I think authenticity and trust will command a (larger) premium in this new age of slop.

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.

No doubt this is happening, but I wonder how many authors of papers "slated for desk rejection" 10 years ago could answer questions about their papers? We'd need that comparison to understand if this is a new problem or if AI is just a new source of content that the authors of poorly-written papers are using.

FYI in case the author is reading, https://www.cs.cmu.edu/~nihars/preprints/greCAPTCHA.pdf is a dead link.

EDIT: I found a live link on arxiv https://arxiv.org/html/2609.20481v1

In June 2026 I proposed a CAPTCHA for scientific publications

https://chorasimilarity.wordpress.com/2026/06/13/a-captcha-f...

At the moment this was seen as a tongue in cheek proposal.

I’d agree it’d be a funny proposal, wouldn’t have worked back then but funny.

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.

I'm not familiar with the world of academic publishing, so I want to ask: how is the industry making sure that submissions aren't at least partially AI-generated?

Is it standard practice for authors to have to defend their submissions via interview like this? If not, why not?

Does the vetting process vary with the quality of the publisher?

As an outsider, it's extremely worrying that anyone would even attempt to submit an AI-generated paper for publication in an academic journal. At that level I would have assumed literally everybody should know better than to even try.

Is it standard practice for authors to have to defend their submissions via interview like this? If not, why not?

It isn't, but maybe it should be. For post-grad qualifications oral defense is standard, and I didn't mind defending my central thesis then, and won't mind now.

Not in a direct interview style, but most us conferences can request additional information or feedback. If they conditionally accept or reject a paper, that conditional relies on feedback from the author(s).

Interviews like this are interesting, but in no way can scale to the infinite paper slop conferences are facing.

Not in a direct interview style, but most us conferences can request additional information or feedback. If they conditionally accept or reject a paper, that conditional relies on feedback from the author(s).

Requesting feedback is useless, as the article points out - the "authors" could not answer basic questions during the interview, but after the interview were able to send full explanations to the interviewer.

If you have indirect feedback ("please answer these questions we have") the "author" will simply feed it into an LLM and send the results back. You need to get the author to do an oral defense to verify that they wrote the paper.

This is the main problem with AI generated output, whether it's a research paper, a blog, an email, a comment on a forum, similar: the value in knowing that a human wrote $X sends a signal - that the human understands what it is they wrote, even if they misunderstand the concepts.

When you get a message from someone who is a "I only used an LLM to clean it up, the thoughts are all mine"[1] person, you cannot engage with them, because they may not understand the message they transmitted, and so any human engaging with them is only burning their own time for no gain.

When you get a message from a real person, you get not only the message, you also get a signal about their understanding. That signal is missing in AI generated messages.

==========================

[1] Sure, buddy. We believe you /s.

I only used an LLM to clean it up, the thoughts are all mine"

I mean, it seems to me like that should be nearly everybody at this point, right? There are at least some elements of using LLMs that are the equivalent of a spell check, like asking to make sure that links work and that references are to the thing they're supposed to be, and so on. I feel like the only difference at this point is between people who do that and say that they do, and people who do that and don't say that they do.

I feel like the only difference at this point is between people who do that and say that they do, and people who do that and don't say that they do.

No. Did you "clean this up" with an LLM before posting it?

I hate to go there, but how is that argument different from a thief who argues "Everyone steals. The only difference is some of them claim they do, and some claim they don't"?

I mean, it seems to me like that should be nearly everybody at this point, right? There are at least some elements of using LLMs that are the equivalent of a spell check, like asking to make sure that links work and that references are to the thing they're supposed to be, and so on.

Right. Maybe. The problem is that when material has all the tells of LLM generation, do you expect the reader to figure out if the thoughts were the authors or introduced by the LLM?

The value in a human directly communicating with you is that you understand what they are saying; if they misunderstand, you spot their misunderstanding. If they are talking at cross-purposes, you know you are arguing past each other. If they agree with you, you know they agree with you.

That signal is missing when the communication is LLM generated; you message said "Not X, Not Y, just Z", but I can't tell if you even know what X, Y and Z are. f I ask you to give me a definition for them so I can ensure we aren't talking about different things, the LLM response will be "X is $SOMETHING", but I still can't tell if you think that X is $SOMETHING or if you still think that X is $SOMETHING_ELSE while your LLM and I agree that X is $SOMETHING.

Communication from a person tells us something about that person, outside of the message being communicated. If that communication is relayed via an non-invested third party, the missing signal prevents any communication.

“Requesting feedback is useless, as the article points out”, I would agree post llm availability. Before that (~pre 2023 I suppose), this wouldn’t have been as large of a concern.

“You need to get the author to do an oral defense to verify that they wrote the paper.” While I agree with this point, it can’t scale. As with the indirect response, vishing and likely video mimicry are only going to be easier with the next llm release. This leaves requiring each author who submitted a paper to fly to a place just to defend a paper in person (assuming all volunteer reviewers are in the same place).

“You need to get the author to do an oral defense to verify that they wrote the paper.” While I agree with this point, it can’t scale.

Are we prepared to sacrifice everything on the altar of scalability?

At any rate, why can't this scale? Go to the nearest accredited university, sit at a terminal they give you and do you interview.

Universities already have the space to do this, for potentially hundreds of defenders per day.

I’ve only published a few papers, but this interview sounds extremely unusual to me (I mean, it is clearly a special thing that the editor is doing, which is fine). I wouldn’t do something unethical, but if I had and the editor asked me for an interview like this, I’d know I’d probably been caught.

Why is it unusual: it sounds extremely time-consuming.

As to how worrying AI-generated papers are… it sounds more like a headache for the editors really.

In general, journals don’t have to be perfect; mostly researchers read research papers. You already have to read critically (publish-or-perish has been a thing for a while, so there are plenty of not-so-great papers out there). Peer review is just the “entry” barrier, science is a social process and papers become more or less influential based on a fuzzy process of citation, conference talks, and peer-to-peer suggestions.

it sounds extremely time-consuming.

For whom? Surely the authors can find an hour after submitting the paper to a journal?

Beware that a reviewer easily spend a full week on reviewing a paper, and there are typically three of them. So if one hour of conversation can save three weeks work, it sounds worth it.

I was thinking for the reviewer. Also note that I was only answering as to why it wasn’t done in the past.

For the time comparison, I’m not sure, it doesn’t seem quite apples-to-apples:

1) It is scheduled time vs unscheduled.

2) There must be some base rate consideration… the papers being discussed here were planned to be desk-rejected.

Maybe it could be a good process for saving papers that were going to be desk-rejected, but I dunno, that seems like it’d just lower the quality standards.

But it's not the reviewers who do it, it's the handling editor. Who spends relatively little time per paper (compared to authors and reviewers).

The Medium comments on this post are also on point. Running the same experiment with accepted papers is a good control. Running a similar experiment with reviewers would be interesting, but more obnoxious because they are not being paid.

I would keep a private blacklist (shadow ban) the authors who wasted several hours of a reviewer's time to prove they were not legitimate. The existence of such a list would be problematic, though.

Could the same system we use here be applied? Accepted authors could "vouch" for "dead" papers in case they were "auto-killed"?

This system is broken and providing more evidence that it is broken isn't much of a step towards fixing it.

That would fail, humans and agents could create new “author” accounts by the swarm or have paid author accounts.

Do you imagine a world where people are willing to go through the legal hassle of changing their name to get past a ban for low effort journal submissions?

Legal names aren’t 1:1 with author names. If they were, who’d verify that?

Validating someone’s association with an institution by name seems like a reasonable thing to do. Perhaps it wasn’t done in the past, but times and circumstances have changed. Trust in authorship is lower than ever, and for good reason.

It likely is, but that’s also on the assumption every author has to be in association with an institution and it’s quick to verify that.

What good reason is there for trust in authorship to be low? I would likely agree for if it’s related to llm-slop.

Plagiarism, fictional data, and unreproducible results have been a problem for a some time, and LLMs are now increasingly standing in authors’ shoes. In a system that rewards production over all else, people are naturally going to be incentivized to cut corners.

Some cultures that are increasingly participating in the scientific process don’t even agree as to what the ethical boundaries are.

True, and thanks to llms are of those are being “exploited” exponentially more.

“In a system that rewards production over all else, people are naturally going to be incentivized to cut corners.” That sounds like the human condition and or capitalism, advancements in cars, weapons, toys, etc…

The agreement on ethical boundaries seems like a conversation held at a conference level, otherwise place a governs place b and that never goes well.

I agree fully replicable experiments is a minimum for research papers. Even with caveats it should be required. That being said those who don’t want to fully prove their claims will find another venue. I think frontier models should curb or have provably verified output (chatgpt did say x) for the majority cases to help reduce or help identify the slop.

Even if it's not a legal name, are you going to review the CV of an applicant who used a different name on every publication they claim authorship of? It's going to be pretty obvious someone is being nefarious

It strikes me as akin to plagiarism. If the purported author can’t even answer basic questions about the paper, how can they plausibly claim to have written it?

In the case where someone uses AI to write the paper and then deeply familiarizes themself with it, it may go undetected, but then it’s also presumably less of an issue since they have actually read it carefully and closely. If it’s still bad or wrong after that, then it’s not that different from a human writing a bad or wrong paper on their own and should be treated similarly.

It strikes me as akin to plagiarism. If the purported author can’t even answer basic questions about the paper, how can they plausibly claim to have written it?

Authorship standards differ by field. In biology, for example, it would be common to list someone as an author if they assisted in one experiment. They might be at a different institution and may be unaware of all but the vaguest outline of the paper as a whole—they just got brought onboard because they are an expert in one particular task that needed to be done. In exchange they get to be a middle author (not worth much) and develop a relationship with someone whose expertise they may need on one of their own papers in the future (the primary benefit).

That is not the case here, of course—I just wanted to provide some context for your position not being universally applicable.

IMHO (not a paper writer, but read a lot during my grad school years), the Genie is out of the bottle. The only way forward, as I see it, is using LLMs for reviews also. Basically, filter all submitted papers with an LLM and ask it to summarize it, find the biggest weaknesses and main strong points, etc. that a human can then use to review the paper. Basically, LLM-as-a-reviewer .

Personally, I would love to see a conference where people are explicitly encouraged to use LLMs for doing the work and writing the papers, and LLMs are used to review them too.

The entire point of an academic paper is to add to the sum of human knowledge. How can an LLM trained on a subset of human knowledge possibly even begin to accurate evaluate such a paper?

I trust an LLM to review that the language used in the paper is grammatically correct, but not to evaluate new information for accuracy.

There should at least be a code of professional conduct where authors state the extent to which LLMs were used. (This would also help not wasting time by asking some “authors” about “their” paper.)

Journals themselves should make policies about the extent to which they allow the use of LLMs. In some areas it might be considered more benign than in others.

This already happens. It's clear when a reviewer used an LLM, and it's very annoying for the authors that have to respond to what are usually low quality, superficial reviews.

All reviewers, Action Editors and Editors-in-Chief for TMLR are unpaid volunteers.

He is not an unpaid volunteer.

He's an associate professor at the prestigious Carnegie Mellon University. He is not paid by the journal, but he is paid a salary by the university, and the university expects that a small part of his academic work is to serve as an editor in academic journals.