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.

Evidence-based assertions are a good thing, but some things are so obvious that a "comparison" or "research" is not needed. This is already an obvious problem in so many places from high schoolers turning in assignments they don't understand, coders submitting code changes they don't understand, blog posts, and certainly to scientific papers.

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.

Thanks. IMO the most fun is in the CAPTCHA, which turns on its head the Turing test.

But it goes even further than their greCAPTCHA and it solves their consumed time problem.

Indeed, in their proposal they have a human bottleneck, but in the june 2026 proposal is suggested that one could use an AI to generate the results without the knowledge of the submitted article.

If the AI can generate a pretty close result, with the article fed gradually as a prompt, then reject.

And even further, that it might be not even a need to publish anymore.

Just use the article for training and make a public database with some numbers about the successful researcher, where we see an influence score (how many times an idea from an accepted article are used by other accepted articles), a publication score (how many articles the author had).

I wonder if the reality will be more or less surprising, my bet is on "more".

Sorry to be uncool and try and explain away the joke, but. I'm reading three parts to this.. one is the risk to the job of researches, by the impacts of the systems that they have typically used until now, by being flooded with texts that look like research articles, but are produced by llms.. another is a critique of that system in the first place, in the interaction of both for profit publishing, and research funding based on measuring of publishing in those for-profit journals.. and then there is also a concern about theft of work, and plagiarism, by AI providers, ingesting pre-published work, and training on it, and additionally those providers maybe acting as gate-keepers or .. imposing their own normative values or being opaque and not impartial in how they operate.. Did I parse that correctly?

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.

Makes sense, but first and last author should be able to answer on all the work

Yes. The cognitive process performed by a person using an LLM is often no different from that performed by a person using a ghost author, which is a form of plagiarism covered by 42 CFR § 93.234 - Research misconduct.

Plagiarism, at its most fundamental level, is a lie. It is the taking of works or ideas of others and passing them off as your own, either directly or indirectly. The misdeed itself is in the lie, the “I created this” when it is known to be untrue.

However, that lie isn’t being told to the original victim. It’s a lie about the victim, claiming that they didn’t create it or their contributions didn’t matter, but it’s not a lie to them. Instead, it’s a lie to the audience, which is the second victim and the actual target of the con.

https://www.plagiarismtoday.com/2019/08/01/the-two-victims-o...

This is a very good definition, but today people will just claim the AI is not a victim, and pass this argument off to the legal cases the labs are already having in court wrt fair use. It's sad, but this is the argument people have made to themselves.

The ‘real author’ isn’t the only injured party.

Copying from a book with a long dead author, or paying a willing confederate to write your thesis for you, is still plagiarism.

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.

Boy do I have news for you! :-D

"low quality, superficial reviews" have always been around. Reviewing is most often an unpaid, thankless job and many times reviewers barely put in the effort.

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.

This is the journal's policy on LLM use by authors [1]:

LLMs may be used as general-purpose assistive tools. Whichever tools are used, authors are fully responsible for content on which they are listed as (co-) authors. This includes, but is not limited to, content generated by LLMs that could be construed as plagiarism or scientific misconduct (e.g., fabrication of facts). Low-quality contributions (be they submissions or reviews) that appear to be largely LLM-generated will be closely examined for evidence of the issues mentioned previously, such as scientific misconduct. LLMs are not eligible for authorship. We will periodically revise this policy as new information about the use of LLMs in the scientific process becomes available.

While it doesn't outright encourage using LLMs, it's right at the door, and IMO a policy this weak is actively contributing to the problem the article's author is complaining about. In my opinion any policy weaker than "using LLMs to generate any part of your submission is not allowed and considered a serious breach of ethics" is insane. People like to say that such policies are unenforceable, but that's really not the point (at first), since there are other things like (somewhat ironically) p-hacking that are pretty hard to detect but still widely recognized as unethical. We haven't exactly solved p-hacking either, but at least most of us can agree that p-hacking should be eliminated.

It's hard for me not to read between the lines here. Maybe it's the tinfoil talking, but it being a machine-learning journal, it probably embodies a generally pro-AI philosophy, and thus may not want to discourage too much of it...

It may also be worth noting that this journal apparently uses AI itself on the reviewing side [2]. I'm not claiming this is super unethical or anything as long as the main review is human (although I have concerns), it probably should be part of the conversation.

[1]: https://jmlr.org/tmlr/editorial-policies.html

[2]: https://medium.com/@TmlrOrg/ai-reviews-at-tmlr-for-assessing...

So you can’t interact with an LLM, or use any product that has LLM features when performing the research?

That is not what I said at all. For research whose ostensible purpose is to increase human understanding, the absolutely bare minimum we could ask for is for the authors to understand their own work well enough to write their research paper themselves, without having an LLM crank it out. I'm not saying anything about LLMs at other points during the research. Unless I'm missing something, the TMLR policy does not forbid using LLMs to generate entire research papers. It only says the authors are "responsible for the content" and warns against "low-quality contributions."

I answered requests to be a peer reviewer. (I'm not sure why I was selected, I don't have many publications or credentials.) I saw a lot of papers with hallucinated references. I also remember one paper that described a methodology that I don't think the authors really performed, I think it was just academic fraud where they pretended to have performed an experiment. At the time that I answered the journal requests, AI could hallucinate fake reports, but agents weren't powerful enough to run the experiments yet.

These days agents are able to really perform genuine experiments and write up the results. A prompt like this: "You'll work autonomously end to end to select a research task that meaningfully advances the state of the art in AI, is clearly defined and worth performing, that people would be interested in reading, and that you can perform on this hardware" (insert details) " in a week. Carefully log your steps so that your results can be replicated. Then, do a research review and write your paper about it up with correct, cited references. You must check all of your citations. Look up current lists of "Claudisms", (such as use of the word "genuinely", or "load-bearing"), and remove them from your writeup. After writing your writeup, edit it and pare it down, remove anything unnecessary, keep it fast paced and interesting. Also, try to tell a story, be engaging in your writeup. Don't use violent metaphors, remove references to killing, strangulation, etc. Your writeup should be ready to publish and accurately reflect a real experiment with a meaningful result that advances the state of the art and contributes to understanding. Be concise and focus on why it matters."

Okay, so there's the prompt. You can give it to any AI and have a journal-ready publication in a week. I guess you can ask it to add charts and stuff, if you want to be fancy.

If I gave my agent the above prompt, would I be one of the authors? Maybe it's fair to say I guided, facilitated, elicited, or advised it. But it's clear that the AI would be the one that is actually selecting and running the experiment and writing up the results.

Someone could probably get a publication without even reading the paper they wrote their name on. Their only contribution might be editing their name into the PDF.

It can be turned around as well, for validating existing research, creating a bot that checks papers against all the well-known logical fallacies, issues with statistical methods, checks the images etc.

I hate to say this, but a researcher in biology ain't a statistician or logician. Many math paper hand waves over many parts of a proof.. yet both of those can be and have been useful in practice.

In the same vein, the Symposium on Theory of Computing (STOC) for 2027 has made some interesting changes to its Call For Papers (https://acm-stoc.org/stoc2027/stoc2027-cfp.html), notably requiring that papers be submitted beforehand to a preprint repository and that authors submit a 20-30 minutes video presentation explaining their results.

That’s a great “story from the trenches.”

I’m wondering if anyone just flat-out admits they used LLMs in their work. I have no problem, doing that, myself, but I also have the luxury of not having my livelihood on the line, and not being overly-concerned about what people think of me.

Eventually, I assume that AI will affect every aspect of the industry, and it will actually be a signal of effectiveness, to claim its use. I could see a day, when claims of not using AI would be like artisans, declaring their work to be “genuine hand-crafted,” and relegated to fairly small, specialized corners of the industry.

If we can't reproduce the work or it doesn't work or it's not actually doing anything of value it's less like "handmade furniture Vs Ikea" and more like "handmade furniture Vs a bundle of sticks".

I don't know what it is but something is clearly wrong that people are upset about the people involved not really understanding the things they're building when they have fulfilled "the brief" using LLMs. Sometimes it feels silly like going through the motions of typing code is somehow helpful. But I think there is some line where people are justified in being concerned when a business or a paper doesn't actually understand what they're ostensibly "owning" or improving as a product/paper.

An observation from within the system:

A year or so ago I completed a PhD in theoretical high energy physics. I did that after working for about 5 years outside of academia [the reasons for that were financial issues in my family]. Because of this hiatus (I suppose) and the fact that I had troubles getting reference letters (my master thesis advisor passed away at quite a young age right at the time when I tried to start applying for positions; we actually agreed to meet in person to discuss next steps but the meeting never happened) I had issues getting accepted into PhD programs and I ended up with an offer from a rather weak institution in my home country (which is in EU). I had no other options to choose from (and I could not wait another year) so I accepted. However, I also looked at the publishing record of the team that I was about to join. Their works did not seem stellar (and I did not expect that) but they seemed to publish regularly, on several topics, and with several collaborating institutions.

When I started I quickly realized that the knowledge/expertise level was much lower than I expected (and I did not expect too much). The group consisted of the group leader and three senior researchers, and I am pretty sure that my knowledge of QFT when I *started* working with them was the best in the group. (Now, I had studied in my own time during some part of the years I was outside of academia, and I think my knowledge at that time exceeded that of an average 1st year hep-theory PhD student in Europe, but it was also nothing spectacular; definitely not what I would call "competent"; which is admittedly a high bar in quantum field theory / hep theory, but something I would expect senior researchers to more or less satisfy.)

I realized that the one topic on which the group was publishing on their own was just a continual rehash of the same thing (applied to various different problems, so perhaps not completely useless) and the other topics, those which seemed more advanced when I originally looked at the publication record, were all done essentially outside the group by other people. The group members contributed with some non-essential help (often resembling a work done by a student, such as finalizing a manuscript or double checking calculations) or with nothing; and were added as coauthors due to some other reasons (I suppose: past loyalties, friend groups, advantage of having foreign or external institution co-authors). [Note: I was not included into any of those collaborations so I am not speaking with a full knowledge of the inner workings there.]

During this time I saw that people being added as coauthors had a very noisy relation to what they did or didn't do on any given paper. I was added as a co-author on a paper that I made essentially no work on [not fair], I was added as a co-author on papers which drew upon some of my earlier results [fair, but I did not subscribe to the overall spirit of the paper or even to the paper being published at all], I was a co-author of papers where number of authors and my positions in the list roughly corresponded to my contribution [as it should be], I was a co-author of papers that were done basically by myself but there were several other co-authors included who made a little to no contribution [and the ordering of the author list was always alphabetical not reflecting the relative contribution].

There is also another aspect of this: If you are an early career researcher (PhD student, PostDoc, and even non-tenured professor) you might have a very limited control over who gets included on the publications you work on, and on what publications your name appears. In my case, I am a very disagreeable person when I think things are being done wrongly and yet I have not managed to refuse from being included on papers I did not want to be a co-author of, or to prevent people who contributed nothing to be included on my papers. [I mean the only way to achieve that meant escalating conflict into levels which (a) would likely disable any further cooperation with the group, and (b) might be even morally questionable concerning the level of distress it would make to other people who just seemed to be fine about "the normal way" things were being done.]

In any case, while there are still some really good people in academia (actually the best people I met in my life were nearly all in academia), in overall (by no means fully indicated in the above paragraphs) I feel very bitter about it, and I think it is so dysfunctional and morally corrupted, that it is nearly inevitable that it collapses in future. The current and future shockwaves of the change in the public sentiment and funding, the evolution of demographics, and the consequences of AI, just hasten the process that would have probably unfolded, sooner or later, anyway.