Asking Authors About Their Own Papers(medium.com) |
Asking Authors About Their Own Papers(medium.com) |
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.
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.
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.
The problem isn't with the papers here though, it is the author's understanding of the paper that is in question. A paper written by some hypothetically awesome AI would be a good paper but just not really the proclaimed author's paper.
I think this highlights a dual function of citations that are in tension. A citation can be to claim a stated idea has been made and tested with sufficient rigour to be published. Citation's can also be used to 'credit' others, treating reference as a type of currency. I think this latter form is an outright mistake, but entrenched in academia. The notion of giving credit like this creates a perverse incentive that lies behind much academic fraud, there is enough incentive to be the person to state something that it outweighs the requirement that person has for the statement to be true. Without that notion of credit as currency, issues like plagiarism simply disappear. In the absence of credit, someone making the same claims as someone else without referencing them is just making their own case weaker. Not necessarily less true, but less convincing. If citations were used just used to support a paper then the incentive is to cite, and failing to reference existing work harms only the author.
I think there is too much "This is my idea" and not enough "I think this is true". Credit fails as a measure of effort, diligence, innovation, or truth. Careers are being made and broken by how effectively an individual can game the system.
Jan 2026 https://www.theatlantic.com/science/2026/01/ai-slop-science-...
"For more than a century, scientific journals have been the pipes through which knowledge of the natural world flows into our culture. Now they’re being clogged with AI slop."
Sept 2026 https://www.theatlantic.com/ideas/2026/09/college-education-...
Academia, particularly the university system, is an untenable collection of interests. The triple stresses of COVID, AI, and funding withdrawal seem to presage what will be a significant disruption.
Actually, that sounds like an interesting idea for peer review in general, to include an interview between referees and authors. If it saves one round of rebuttals/reactions, it needn't even consume a lot more of everyone's time if you're doing those things properly. What it would undermine would be blindness, but something's gotta give, and it was already on its way out.
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.
> 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...
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.
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.
Interviews like this are interesting, but in no way can scale to the infinite paper slop conferences are facing.
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.
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.
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.
Some do it for the CV, some do it for science, but I don't know any Uni that expects them to be editor more than they expect them to edit the Wikipedia or write popular science. Cool if you do it, but not expected.
EDIT: I found a live link on arxiv https://arxiv.org/html/2609.20481v1
https://chorasimilarity.wordpress.com/2026/06/13/a-captcha-f...
At the moment this was seen as a tongue in cheek proposal.
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.
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.
"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.
I trust an LLM to review that the language used in the paper is grammatically correct, but not to evaluate new information for accuracy.
1) Humans also are trained on a subset of human knowledge. 2)A lot of papers are just about experimenting something, and then applying simple stats. Eg empirical studies, around 1/3rd of published papers. Like, we tried this drug or did this experiment, from a sample size X here are the results. An expert is needed to maybe comment on the conclusion/hypothesis of the underlying suspected mechanism, but LLMs are still very useful on catching bad statistics or p hacking (so so common)
What can't be gotten rid of fast enough is the notion that having written something is meaningful on its own. Making something that looks right was a level above total novice: now it's the floor.
For a start LLMs love LLM generated text, so you are boosting papers people never had any input in.
Secondly, LLMs in my experience are good at small issues, but fail totally at the whole paper being obviously poorly constructed, or clearly fake.
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".
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.
What good reason is there for trust in authorship to be low? I would likely agree for if it’s related to llm-slop.
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.
“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).
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 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.
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.
But also: When we want to ask a question of a bot and then get an answer from that bot, then we'd have already made a deliberate and rational decision to ask a bot ourselves.
We not need nor want anyone's help on that front; this kind of low-effort help results in an insulting waste of time.
Abject silence would be an improvement in communication quality over having an unwanted third-party conversation with a bot.
“Universities already have the space to do this, for potentially hundreds of defenders per day.” Universities have space for their own students while they’re still funded for the time, but conferences aren’t tied to universities. Even so if they were, from the author “The fraction of desk rejected papers at TMLR used to be about 6% in 2023 but is now at about 53%.” universities would have to raise funds for this massive increase of slop review. Us conferences uses unpaid volunteers (most part) from those areas for reviews.
If an independent conference were to hold in-person interviews (and maybe fly people out) for 1000s of submissions, where would they hold it and how would they pay for it?
Some cultures that are increasingly participating in the scientific process don’t even agree as to what the ethical boundaries are.
“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.
Indeed it is! That's why we set up rules of the game/road/etc. that we expect participants to adhere to, and impose sanctions when they don't. We're in that uncomfortable place today where we're trying to figure out how our rules should adapt to this new era.
Life is messy.