I flagged two research papers for fake authors and both were accepted as orals
geospatialml.com
geospatialml.com
- papers are written by AI (as pointed out in this article, and as obvious to anyone who spends a while actually reading recent AI research)
- papers are reviewed by AI (NeurIPS is doing an AI assisted review experiment - https://neurips.cc/Conferences/2026/ai-reviewing-experiment - and I feel the trend is moving towards AI reviewers whether we like it or not)
- papers are read, summarized and digested by AI, because there are just so many papers at leading AI conferences that nobody has time to eyeball them all
We are very rapidly automating humans out of the academic publication loop here.
I may generate slop from time to time, but I do my best to keep it to myself.
We are nowhere near AI being able to judge the quality of research (in fact, one might reasonably state that even most humans can't really judge the quality of research). Most things in society are not like math: we can't automate (via verification) our way out of noise overwhelming the signal.
Folks are willing to entirely abuse the public resource that is faithful, honest reviewing. (This is unsurprising; the abuse of the commons / public resources has been rising for a long time). There isn't a good solution other than something akin to draconian social scoring to limit access to the reviewing system.
This is a legitimate question: should people have reputations? Should their behavior be made more visible publicly, both good and bad? How?
In a small contained society, where consequences are more directly affecting individuals immedately, these questions don't need to be asked, because they're inherantly answered. We now are a society with billions of people, and dire consequences sometimes deferred for a generation, or more. Part of our general failing is the lack of good answers to the above questions. For many people, there are rarely negative consequences for causing harm to others, and the rewards can be very great indeed.
Until we’re actually serious about treating people equitably, (not equally, as that would simply leave the lopsided power structure we have in place) we aren’t getting out of this.
So this is going to come down to things that aren't usually illegal or 'that illegal', but otherwise affect society. So for instance one group might want to reasonably punish the directors of a company that causes emissions. Another group might want to reasonable reward the directors of a company that creates a large number of desirable jobs. And even in this one example you immediately end up with a weird scenario. Is it okay to pollute as long as you make enough jobs? Who gets to decide that?
There's no need for cartoon villainy for this to be a bad idea. Even completely well intentioned, it just doesn't work out well. And the more diverse a society, the worse it's going to work.
Yes, there can be obvious patterns to money/friends/skin color/gender that should be watched out for. But if you're paying much attention to those, then your science will quickly become a mere side gig to your new Cultural Warrior job.
I'd phrase it "most people want to imagine". Or will claim they want to - since not believing sounds depressing, or suggests that the person is an evildoer hoping to escape justice. (But unfortunately, people usually disagree about exactly what would be "just" or "fair". While wanting to imagine that they don't. Yes, the problem just went meta.)
> when the reality is ...
Try asking some really old folks about how much drama, inequity, and nastiness there often was in social settings where everyone was the same color and gender, nobody was notably wealthy, and nobody had any great connections. Or talk to an experienced junior high teacher. Or read some history. Humans are quite capable of dividing themselves into camps over any "differences" that they're able to perceive. Or invent, since dividing themselves into camps is often the unspoken objective.
In my experience, this viewpoint is most strong in people whose actions are the most direct cause of their social issues. Almost always these people strongly refuse to admit that their actions should or are under their control. At which point others start to avoid them.
There are large scale social stratifications due to various things however individuals live in a very wide band in my experience. Some make choices that put them at the top of that band and others make choices that put them at the bottom.
Let's take two of your examples:
> how much money someone has put in someone’s pocket
> who someone knows
Careers choices directly lead to having higher income and more money. Socializing and putting effort into building a social network gives you more people to know. Both are to a decent degree under someone's control and based on their actions. You claiming otherwise is exactly proving my point on people wanting an excuse for their poor historical actions.
But it's also worth pointing out that "consequences and reputations stemming from one's actions" is already the world we live in and always have lived in. Hell, even Hacker News has karma points, downvoting, shadow banning, and the like. There's no such thing as a society with zero consequences and zero reputations. The only real question is a matter of degree, structure, severity, reach, and various idiosyncrasies that differ across cultures.
So it would be nice to have a more nuanced discussion about this instead of treating it like a 0 or 1 decision.
and so it was obviously portrayed as kind of you know for the story and it worked and I like the episode and everything but it got me thinking in terms of a society like that of people could probably construct something that used that kind of a system but in a more regulated way and a more humane way and I think that's turns into the question of do we want to try and make this new thing or do we want to try and fix the old thing?
regardless, it got me thinking about some kind of a potential implementation of a formal social reputation system that essentially mirrors what goes on informally now, but makes it visible and makes the actions of affecting somebody's reputation visible.
My basic intuition here and for most things that deal with society or whatever is that Satan lives in the details and infinite potential dystopias and the potential for extreme inhumanity lurks in every crevice and corner. unknown unknowns and shit.
In general you can already get this by asking and taking feedback from others. I've seen a lot more people shoot or ignore the messenger in those cases then actually take the feedback. Everyone is the hero of their own story.
which makes me think that these kinds of systems are interesting to talk about and possibly productive. but when anybody gets a serious idea to start implementing them, I think it runs into the same problem that everybody that got really excited about Corey Doctorow's wuffie. while not an original idea, his description of it really catapulted the concept of social currency into the nerdosphere. at the same time that Reddit karma was becoming a thing. and so people were understandably irrationally exuberant.
at any rate, they figured out like many before them that trying to automatically manage reputation at scale is a socially difficult problem to solve, before even getting to the technical issues.
and yes it is one of those things that as I get older I just have to take a step back and use those kinds of people as an example of how not to be. I think AI psychosis as talked about when it was called chatgpt psychosis is maybe more properly classified as main character syndrome level 11 or something. I am not a psychologist. and it makes me wonder if that kind of main character syndrome is some kind of a social contagion. because in the modern incarnation that is everywhere it seemed to really get a foothold in podcast-o-sphere. though, like everything else, I'm sure that could easily be mapped into. people with opinions have high opinions of their own opinions and it inflates their egos and that has been going on since one person got in a room with themselves and started thinking.
Internet Right/WrongThink scoring should be an argument against any sort of formalized or standardized social credit, not for it.
Wonder if it could degrade to something worse than the status quo where reputations are falsely tarnished by competitors purely as a weapon to get ahead…
I'm living here (China, Beijing and Jiangsu) now, and there are exactly two cases in the past eight months where the "social credit" system has had any impact at all:
- To open "take first pay after" vending machines. These are vending machines which are basically big locked fridges; if your credit score is high enough, you can open the machines, take whatever drinks/snacks you want, and they'll use (I presume) computer vision to charge you afterwards. If you don't have a high enough credit score, tough, you can't use them. (But there's almost always normal pay-first vending machines nearby).
- To borrow mobile charging packs (powerbanks). Some operators will let you "swipe your credit" (check your credit score) to take one without paying a deposit. If your credit score isn't high enough, you first pay e.g. a ¥99 deposit (~$15USD) which gets returned when you return the powerbank, not a big deal.
That's...it. My credit score is high enough on only one platform (Alipay), so I get to try what happens with both "high" and "low" credit, and I can confidently say these are the *only* two cases where I have even been asked to show the credit score. I have taken multiple train trips, bought lots of stuff in stores and restaurants, etc. without ever touching the "social credit score".
P.S. I literally don't even know how to raise my credit score, and neither do many of the folks who live here - again, not that it matters, because the score is simply not that important.
It is people who did not pay their debts. Dr Jonathan Tam has a video on YT explaining it called:
Why the World Fell for China's Fake Dystopia
(Genuine question, I'm not American and don't have any desire to move to the US).
Neither. Above poster is wrong. You don't require anything to own a house or car in the US except your own cash/equiv. If you wish to borrow other people's money or property (in some cases at all when counterparty risk is high enough or buyers competitive enough, in most cases it's more a matter of interest rate), a sufficient credit score is the most standard and easy way to "make the case" that you will repay. But that's truly not at all required. I bought my current truck for $8100 cash on the table, I just showed up at the other guy's place and inspected it and chatted, looked good enough, paid him and we did the title change document, I screwed on a new plate and off I drove with it. Effectively zero dealers will not accept cash upfront. You can buy land and just build your own place.
Obviously, a lot of us find having credit very useful. And it's also both convenient and good for fairness/economic velocity/efficiency not to generally have to explicitly put up any extra collateral to get it or go through some complex old fashioned social dance. Hence "credit scores" that systemize a lot. But claiming they decide "whether you're allowed to have a house or a car" warps things badly.
- it did not work before because greed
- will not work anymore because AI
Is human science dead?
We have a past example of this in the US, too. Certain countries have historically had a huge problem with paper mills. Because of this, most people in the US/EU do have a negative bias once they see the author affiliations of those countries. Yes, it isn't fair to researchers who aren't pulling some shenaniganry. But it is a common shortcut; the sixth or seventh time you've spent a few hours reviewing a paper filled with bullshit, you probably would develop it too.
I imagine we will see similar things come up: the most obvious I can think of is, if it's not a well-known institution then it might be bullshit.
Hell, we are already seeing something similar in FOSS, where many high profile projects have completely banned AI contributions due to all the low effort slop PRs.
Pay a deposit to have your work reviewed, if it's accepted as a good faith submission, the deposit is returned minus a minimal, irrelevant fee, if it is deemed in bad faith, take all of the deposit as a time-waste tax. With enough cases going wrong, the flood of slop slows down and maintainers have to pull the trigger as often.
This was the solution several people had proposed for Git issues, but none that I know of took the plunge. I really think curl should have done that.
We can use it as vacuum to clean things up. The same pump can be used as a shit fire hose.
The effort we need to clean things up is significantly higher than the effort needed to make a mess.
If you add a score/metric this incentivizes the wrong thing, like how money, funding, and career already incentivizes all the wrong non-scientific behaviors (like faking data, p-hacking, etc.)
Don't we already have all of these things because academia decided to use H-index as a metric for career impact?
You'd be less willing to publish ai slop if your name had to be tied to it for years to come.
We are rendering it irrelevant. If this is the norm for academia, I’m sympathetic to the folks looking to cut its funding.
If a discipline is spending public dollars at OpenAI and Anthropic, we're funding them with extra steps. (And losing nothing somebody else couldn't do.)
So, if you have ideas of a new, better system let's talk about those, before getting happy something gets destroyed and hope someone else will come with a better solution.
So with the current system, some things work, some things don't work (ex: psychology/Alzheimer). Yes, the system should be improved. No, I am not convinced that "destroying" the current system will result very easily into something better.
We need to discuss actual solutions for the replication crisis. There are even steps towards improving that, like requiring open data for papers, which makes it harder for people to do some of the manipulations that resulted in the replication crisis. I personally would go even further: you should provide complete documentation (tools, notes, data, raw files, etc.), but then there are some people opposing that due to "privacy" (for medical) or "patents" (for industrial stuff).
The peer review system as we know it today has only really existed for less than a century. It is no how science was traditionally done. It was adopted due to some real problems with the old system, so I'm not saying we should go back. But it's not clear either that unpaid peer-review is the ultimate end-state either.
100% agreement on your last paragraph though.
AI is a special case, as academia isn't usually that lucrative. In the rest of CS, many major conferences still have ~300 people, and interesting stuff often happens in specialized meetings with fewer than 100 participants.
Academia is a specific case. It happens everywhere.
So I might as well expound, innit.
There's a "maximum complexity of utterance" criterion in the firewall around the gene po'. Been getting lower since the golden age of Twitter, back when they planned revolutions on there, remember that? We didn't, they immolated one of our boys anyway.
Now all that's left is the internalized character limit.
Soon cognition (or its simulacrum, depending on how you feel about cognition and simulacrum-of-cognition being in fact one and the same) will be permissible only to bots; resilers will be trapped in personalized Skinner boxes of existential dread and mental collapse.
Nothing new under the sun til it's all computronium. Me, I'm still holding my breath for that libre folding phone. Instead, you're getting the alphabet and metacognition privatized. Billions in personhours, to squat a word. That's what they need to mimic a fraction of our power.
It's a sad thing. But the monetization and enshitification of journal publications over the decade or so, even prior to AI, certainly has not helped this trend.
It more looks like the breakdown of the current academic publication system, which was rotten to the core pre-AI and which internal contradictions are just accelerated by AI to the point of breakdown now.
AI is simply going to bury it as a useful system. What will grow from the ashes will be something much more dynamic, where verifiable data is the gold and the conclusions and associated details will persist only as the human-level translation.
In this case presumably the virtue is "able to churn out papers".
> because there are just so many papers at leading AI conferences
A low acceptance rate is unhealthy, especially in conferences (1 round of review). Papers just get recycled to the next conference, which, as is easy to model, creates an exponential feedback loop. It doesn't explain all the papers submitted, but it sure can explain a lot. Too much rejection is like shooting yourself in the foot.
Not to mention that it's just easy to reject works. All works are flawed, especially works that are in less mature domains. I see plenty a paper get rejected for lack of money. "Not enough experiments" is an common critique that's used inappropriately (along with the highly subjective "not novel enough" one) because it's fine to always want more but no lab has infinite funding. It is used lazily. The question shouldn't be about if your favorite benchmark is used, it should be if there isn't enough evidence to support the hypothesis or not. A mature domain where thousands of people work in it, yeah, that needs stronger evidence. A niche domain where dozens of people work in? Not as many required. Rejecting them ultimately slows down the progress of science because you require any new idea to outperform mature ideas. Ironically killing novelty as no one is going to, or even could (publish or perish), spend all the time and money to mature a niche all on their own.
Of course, there’s lots of room for subjectivity here; what constitutes the “best” research is still at the whim of reviewers.
> Reviewers know what venues they are reviewing for, and attentive ones will adapt their review based on the prestige of the venue.
I don't think it's constable that luck plays a big role. Do we need you do a third NeruIPS study to convince people?
The real problem is that we don't actually know if an idea is good or not until it's had more time to be explored and studied. A great example of this is diffusion models. There's 6 years between Sohl-Dickstein's paper and Jonathan Ho's. All because GANs got popular, so only a few people kept looking at diffusion until one person scaled it. There's hundreds of cases like that, including attention and resnets (I'll defend Schmidhuber's Highway Nets here). So much fruitful research gets cast away for no good reason.
A reviewer can't ever determine if research is good or impactful. It's impossible to do by just reading a paper. So that needs to be taken out of the equation. What a reviewer can do, though, is determine if a paper is bad or fraudulent. So IMO, we should publish anything that isn't fraudulent. Let time tell us the impact, because history tells us we're not very good at figuring that out ourselves
I know managers and administrators want a simple metric, but there is no simple metric for research.
The advantage of "publish or perish" is that it is based on something concrete. The system is gamed, for sure, and even more so with AI, but still, a paper which is cited a lot tend to be useful and the authors get rewarded.
Remove that metric, and for the lack of a better idea, it will just turn into a game of who has the best connections or who talks the most convincingly, I mean, even more than it is now.
Instead of wasting some resources on corruption and bad judgement, we now waste them by forcing people to chase the wrong goal. In other words: "If you rely on incentives, you undermine values." (Barry Schwartz)
So I don't disagree with you, but I think we went too far into that direction. We have allow people to use their better judgement sometimes.
Even with the technical debt. We have zillions of papers published, we know that most of them probably won't replicate, we don't know which ones.
Alas, it's probably just wishful thinking on my part.
You probably don't want to throw someone in jail for one plagiarized paper, so between jail time and losing your job, I don't know what else you have.
[0] https://retractionwatch.com/2026/07/27/cambridge-jason-arday...
Plagiarism brings a slap on the wrist unless the case is very high profile, like the example posted by azan_20 in the sibling where the plagiarising academic had many accolades and a big reputation. If the target or source of plagiarism is not famous it's like stealing bikes.
And of course if you're a senior academic being accused of misconduct by a less senior academic you can LOL about it because it's all a big joke and the less senior academic is the one who'll get a hit to their reputation for making a fuss and rocking the boat.
I'd like to clarify - the very high profile case did not even get wrist slap and Cambridge is actually defending him.
They should consider swapping this for a log plot.
I can imagine in 2027 academia looking like Moltbook.
This is certainly “directionally correct”, but keep in mind this is all highly uneven across fields and subfields. The people generating slop articles are mostly trying to publish big flashy things, and naturally ML research has it much worse than most other fields. There are many topics that are super important and interesting, but niche or obscure enough that no slop authors is trying to publish on them yet. So plenty of topics are still dominated by real earnest researchers doing their best, but they’re niche enough that you wouldn’t know about them unless you study that field.
If all these papers were not gatekept by journals, it would be trivially easy to validate at least the existence of cited papers and quotes.
Of course, you do need an API key and it took quite a while to get it last time I needed it, but it's there.
Yeah. It’s trivial to do for one paper while sitting at a computer in a university library. How trivial is it to validate the hundreds of sources and quotes that appear in any given volume of a journal?
From my perspective, it's a clear manifestation of humanity's most pervasive failing - the one that defines every group, eventually.
No One Anywhere Wants To Clean Their Own House.Checking the content is of course a lot more difficult, but that doesn't magically become trivial with open-access journals: you still need to read and interpret what is being said in the paper and compare it to the claims being made in the citation. Granted, these days you could use AI for a first pass, but it's still going to be incredibly tedious work.
Imagine being an author and paying to publish your novel. Also btw the editor is another author but has to proofread your book for free.
Many academics will publish preprints or their more popular papers on their own websites etc. It's just common sense because to academics they don't get paid a single cent by the publisher (and instead have to pay the publishers instead) and more publicity for them is always better than less.
That is... completely normal.
> Also btw the editor is another author but has to proofread your book for free.
That would be weirder; if you're paying for your own publication, you don't get an editor at all unless you hire them yourself.
Only at vanity presses. Money flows towards the author, as they say.
Peer review means 3 people spend their time to review your work and you don’t have to pay them a cent, so you review others' work for free. The compensation for reviewing is reviews.
They’re not always great (sometimes they’re downright nasty), but I’ve gotten enough high quality reviews over the years that I’m willing to say the time I’ve spent reviewing others’ work has been fairly compensated.
Is that really a thing? So if I submit a legit paper it is being reviewed forcefully by a bunch of random people from god knows where?
I was forced to review 7 papers.
Now, how are these papers chosen for you?
You get to bid on which to review.
Bid on 30 papers out of 30,000.
I got none of those, and I had to review 7 papers I wasn't really competent enough to review.
I went to smaller conferences, I suppose, but back then I also had to review papers outside my direct expertise, although nothing too remote. But I didn't know the literature well, obviously. I was able to weed out the sub-par papers (I think), but it was harder to estimate the merits of those that did make sense, as I only saw 1 or 2 per area. And that's what determines the acceptance, after all. No criticism because the reviewer judged it perfect or because the reviewer didn't know what to look for? Outcome is the same.
It doesn't scale.
So basically leave it up to the Area Chair. It still helps them a bit that you give the paper a quick once over and flag it up if it's somehow complete crap. The problem is that I'm probably missing the chance to review papers I'd be really interested in and that I could help to improve.
I guess it's a bit ridiculous that AI conferences in particular can't find a systematic way to route papers to subject matter experts. I guess the usual system with area and sub-area keywords is overwhelmed by the extreme increase in numbers. This year AAAI made link to my Google Scholar and DBLP, link my top five papers, and add some more info about my subject to my profile; and they still failed to rate the papers that were really relevant as most relevant. At least the bidding pool was a bit smaller this time so I guess they're trying to work something out.
Meanwhile submission numbers are exploding and I fear all the conferences can do is try to play catch up.
Another idea: make it less about the written word and more about artifacts. Require more proof, more protocols, more spreadsheets, more code, etc.
And stop caring about the words so much, as they really don’t mean anything anymore. Larger papers should be punished instead of rewarded; the shorter the paper (in terms of words) the higher the chance to be published. Every word should be backed up by data, code, etc. Any paragraph that doesn’t pair with proof should mean rejection.
As for it "being a thing": sadly yes. The number of papers has been steadily increasing over the years and the number of volunteer authors is simply not enough.
Most of the folks on HN will be more familiar with genAI tools and can just use the skill Caleb and Isaac provided in this blog post. My tool is just likely more token efficient and has a GUI where you can review the extracted bib and edit it more easily.
I do wonder what truthfully could be on ai verification, if even one paper with such an error is accepted it sets the precedent you hopefully get lucky to not get caught (then again verifying for basic tells isn't the same verifying is this genuinely a worthwhile publication, but that's a separate matter)
What? I mean who does this. My voice is my voice and it's literally never occurred to me to have an LLM do a first draft. I thought that was college kid stuff.
There are already standard keys to index publications, like DOIs. Requiring a list of DOIs for citations would make a lot more sense and be somewhat feasible, but still doesn’t prevent errors in the author list given in the draft.
> Submissions are confidential, bibliographies included, and the audit works by sending pieces of one to a hosted LLM – even though the LLM never writes a word of your review. ECCV 2026’s reviewing policies state that LLMs “are NOT allowed to be used to write reviews or meta-reviews, whether it is run locally or via an API,” and separately bar reviewers from sharing substantial excerpts of a submission with an LLM. WACV’s reviewer guidelines call LLM-generated reviews “highly irresponsible behavior,” sanctionable by desk rejection of the reviewer’s own papers, and their confidentiality rules forbid showing a submission’s material to anyone who is not a reviewer – which a hosted LLM is not. NeurIPS’s LLM policy restricts what reviewers can share with LLM services; its AI-assisted reviewing experiment is the sanctioned route.
So you can send some LLM generated crap to be published and even if you get caught, there is no consequences. Since your funding is likely connected to how much you publish, even if it’s toilet paper, so this system actually rewards one from spewing out shit papers no-one reads. But if you use LLM to review them, guess what, you will get punished harshly.
I think if you send in LLM crap with hallucinated citations you should get 5 year ban on even sending anything to that conference or publication. And perhaps we should create a local model -based application that filters out this crap. The one the authors had made is a good start, but surely you don’t need Claude to review a bibliography for errors? Surely Qwen with a SearXNG limited to arxiv etc. can do the job?
The genie is out of the bottle. We need to figure out a way to contain it. I think a solution is to use LLMs for peer reviews also; fight fire with fire?
https://arxiv.org/abs/2510.15061
I consider all types of "I liked this output, but don't the moment I learned it was AI generated" to be externalizations of "carbon chauvinism" (https://en.wikipedia.org/wiki/Carbon_chauvinism) and basically bigotry.
And BTW, the term "meritocracy" was coined in a book that was extremely critical of the idea and which argued that a real meritocracy is actually dystopian. We consider our work "harming meritocracy" to be a good outcome: (https://en.wikipedia.org/wiki/The_Rise_of_the_Meritocracy)