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 asked, would say that Foo and Bar are X, but "Everybody I have coffee with agrees" can't be cited, so we get this sort of junk citation.

Hopefully it's not crucial to the new work that Foo and Bar are in fact X. But that's not always the case, and it's a problem that years later somebody else will cite this paper, for the claim "Foo and Bar are X" which it was in fact merely citing erroneously.

>“consequence of a previous technological revolution: the internet.”

And also of increasingly ridiculous and overly broad concepts of what plagiarism is. At some point things shifted from “don’t represent others’ work as novel” towards “give a genealogical ontology of every concept above that of an intro 101 college course on the topic.”

It's also a consequence of the sheer number of building blocks which are involved in modern science.

In the methods section, it's very common to say "We employ method barfoo [1] as implemented in library libbar [2], with the specific variant widget due to Smith et al. [3] and the gobbledygook renormalization [4,5]. The feoozbar is solved with geometric multigrid [6]. Data is analyzed using the froiznok method [7] from the boolbool library [8]." There goes 8, now you have 2 citations left for the introduction.

It is not (just) consequence of the internet, the scientific production itself has grown exponentially. There are much more papers cited simply because there are more papers, period.

Not even the Internet per se but citation index becoming universally accepted KPI for research work.

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

> I've always assumed peer review is similar to diff review. Where I'm willing to sign my name onto the work of others. If I approve a diff/pr and it takes down prod. It's just as much my fault, no?

Ph.D. in neuroscience here. Programmer by trade. This is not true. Less you know about most peer revies is better.

The better peer reviews are also not this 'thorough' and no one expects reviewers to read or even check references. Unless they are citing something they are familiar with and you are using it wrong then they will likely complain. Or they find some unknown citations very relevant to their work, they will read.

I don't have a great analogy to draw here. peer review is usually a thankless and unpaid work so there is unlikely to be any motivation for fraud detection unless it somehow affects your work.

This is true, but here the equivalent situation is someone using a greek question mark (";") instead of a semicolon (";"), and you as a code reviewer are only expected to review the code visually and are not provided the resources required to compile the code on your local machine to see the compiler fail.

Yes in theory you can go through every semicolon to check if it's not actually a greek question mark; but one assumes good faith and baseline competence such that you as the reviewer would generally not be expected to perform such pedantic checks.

So if you think you might have reasonably missed greek question marks in a visual code review, then hopefully you can also appreciate how a paper reviewer might miss a false citation.

That is not, cannot be, and shouldn't be, the bar for peer review. There are two major differences between it and code review:

1. A patch is self-contained and applies to a codebase you have just as much access to as the author. A paper, on the other hand, is just the tip of the iceberg of research work, especially if there is some experiment or data collection involved. The reviewer does not have access to, say, videos of how the data was collected (and even if they did, they don't have the time to review all of that material).

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 refuted by another paper.

A reviewer is assessing the relevance and "impact" of a paper rather than correctness itself directly. Reviewers may not even have access to the data itself that authors may have used. The way it essentially works is an editor asks the reviewers "is this paper worthy to be published in my journal?" and the reviewers basically have to answer that question. The process is actually the editor/journal's responsibility.

> I've always assumed peer review is similar to diff review. Where I'm willing to sign my name onto the work of others. If I approve a diff/pr and it takes down prod. It's just as much my fault, no?

No.

Modern peer review is “how can I do minimum possible work so I can write ‘ICLR Reviewer 2025’ on my personal website”

For ICLR reviewers were asked to review 5 papers in two weeks. Unpaid voluntary work in addition to their normal teaching, supervision, meetings, and other research duties. It's just not possible to understand and thoroughly review each paper even for topic experts. If you want to compare peer review to coding, it's more like "no syntax errors, code still compiles" rather than pr review.

> Surely there are tools to retrieve all the citations,

Even if you could retrieve all citations (which isn't always as easy as you might hope) to validate citations you'd also have to confirm the paper says what the person citing it says. If I say "A GPU requires 1.4kg of copper" citing [1] is that a valid citation?

That means not just reviewing one paper, but also potentially checking 70+ papers it cites. The vast majority of paper reviewers will not check citations actually say what they're claimed to say, unless a truly outlandish claim is made.

At the same time, academia is strangely resistant to putting hyperlinks in citations, preferring to maintain old traditions - like citing conference papers by page number in a hypothetical book that has never been published; and having both a free and a paywalled version of a paper while considering the paywalled version the 'official' version.

[1] https://arxiv.org/pdf/2512.04142

how delightfully optimistic of you to think those abstracts would not also be ai generated ...

Thanks for sharing that. Interesting how there was a solution to a problem that didn't really exist yet.. I mean, I'm sure it was there for a reason, but I assume it was more things like wrongful attribution, missing commas etc. rather than outright invented quotes to fit a narrative or do you have more background on that?

...at least the mandatory automated checking processes are probably not far off at least for the more reputable journals, but it still makes you wonder how much you can trust the last two years of LLM-enhanced science that is now being quoted in current publications and if those hallucinations can be "reverted" after having been re-quoted. A bit like Wikipedia can be abused to establish facts.

The problem is most academics just do not have the time to do this for free, or in fact even if paid. In addition you may not even have access to the references. In acoustics it's not uncommon to cite works that don't even exist online and it's unlikely the reviewer will have the work in their library.

Agreed, and I'd go further. If nobody is reviewing citations they may as well not exist. Why bother?

There are a ton of edge cases and a bit of contextual understanding for what is a hallucinated citation (i.e. what if its republished from arxiv to ICLR?)

But to your point, seems we need a tool that can do this

It's not, there's lots of ways to resolve citations without even using AI.

I experimented a couple of years ago with getting LLMs to check citations but stopped working on it because there's no incentive. You could run a fancy expensive pipeline burning scarce GPU hours and find a bunch of bad citations. Then what? Nobody cares. No journal is going to retract any of these papers, the academics themselves won't care or even respond to your emails, nobody is willing to pay for this stuff, least of all the universities, journals or governments themselves.

For example, there's a guy in France who runs a pre-LLM pipeline to discover bad papers using hand-coded heuristics like regexs or metadata analysis e.g. checking if a citation has been retracted. Many of the things it detects are plagiarism, paper mills (i.e. companies that sell fake papers to academics for a profit), or the result of joke paper creators like SciGen.

https://dbrech.irit.fr/pls/apex/f?p=9999:1::::::

Other than populating an obscure database nobody knows about, this work achieved bupkis.

In theory, the review tries to determine if the conclusion reached actually follows from whatever data is provided. It assumes that everything is honest, it's just looking to see if there were mistakes made.

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