset comprising 200 papers across various venue templates and defect types. Extensive experiments demonstrate that PaperFit significantly outperforms existing baselines, proving that integrating visual feedback into the editing loop is essential for transforming compilable source code into publication-ready PDFs. This work highlights VTO as a critical, previously missing stage in document automation pipelines, offering a robust solution for researchers aiming to streamline the final formatting of academic papers.
cs.AI updates on arXiv.org