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.
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.
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.