One larger problem here is the value of a research paper is rarely the specific knowledge it adds but in the process of researching that adds to the collective knowledge+experience of those involved, especially training graduate students. AI papers shortcut this entirely. Academia has a lot to answer for this too by making papers the currency of success. AI generated papers are almost shortcut learning at a full system level.
Peer review has its historical issues, but the landscape of science and science-publishing has changed. New problems of authorship and authorial-understanding are now challenged by LLMs writing (at least) good sounding papers - some of which might be of acceptable quality in subject (I am not against AI in the sciences; some of the math work has been great). On the other hand: I am against authors not understanding their own work. High repute journals may need to add "oral exams" to the paper acceptance process...
FYI in case the author is reading, <a href="https://www.cs.cmu.edu/~nihars/preprints/greCAPTCHA.pdf" rel="nofollow">https://www.cs.cmu.edu/~nihars/preprints/greCAPTCHA.pdf</a> is a dead link.<p>EDIT: I found a live link on arxiv <a href="https://arxiv.org/html/2609.20481v1" rel="nofollow">https://arxiv.org/html/2609.20481v1</a>
I wonder how the ratios would change for papers at different parts of the review process. For what fraction of published papers are the authors unable to answer basic questions about them?
I think authenticity and trust will command a (larger) premium in this new age of slop.<p>The article highlights how only one out of ten paper’s authors were able to answer questions thoroughly and at a high level. This indicates an overwhelming percentage of authors are slopping up their work with AI and submitting it without even reading it.