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.
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.
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.
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.
> 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.
> Accepted authors could "vouch" for "dead" papers in case they were "auto-killed"?
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.
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?
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.
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.
> That sounds like the human condition and or capitalism, advancements in cars, weapons, toys, etc…
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.
Oh no I fully agree with that. We just have to find a medium that people can easily use but can’t abuse. The constant cat and mouse security problem given non-infinite money. In-person defense would solve this but I think the cost is too high.
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
The CV of an applicant is definitely different. For a job application where a cv were used, that’s a slower/paid process where attention should be paid to details. However what’s a difference to a conference with a “young upcoming author” vs llm-Lorem ipsum author name?
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.
"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."
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.
I disagree for academia using papers as an easy medium for verification and providing more knowledge. Llms always short circuit everything, so how would you fix academia?
> Separately, our group has been exploring approaches along these lines to make such evaluations more scalable
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.
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.
maybe but as a paper writer, the quality of reviews and reviewers have gone down because of LLM-as-a-reviewer too, because LLM reviews seem to regurgitate the limitations section of the paper, and are highly influenced by the way things are phrased in the paper rather than the actual substance.
but I'm hopeful that some middle ground will be found in the future
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.
There is. NLP conferences, like the ACL family (and the ARR) require you to disclose in the paper if you used LLMs for writing and coding, which ones and how. Whether every author is honest is a different matter.
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.
"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.
Similar experience to sibling: a disinterested human isn't going to ask for 30 different detailed things to add to, or change in, the paper that could technically be improved but aren't worth the additional page real estate. An LLM reviewer definitely does this.
There is still a difference. Previously, if the review was low quality and superficial, it was also quite short and easy to answer. Now I receive fully LLM-written reviews of 2-3 pages (or more) of superficial comments asking for tons of additional text and experiments that are almost always outside the scope of the work. Answering such a review requires a lot of work with absolutely no gain.
I think that’s a lot of risk of anchoring reviewer bias. I’d be more comfortable with a triaged review where the editor’s office uses models to score whether a human editor should evaluate a paper to potentially send out for review, then the editor makes their own assessment, and the reviewers continue to do their job unassisted
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.
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)
Yes. I want reproducability, open-sourcing, accessibility, correctness, and most of all: usefulness. I don't care how it was written or reviewed, as long as some assurances regarding above things can be made, and I don't see why LLMs would get in the way of that.
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.
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...
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.
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."
That’s a reasonable position to hold. The way it was stated earlier would have included the content of the paper (the research) not just the words in the paper.
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.
“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).
“Are we prepared to sacrifice everything on the altar of scalability?” If the performance of verification isn’t great, it won’t get adopted. Just general acceptance timelines, look at how long it took for Python 2->3, https 1.2, us real-ids. You can’t adopt a significant change immediately and expect things to work immediately.
“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?
> 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.
You've nailed my thoughts on that aspect pretty well. You demonstrate why when we choose communicate with a person, we definitely want a person at the other end of the line.
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.
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.
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.
Honestly, I was sloppily thinking of the general reviewer/editor system as one thing (and didn’t notice that I’d lumped them together until you brought it up here). Good point. Although the editor has a lot more papers to handle, so I think they are probably busy as well.
> 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.
There are much easier and more prestigious ways of fulfilling university service requirements than reviewing and editing for TMLR, so I think it is correct in spirit to label it as volunteering.
> 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.
Elsevier, springer and mdpi have literally billions dollars in profit thanks to this free labor. Why would university pay for performing labor for for-profit companies? The system is broken and we should name things as they are - it’s free labor.
Is it really “research” - as in expanding human knowledge - if nobody understands it? The point is deepening human understanding, not producing research papers
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".
Curation is the new skill. The dismissal of a result on the basis of authorship is one of the reasons blind reviews are there. It sounds like we need more scientists. In al seriousness, this is a skill not just for science. Even at work, the amount of slop is rising exponentially and people are half-treating the symptom with its source, using AI to summarise. We will find our ways eventually.
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.
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.
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.
That may well be the case. My suspicion is that many research papers would not pass such a quality review. I am not saying anything about their usefulness, though.
You are not suggesting it, but as some might, I want to emphasize that I think it is, really, really, really bad idea to argue for limiting exposure of the bad practices because the papers may be useful even when not following good practices.
The science can work only when we can trust that the foundation it has been built on, including previous research, is solid.
The trust is partly based on the fact that science is self-correcting. As we know from charge of electron measurements, the existing narrative can work against the self-correction, even when everybody is trying to be as truthful as possible.
My impression is that to a large extend, on many fields, publication numbers (and money) have become so important that self-correction process may have become broken.
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.
LLMs of course, but think the real issue here is incentives. Clearly the participants are incentivized to publish garbage.
That's going to crater the signal to noise ratio of papers so best that be fixed asap. How though...
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.
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.
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...
> 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.
That’s fair, I should have clarified that I was thinking about the solo authors when I wrote that.
> Accepted authors could "vouch" for "dead" papers in case they were "auto-killed"?
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.
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.
> That sounds like the human condition and or capitalism, advancements in cars, weapons, toys, etc…
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.
Oh no I fully agree with that. We just have to find a medium that people can easily use but can’t abuse. The constant cat and mouse security problem given non-infinite money. In-person defense would solve this but I think the cost is too high.
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
The CV of an applicant is definitely different. For a job application where a cv were used, that’s a slower/paid process where attention should be paid to details. However what’s a difference to a conference with a “young upcoming author” vs llm-Lorem ipsum author name?
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.
Related:
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.
I disagree for academia using papers as an easy medium for verification and providing more knowledge. Llms always short circuit everything, so how would you fix academia?
> Separately, our group has been exploring approaches along these lines to make such evaluations more scalable
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.
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.
maybe but as a paper writer, the quality of reviews and reviewers have gone down because of LLM-as-a-reviewer too, because LLM reviews seem to regurgitate the limitations section of the paper, and are highly influenced by the way things are phrased in the paper rather than the actual substance.
but I'm hopeful that some middle ground will be found in the future
Here is my proposal (or actually our proposal - me and my agents): https://zby.github.io/commonplace/articles/what-an-automated...
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.
There is. NLP conferences, like the ACL family (and the ARR) require you to disclose in the paper if you used LLMs for writing and coding, which ones and how. Whether every author is honest is a different matter.
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.
Similar experience to sibling: a disinterested human isn't going to ask for 30 different detailed things to add to, or change in, the paper that could technically be improved but aren't worth the additional page real estate. An LLM reviewer definitely does this.
There is still a difference. Previously, if the review was low quality and superficial, it was also quite short and easy to answer. Now I receive fully LLM-written reviews of 2-3 pages (or more) of superficial comments asking for tons of additional text and experiments that are almost always outside the scope of the work. Answering such a review requires a lot of work with absolutely no gain.
To be fair, low quality superficial reviews were also not uncommon before LLMs…
Extend this beyond review. The most value we would get is from quality checking existing published papers.
I think that’s a lot of risk of anchoring reviewer bias. I’d be more comfortable with a triaged review where the editor’s office uses models to score whether a human editor should evaluate a paper to potentially send out for review, then the editor makes their own assessment, and the reviewers continue to do their job unassisted
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.
Schmidthuber has an answer: https://arxiv.org/abs/0812.4360
For a more practical approach you need to use proxies: https://zby.github.io/commonplace/articles/what-an-automated...
This is very bad logic
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)
Yes. I want reproducability, open-sourcing, accessibility, correctness, and most of all: usefulness. I don't care how it was written or reviewed, as long as some assurances regarding above things can be made, and I don't see why LLMs would get in the way of that.
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.
Nope, I’ve tried this, it’s awful.
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.
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...
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."
That’s a reasonable position to hold. The way it was stated earlier would have included the content of the paper (the research) not just the words in the paper.
This could be renamed "Asking authors about their own pull requests" and the outcome would be exactly the same.
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.
“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.
“Are we prepared to sacrifice everything on the altar of scalability?” If the performance of verification isn’t great, it won’t get adopted. Just general acceptance timelines, look at how long it took for Python 2->3, https 1.2, us real-ids. You can’t adopt a significant change immediately and expect things to work immediately.
“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?
> 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.
You've nailed my thoughts on that aspect pretty well. You demonstrate why when we choose communicate with a person, we definitely want a person at the other end of the line.
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.
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).
Honestly, I was sloppily thinking of the general reviewer/editor system as one thing (and didn’t notice that I’d lumped them together until you brought it up here). Good point. Although the editor has a lot more papers to handle, so I think they are probably busy as well.
The rates of paper publishing show that everyone must be using ai now or the rates wouldn't have gone up
> 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.
There are much easier and more prestigious ways of fulfilling university service requirements than reviewing and editing for TMLR, so I think it is correct in spirit to label it as volunteering.
> 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.
Elsevier, springer and mdpi have literally billions dollars in profit thanks to this free labor. Why would university pay for performing labor for for-profit companies? The system is broken and we should name things as they are - it’s free labor.
Is it really “research” - as in expanding human knowledge - if nobody understands it? The point is deepening human understanding, not producing research 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".
Curation is the new skill. The dismissal of a result on the basis of authorship is one of the reasons blind reviews are there. It sounds like we need more scientists. In al seriousness, this is a skill not just for science. Even at work, the amount of slop is rising exponentially and people are half-treating the symptom with its source, using AI to summarise. We will find our ways eventually.
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.
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.
Yeah sadly you're probably right.
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.
That may well be the case. My suspicion is that many research papers would not pass such a quality review. I am not saying anything about their usefulness, though.
You are not suggesting it, but as some might, I want to emphasize that I think it is, really, really, really bad idea to argue for limiting exposure of the bad practices because the papers may be useful even when not following good practices.
The science can work only when we can trust that the foundation it has been built on, including previous research, is solid.
The trust is partly based on the fact that science is self-correcting. As we know from charge of electron measurements, the existing narrative can work against the self-correction, even when everybody is trying to be as truthful as possible.
My impression is that to a large extend, on many fields, publication numbers (and money) have become so important that self-correction process may have become broken.
completely necessary.
Sounds like I shouldn't be so hard on AI when it hallucinates things based on this data? :)