aiscientists-dev/academic-humanizer

     

我们为什么要建造这一切

我们中有些人写了很多论文和资助申请，我们的团队开始用AI来辅助 草稿。问题在于，AI辅助的草稿往往泛泛而谈，内容多是“近年来…” 开头、夸张的措辞和过长的句子。它们也偏离了作者自己的声音， 失去精细奖学金取决于具体情况。

有些工具叫做“人性化工具”，但它们是为博客和营销设计的。在纸上运行一个，或者 NSF提案，它会让精确度和其他一切一同平淡化。学术界的谨慎措辞 写作依赖的是第一个被淘汰的东西。

于是我们自己整理了一份。为了校准，我们让AI将自己的草稿与我们团队的草稿进行比较 接受了论文和资助的提案，我们手工逐一分析了差异。这没什么花哨的， 这不是关于游戏评测、破解侦测器或添加虚假新奇。我们想要的是AI辅助 需要清晰阅读，并以作者自己的声音阅读，并附上编号、引用和声明 完全按照原文写的。

伦理与披露

这是一个编辑辅助工具，旨在提升清晰度和表达方式，并根据作者之前被认可的作品进行校准。

Why we built this

Some of us write a lot of papers and grant proposals, and our team started using AI to help with drafts. The problem is that AI-assisted drafts come out generic and verbose, with “In recent years…” openers, inflated phrasing, and over-long sentences. They also drift from the author’s own voice and lose the precision scholarship depends on.

There are tools called “humanizers,” but they are built for blogs and marketing. Run one on a paper or an NSF proposal and it flattens the precision along with everything else. The careful wording academic writing depends on is the first thing to go.

So we put together our own. To calibrate it, we had the AI compare its own drafts with our team’s accepted papers and funded proposals, and we went through the differences by hand. It is nothing fancy, and it is not about gaming review, defeating detectors, or adding fake novelty. We wanted AI-assisted drafts to read clearly and in the author’s own voice, with the numbers, citations, and claims left exactly as written.

Ethics and disclosure

This is an editing aid for clarity and voice, calibrated to an author’s own prior accepted work. It does not generate findings, invent data, or change citations, and it is not designed to evade AI-use detection. Using it does not remove your obligation to disclose AI assistance: always follow the disclosure policy of the venue you submit to.

See it work

[!CAUTION] Before (a generic AI draft):

In recent years, continual learning has attracted increasing attention and achieved remarkable success. However, existing methods still face crucial challenges. In this proposal, we propose a novel framework that leverages cutting-edge techniques to delve into these intricate problems, paving the way for a transformative paradigm that will revolutionize the field.

[!TIP] After (clear, in the author’s voice, with claims tied to evidence):

Continual learning matters, but today’s methods stay empirical and their principles are unclear. That limits reliability and progress. This proposal builds a principled framework on three fronts: adaptation, soft supervision, and cross-domain knowledge. We demonstrate it on autonomous driving and network management.

More before/after passes are in examples/before-after.md: a general