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Over fifty new hallucinations in ICLR 2026 submissions

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Re: Over fifty new hallucinations in ICLR 2026 submissions

#301

Post by HPsquared »

stdbrouw wrote: post #101
andy99 wrote: post #48

Earlier quoted context omitted.

I’ve reviewed a lot of papers, I don’t consider it the reviewers responsibility to manually verify all citations are real. If there was an unusual citation that was relied on heavily for the basis of the work, one would expect it to be checked. Things like broad prior work, you’d just assume it’s part of background. The reviewer is not a proofreader, they are checking the rigour and relevance of the work, which does…

The idea that references in a scientific paper should be plentiful but aren't really that important, is a consequence of a previous technological revolution: the internet. You'll find a lot of papers from, say, the '70s, with a grand total of maybe 10 references, all of them to crucial prior work, and if those references don't say what the author claims they should say (e.g. that the particular method that is employe…

Maybe there could be a system to classify the importance of each reference.

Re: Over fifty new hallucinations in ICLR 2026 submissions

#302

Post by HPsquared »

tialaramex wrote: post #139
stdbrouw wrote: post #101

Earlier quoted context omitted.

The idea that references in a scientific paper should be plentiful but aren't really that important, is a consequence of a previous technological revolution: the internet. You'll find a lot of papers from, say, the '70s, with a grand total of maybe 10 references, all of them to crucial prior work, and if those references don't say what the author claims they should say (e.g. that the particular method that is employe…

Whether in the 1970s or now, it's too often the case that a paper says "Foo and Bar are X" and cites two sources for this fact. You chase down the sources, the first one says "We weren't able to determine whether Foo is X" and never mentions Bar. The second says "Assuming Bar is X, we show that Foo is probably X too". The paper author likely believes Foo and Bar are X, it may well be that all their co-workers, if ask…

Wikipedia calls this citogenesis.

Re: Over fifty new hallucinations in ICLR 2026 submissions

#303

Post by cindyllm »

DonHopkins wrote: post #298
theoldgreybeard wrote: post #16

If a carpenter builds a crappy shelf “because” his power tools are not calibrated correctly - that’s a crappy carpenter, not a crappy tool. If a scientist uses an LLM to write a paper with fabricated citations - that’s a crappy scientist. AI is not the problem, laziness and negligence is. There needs to be serious social consequences to this kind of thing, otherwise we are tacitly endorsing it.

Shouldn't there be a black list of people who get caught writing fraudulent papers?

[dead]

Re: Over fifty new hallucinations in ICLR 2026 submissions

#304

Post by amrocha »

Yoofie wrote: post #226
kklisura wrote: post #195

Earlier quoted context omitted.

> AI is not the problem, laziness and negligence is This reminds me about discourse about a gun problem in US, "guns don't kill people, people kill people", etc - it is a discourse used solely for the purpose of not doing anything and not addressing anything about the underlying problem. So no, you're wrong - AI IS THE PROBLEM.

No, the OP is right in this case. Did you read TFA? It was "peer reviewed". > Worryingly, each of these submissions has already been reviewed by 3-5 peer experts, most of whom missed the fake citation(s). This failure suggests that some of these papers might have been accepted by ICLR without any intervention. Some had average ratings of 8/10, meaning they would almost certainly have been published. If the peer revie…

If AI fraud is good at avoiding detection via peer review that doesn’t mean peer review is useless.

If your unit tests don’t catch all errors it doesn’t mean unit tests are useless.

Re: Over fifty new hallucinations in ICLR 2026 submissions

#306

Post by vkou »

noitpmeder wrote: post #180
merely-unlikely wrote: post #164

Earlier quoted context omitted.

This discussion makes me think peer reviews need more automated tooling somewhat analogous to what software engineers have long relied on. For example, a tool could use an LLM to check that the citation actually substantiates the claim the paper says it does, or else flags the claim for review.

I'd go one further and say all published papers should come with a clear list of "claimed truths", and one is only able to cite said paper if they are linking in to an explicit truth. Then you can build a true hierarchy of citation dependencies, checked 'statically', and have better indications of impact if a fundamental truth is disproven, ...

Have you authored a lot of non-CS papers?

Could you provide a proof of concept paper for that sort of thing? Not a toy example, an actual example, derived from messy real-world data, in a non-trivial[1] field?

---

[1] Any field is non-trivial when you get deep enough into it.

Re: Over fifty new hallucinations in ICLR 2026 submissions

#307

Post by amrocha »

embedding-shape wrote: post #280
bossyTeacher wrote: post #242

Earlier quoted context omitted.

Well, then what does this say of LLM engineers at literally any AI company in existence if they are delivering AI that is unreliable then? Surely, they must take responsibility for the quality of their work and not blame it on something else.

I feel like what "unreliable" means, depends on well you understand LLMs. I use them in my professional work, and they're reliable in terms of I'm always getting tokens back from them, I don't think my local models have failed even once at doing just that. And this is the product that is being sold. Some people take that to mean that responses from LLMs are (by human standards) "always correct" and "based on knowledg…

it’s not “some people”, it’s practically everyone that doesn’t understand how these tools work, and even some people that do.

Lawyers are running their careers by citing hallucinated cases. Researchers are writing papers with hallucinated references. Programmers are taking down production by not verifying AI code.

Humans were made to do things, not to verify things. Verifying something is 10x harder than doing it right. AI in the hands of humans is a foot rocket launcher.

Re: Over fifty new hallucinations in ICLR 2026 submissions

#308

Post by vkou »

hnfong wrote: post #205
grayhatter wrote: post #151

Earlier quoted context omitted.

> That is not, cannot be, and shouldn't be, the bar for peer review. Given the repeatability crisis I keep reading about, maybe something should change? > 2. The software is also self-contained. That's "prodcution". But a scientific paper does not necessarily aim to represent scientific consensus, but a finding by a particular team of researchers. If a paper's conclusions are wrong, it's expected that it will be refu…

IMHO what should change is we stop putting "peer reviewed" articles on a pedestal. Even if peer review is as rigorous as code reviewed (the former which is usually unpaid), we all know that reviewed code still has bugs, and a programmer would be nuts to go around saying "this code is reviewed by experts, we can assume it's bug free, right?" But there are too many people who are just assuming peer reviewed articles me…

> IMHO what should change is we stop putting "peer reviewed" articles on a pedestal.

Correct. Peer review is a minimal and necessary but not sufficient step.

Re: Over fifty new hallucinations in ICLR 2026 submissions

#309

Post by xnx »

legostormtroopr wrote: post #296
xnx wrote: post #289

Earlier quoted context omitted.

No doubt the best electricians are currently better than the best AI, but the best AI is likely now better than the novice homeowner. The trajectory over the past 2 years has been very good. Another five years and AI may be better than all but the very best, or most specialized, electricians.

Current state AI doesn’t have hands. How can it possibly be better at installing electrics than anyone? Your post reads like AI precisely because while the grammar is fine, it lacks context - like someone prompted “reply that AI is better than average”.

An electrician with total knowledge/understanding, but only the average dexterity of a non-professional would still be very useful.

Re: Over fifty new hallucinations in ICLR 2026 submissions

#310

Post by nwallin »

theoldgreybeard wrote: post #16

If a carpenter builds a crappy shelf “because” his power tools are not calibrated correctly - that’s a crappy carpenter, not a crappy tool. If a scientist uses an LLM to write a paper with fabricated citations - that’s a crappy scientist. AI is not the problem, laziness and negligence is. There needs to be serious social consequences to this kind of thing, otherwise we are tacitly endorsing it.

"Anyone, from the most clueless amateur to the best cryptographer, can create an algorithm that he himself can’t break."--Bruce Schneier

There's a corollary here with LLMs, but I'm not pithy enough to phrase it well. Anyone can create something using LLMs that they, themselves, aren't skilled enough to spot the LLMs' hallucinations. Or something.

LLMs are incredibly good at exploiting peoples' confirmation biases. If it "thinks" it knows what you believe/want, it will tell you what you believe/want. There does not exist a way to interface with LLMs that will not ultimately end in the LLM telling you exactly what you want to hear. Using an LLM in your process necessarily results in being told that you're right, even when you're wrong. Using an LLM necessarily results in it reinforcing all of your prior beliefs, regardless of whether those prior beliefs are correct. To an LLM, all hypotheses are true, it's just a matter of hallucinating enough evidence to satisfy the users' skepticism.

I do not believe there exists a way to safely use LLMs in scientific processes. Period. If my belief is true, and ChatGPT has told me it's true, then yes, AI, the tool, is the problem, not the human using the tool.

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