Tom Dietterich (Editor in Chief at arXiv) posted on LinkedIn the other day that they’re having trouble keeping up with the onslaught of AI-generated papers. Lots of suggestions in the comments but no magic bullets.
It’s ironic that the LLMs which benefit so much from reading arXiv papers of yore are now being used to pollute it. Personally if I see a single author post 2023, I assume it’s junk, especially if they’re not from an actual research institution. Not all solo independent researchers are phonies but … many phonies are solo independent researchers.
OpenReview is okay but even some of the reviewers are apparently using LLMs or just hardly reading.
A recent review of mine had a LLM-ism at the very end, "would you like me to format this into a formal peer review report?" So they very likely copy-pasted their whole review :). I'm pretty down on academia atm ;-;
Super unfortunate. On the other side, I reviewed four papers for a top-tier AI conference and three were clearly fully Claude generated, as in all text, figures, results, everything. Actual good reviewer time is wasted on such papers and your (i hope) human written good paper receives AI responses. It's a sad state of affairs for sure.
I find it incredibly sad that this is what the world is becoming, and I mostly blame people for this, not the LLMs.
It’s the same type of people that would have no issue letting an LLM open a pull request on GitHub wasting valuable time of other humans, and whatnot.
I’m using LLMs all the time myself, but it’s so incredibly important to use it to improve the quality of your work, not degrade it. People seem to be totally oblivious about this.
On the flip side, it does make it easier to recognize people who are wasting my time.
I think this is more of a systematic issue. I review now since 2-3 years, I do not get paid, which is fine. However, it takes always a huge amount of time without really having anything from it, but I do it because it is important work.
There now so many researcher that need to publish which explains the flooding, LLM only speed it up, so reciprocal reviews take place. So now you are forced to review and you are having less and less time. So it’s a natural choice for you if you already took an LLM to write a paper to use it to review.
Perhaps one solution would be much harder entry barriers, and enforcing some guidelines. For example that a supervisor can not have more than 5 papers and PhD students only need one real paper on a major conference/journal.
If you see any post on arXiv you should assume it is junk.
I find it ridiculous that people put any value on something being posted on arXiv. That doesn't mean the post is bad. It just means you need to find other means of judging it, for example by actually reading it.
I half agree. It used to be good, they called them "preprints" because they were already sent to a journal and somewhat expected to be accepted. Until people noticed that they were not forced to publish the "preprint" later so it got flooded with crap, hand crafted artisanal crap.
Unless you are working in the area of the paper AND know the reputation of the authors AND take a deep look, just give it the same credibility than to a random PDF posted in WordPress. They have some weak filtering because to post in the arXiv someone must vouch for you or something similar, but it's a very weak filter and people was already abusing it.
Asking earnestly: is ArXiv a valuable resource? I used it few years back when finishing my (late) Master's, but also saw a lot of crap published there (primarily for promotion/visa purposes) so it kind of took the shine out of the service for me.
My take: if not for arxiv and huggingface, the field of ML would be nowhere near where it is today.
More to your question, I recommend something like semanticscholar to find actual relevant papers. Try to identify researchers that seem trustworthy, then explore the citation network around them.
For more hot off the press stuff, follow what gets boosted on social media.
Still doesn’t cover the truly niche stuff but it’s a start
The preprint papers on arXiv are like 99% the same as the published versions, except they're free instead of locked behind a multi-thousand dollar/year paywall.
And if there are differences, that is often a good thing! It can mean the author wanted to format something in a particular way that the journal didn't allow.
It’s good for getting free access to preprints which are often close enough to the paywalled real papers in journals. If I find a paper I want (or more realistically, if ChatGPT finds a paper it wants for me), but it’s behind a paywall, odds are the authors put a preprint on arXiv.
Either you setup feeds for the specific topics/subjects you care about, scan what you come across once a week, or you use it to get access to papers that are usually behind some paywall. I don't think the intention nor the value comes from just haphazardously reading through everything in some section.
It's not peer-reviewed and supposed to free and accessible from both sides so the results kind of makes sense.
Tom Dietterich (Editor in Chief at arXiv) posted on LinkedIn the other day that they’re having trouble keeping up with the onslaught of AI-generated papers. Lots of suggestions in the comments but no magic bullets.
It’s ironic that the LLMs which benefit so much from reading arXiv papers of yore are now being used to pollute it. Personally if I see a single author post 2023, I assume it’s junk, especially if they’re not from an actual research institution. Not all solo independent researchers are phonies but … many phonies are solo independent researchers.
OpenReview is okay but even some of the reviewers are apparently using LLMs or just hardly reading.
A recent review of mine had a LLM-ism at the very end, "would you like me to format this into a formal peer review report?" So they very likely copy-pasted their whole review :). I'm pretty down on academia atm ;-;
Super unfortunate. On the other side, I reviewed four papers for a top-tier AI conference and three were clearly fully Claude generated, as in all text, figures, results, everything. Actual good reviewer time is wasted on such papers and your (i hope) human written good paper receives AI responses. It's a sad state of affairs for sure.
I find it incredibly sad that this is what the world is becoming, and I mostly blame people for this, not the LLMs.
It’s the same type of people that would have no issue letting an LLM open a pull request on GitHub wasting valuable time of other humans, and whatnot.
I’m using LLMs all the time myself, but it’s so incredibly important to use it to improve the quality of your work, not degrade it. People seem to be totally oblivious about this.
On the flip side, it does make it easier to recognize people who are wasting my time.
I think this is more of a systematic issue. I review now since 2-3 years, I do not get paid, which is fine. However, it takes always a huge amount of time without really having anything from it, but I do it because it is important work.
There now so many researcher that need to publish which explains the flooding, LLM only speed it up, so reciprocal reviews take place. So now you are forced to review and you are having less and less time. So it’s a natural choice for you if you already took an LLM to write a paper to use it to review.
Perhaps one solution would be much harder entry barriers, and enforcing some guidelines. For example that a supervisor can not have more than 5 papers and PhD students only need one real paper on a major conference/journal.
You’d better not look at the average CI pipeline in software shops then
someone was a meat proxy
https://gruhn.me/blog/2026-08-03/ in case someone missed the reference
Oh my god that was just written at the beginning of August?? It feels like I read it a year ago. We really are speed running this tech cycle…
If you see any post on arXiv you should assume it is junk. I find it ridiculous that people put any value on something being posted on arXiv. That doesn't mean the post is bad. It just means you need to find other means of judging it, for example by actually reading it.
I half agree. It used to be good, they called them "preprints" because they were already sent to a journal and somewhat expected to be accepted. Until people noticed that they were not forced to publish the "preprint" later so it got flooded with crap, hand crafted artisanal crap.
Unless you are working in the area of the paper AND know the reputation of the authors AND take a deep look, just give it the same credibility than to a random PDF posted in WordPress. They have some weak filtering because to post in the arXiv someone must vouch for you or something similar, but it's a very weak filter and people was already abusing it.
And then the AI slop truck hit...
Are they doing to do something about authors using Arxiv to publish propaganda/opinion pieces but presented as research?
ArXiv gives the appearance of scientific credibility that a blog post wouldn't have so I'm seeing the platform get abused.
Example: https://news.ycombinator.com/item?id=49580164
Probably not because the whole point is they don't do critical review
> ArXiv gives the appearance of scientific credibility that a blog post wouldn't have
That's an error on your side not theirs
Then just call it Substack.
Sorely, sorely needed.
How is science to evolve if good research requires $49 a pop to view?
And what amazes me is that these authors have undeniably stood on the shoulders of giants in order to create their research.
You think authors are the ones driving the $49 view fees? They are not.
They are, if their institution does not have contracts for that specific domain from Elsevier et al.
At my institute we for example do not have access to parts of Springer.
Luckily, more and more is moving towards open access.
Published there in August, from a company rather than a university. Without arXiv we'd have had a blog post and nothing citable.
A blog post is just as "citable" as an arXiv preprint. There's no fundamental difference between them.
One is immutable with a DOI.
> Published
Posted. Having a preprint on arxiv is not publishing.
Asking earnestly: is ArXiv a valuable resource? I used it few years back when finishing my (late) Master's, but also saw a lot of crap published there (primarily for promotion/visa purposes) so it kind of took the shine out of the service for me.
My take: if not for arxiv and huggingface, the field of ML would be nowhere near where it is today.
More to your question, I recommend something like semanticscholar to find actual relevant papers. Try to identify researchers that seem trustworthy, then explore the citation network around them.
For more hot off the press stuff, follow what gets boosted on social media.
Still doesn’t cover the truly niche stuff but it’s a start
A free-to-read PDF host site is valuable enough. Most academic publishers have a pay wall.
The preprint papers on arXiv are like 99% the same as the published versions, except they're free instead of locked behind a multi-thousand dollar/year paywall.
And if there are differences, that is often a good thing! It can mean the author wanted to format something in a particular way that the journal didn't allow.
Often they are extended versions of the journal articles and contain valuable material that had to be cut for space limits.
Some researchers and journals publish peer-reviewed work on arxiv; it serves as a stable archive that won't be plagued by link rot or paywalls.
It’s good for getting free access to preprints which are often close enough to the paywalled real papers in journals. If I find a paper I want (or more realistically, if ChatGPT finds a paper it wants for me), but it’s behind a paywall, odds are the authors put a preprint on arXiv.
Either you setup feeds for the specific topics/subjects you care about, scan what you come across once a week, or you use it to get access to papers that are usually behind some paywall. I don't think the intention nor the value comes from just haphazardously reading through everything in some section.
It's not peer-reviewed and supposed to free and accessible from both sides so the results kind of makes sense.