New comment by epq22 in "How we measured AI writing across arXiv, and where the measurement breaks"

epq22 — Mon, 20 Jul 2026 19:45:47 +0000

but the assumption you are making is that the underlying ideas are compelling. In practice, people often decide to publish incremental and/or mediocre work for various reasons, and then dress these ideas up to seem as compelling as possible to get past peer review or make a press release.

at the least, this is problematic for peer-review because the absolute number of submissions outpaces the time availability of a finite number of expert reviewers. We cannot quickly generate expert human reviewers, and so the community might converge towards half-baked solutions (AI-generated reviews or rejection systems, vastly expanded referee pools, etc.) that tend to erode trust and and make scientific communities more adversarial.

New comment by epq22 in "Over 30% of new ArXiv submissions now read as AI-written"

epq22 — Mon, 20 Jul 2026 19:04:32 +0000

To corroborate this - I found pretty similar rates of AI-flagged papers over time from running pangram on ArXiv (abstracts) in a physics subfield, which is currently at about 25% as of April (write up here: https://peterse.github.io/2026/06/15/The-rising-tide-pt1.htm...).

Its not clear from your writeup what threshold needs to be reached to be classified as "machine written". A preprint where half the text is human and half is 100% AI should be a different category than a preprint where 100% of the text is AI-assisted.

Also its cool that you're making the detector available. When you say "cheap to run", do you know how this compares to pricing for a commercial detector pangram or GPTZero?

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New comment by epq22 in "How we measured AI writing across arXiv, and where the measurement breaks"

New comment by epq22 in "Over 30% of new ArXiv submissions now read as AI-written"