LilanOvO

LilanOvO / Auto-Resubmit

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🚀 Automated & lossless LaTeX paper migration tool. Instantly convert your Overleaf source between top-tier AI conference templates (NeurIPS, ICLR, ACL等). 一键无损转换顶会论文格式！解决转投时的排版折磨，完美保留公式、图表与引用，让科研人员专注研究而非排版。

49
0
100% credibility
Found Apr 07, 2026 at 49 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A helper tool that takes your academic paper files and a new conference style guide to automatically rebuild and package your paper in the fresh format without losing any content.

How It Works

1
📄 Paper Rejection Blues

Your research paper gets turned down by one conference, but you spot a helpful tool to quickly adapt it for another.

2
🛠️ Get the Helper Ready

Download the simple tool and set it up on your computer so it's ready to assist with your paper makeover.

3
📦 Gather Your Files

Collect your paper's files into a bundle and grab the new conference's style guide bundle.

4
✨ Start the Conversion

Feed both bundles to the tool and watch it pull out your content, reshape it to fit perfectly, and create a fresh version.

5
⏳ It Works Quietly

Give it a minute to rearrange everything smoothly while you grab coffee.

🎉 Ready to Resubmit

Enjoy your new complete package with the updated paper, a polished preview printout, and notes on what changed – all set for the next conference!

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Star Growth

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AI-Generated Review

What is Auto-Resubmit?

Auto-Resubmit is a Python CLI tool for automated, lossless LaTeX migration between top AI conference templates like NeurIPS, ICLR, ACL, ICML, CVPR, and AAAI. Point it at your source paper zip from Overleaf and a target template zip via `auto_resubmit run --source-zip input.zip --target-template-zip template.zip --output-dir out/`, and it instantly converts content—title, abstract, body, formulas, figures, bib—into a compiled PDF and submission-ready zip. It eliminates manual preamble tweaks and reformatting drudgery for resubmissions.

Why is it gaining traction?

Unlike manual copy-pasting, it preserves everything offline with tectonic compilation, outputting manifests and audits for verification. Supports mutual conversion across families, handles assets automatically, and flags warnings—saving hours per paper. With 49 stars, it's hooking researchers via quick CLI wins over brittle scripts.

Who should use this?

AI/ML academics resubmitting NeurIPS rejects to ICLR or ACL, computer vision folks shifting CVPR to ICCV papers. Ideal for PhD students or profs juggling conference deadlines without deep LaTeX skills.

Verdict

Solid early MVP (49 stars, 1.0% credibility score) with clear docs and offline reliability—test on a real resubmission, but double-check compiles. Great starter for automated conference workflows; contribute to mature it.

(178 words)

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