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Comment by JBorrow | Hacker News Reader

From my perspective as a journal editor and a reviewer these kinds of tools cause many more problems than they actually solve. They make the 'barrier to entry' for submitting vibed semi-plausible journal articles much lower, which I understand some may see as a benefit. The drawback is that scientific editors and reviewers provide those services for free, as a community benefit. One example was a submission their undergraduate affiliation (in accounting) to submit a paper on cosmology, entirely vibe-coded and vibe-written. This just wastes our (already stretched) time. A significant fraction of submissions are now vibe-written and come from folks who are looking to 'boost' their CV (even having a 'submitted' publication is seen as a benefit), which is really not the point of these journals at all.

I'm not sure I'm convinced of the benefit of lowering the barrier to entry to scientific publishing. The hard part always has been, and always will be, understanding the research context (what's been published before) and producing novel and interesting work (the underlying research). Connecting this together in a paper is indeed a challenge, and a skill that must be developed, but is really a minimal part of the process.

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GenAI largely seems like a DDoS on free resources. The effort to review this stuff is now massively more than the effort to "create" it, so really what is the point of even submitting it, the reviewer could have generated it themself. Seeing it in software development where coworkers are submitting massive PRs they generated but hardly read or tested. Shifting the real work to the PR review.

I'm not sure what the final state would be here but it seems we are going to find it increasingly difficult to find any real factual information on the internet going forward. Particularly as AI starts ingesting it's own generated fake content.

More relevant than ever:

> The amount of energy needed to refute bullshit is an order of magnitude bigger than that needed to produce it.

https://en.wikipedia.org/wiki/Brandolini%27s_law

The P≠NP conjecture in CS says checking a solution is easier than finding one. Verifying a Sudoku is fast; solving it from scratch is hard. But Brandolini's Law says the opposite: refuting bullshit costs way more than producing it.

Not actually contradictory. Verification is cheap when there's a spec to check against. 'Valid Sudoku?' is mechanical. But 'good paper?' has no spec. That's judgment, not verification.

> The P≠NP conjecture in CS says checking a solution is easier than finding one...

... for NP-hard problems.

It says nothing about the difficulty of finding or checking solutions of polynomial ("P") or exponential ("EXPTIME") problems.

producing BS can be equated to generating statements without caring for their truth value. Generating them is easy. Refuting them requires one to find a proof or a contradiction which is a lot of work, and is equal to "solving" the statement. As an analogy, refuting BS is like solving satisfiability, whereas generating BS is like generating propositions.

It's not contradictory because solving and producing bullshit are very different things. Generating less than 81 random numbers between 1 and 9 is probably also cheaper than verifying correctness of a sudoku.

Wow the 3 comments from OC to here are all bangers, they combine into a really nice argument against these toys

> The effort to review this stuff is now massively more than the effort to "create" it

I don't doubt the AI companies will soon announce products that will claim to solve this very problem, generating turnkey submission reviews. Double-dipping is very profitable.

It appears LLM-parasitism isn't close to being done, and keeps finding new commons to spoil.

There are a dozen startups that do this.

> Seeing it in software development where coworkers are submitting massive PRs they generated but hardly read or tested. Shifting the real work to the PR review.

I've seen this complaint a lot of places, but the solution to me seems obvious. Massive PRs should be rejected. This was true before AI was a thing.

In some ways it might be a good thing that shorthand signals of quality are being destroyed because it forces all of us to meaningfully engage with the work. No more LGTM +1 when every PR looks good.

AI slop security reports submitted to curl - https://gist.github.com/bagder/07f7581f6e3d78ef37dfbfc81fd1d...

HN Search: curl AI slop - https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...

This one is hilarious. https://hackerone.com/reports/3516186

If I submitted this, I'd have to punch myself in the face repeatedly.

The great disappointment is that the humans submitting these just don’t care it’s slop and they’re wasting another human’s time. To them, it’s a slot machine you just keep cranking the arm of until coins come out. “Prompt until payout.”

I'm scared that this type of thing is going to do to science journals what AI-generated bug reports is doing to bug bounties. We're truly living in a post-scarcity society now, except that the thing we have an abundance of is garbage, and it's drowning out everything of value.

In a corollary to Sturgeon's Law, I'd propose Altman's Law: "In the Age of AI, 99.999...% of everything is crap"

Altman's Law: 99% of all content is slop

I can get behind this. This assumes a tool will need to be made to help determine the 1% that isn't slop. At which point I assume we will have reinvented web search once more.

Has anyone looked at reviving PageRank?

I mean Kagi is probably the PageRank revival we are talking about.

I have heard from people here that Kagi can help remove slop from searches so I guess yeah.

Although I guess I am DDG user and I love using DDG as well because its free as well but I can see how for some price can be a non issue and they might like kagi more.

So Kagi / DDG (Duckduckgo) yeah.

I’ve been a Kagi subscriber for a while now. Recently picked up ChatGPT Business and now am considering dropping Kagi since I am only using it for trivial searches. Every comparison I’ve done with deep searches by hand and with AI ended up with the same results in far less time using AI.

Does anyone have kept an eye of who uses what back-end?

DDG used to be meta-search on top of Yahoo, which doesn't exist anymore. What do Gabriel and co-workers use now?

I think they all use Bing now.

Kagi is mostly stealing results from Google and disenshittifying them but mixes in other engines like Yandex and Mojeek and Bing.

DDG is Bing.

For images surely this is the next pivot for hot dog / not hot dog.

There's this thing where all the thought leaders in software engineering ask "What will change about building about building a business when code is free" and while, there are some cool things, I've also thought, like it could have some pretty serious negative externalities? I think this question is going to become big everywhere - business, science, etc. which is like - Ok, you have all this stuff, but do is it valuable? Which of it actually takes away value?

I think about this more and more when I see people online about their "agents managing agents" producing...something...24/7/365.

Very rarely is there anything about WHAT these agents are producing and why it's important and valuable.

Indeed - there is a lot of fake "productivity" going on with these swarms of agents

To be fair, the question “what will change” does not presume the changes will be positive. I think it’s the right question to ask, because change is coming whether we like it or not. While we do have agency, there are large forces at play which impact how certain things will play out.

The value is in the same place: solving people's problems.

Now that the code is cheaper (not free quite yet) skills further up the abstraction chain become more valuable.

Programming and design skills are less valuable. However, you still have to know what to build: product and UX skills are more valuable. You still have to know how to build it: software architect skills are more valuable.

The first casualty of LLMs was the slush pile--the unsolicited submission pile for publishers. We've since seen bug bounty programs and open source repositories buckle under the load of AI-generated contributions. And all of these have the same underlying issue: the LLM makes it easy to do things that don't immediately look like garbage, which makes the volume of submission skyrocket while the time-to-reject also goes up slightly because it passes the first (but only the first) absolute garbage filter.

I run a small print-on-demand platform and this is exactly what we're seeing. The submissions used to be easy to filter with basic heuristics or cheap classifiers, but now the grammar and structure are technically perfect. The problem is that running a stronger model to detect the semantic drift or hallucinations costs more than the potential margin on the book. We're pretty much back to manual review which destroys the unit economics.

Why would detecting AI be more expensive than creating it?

If it's print-on-demand, why does it matter? Why shouldn't you accept someone's money to print slop for them?

Some book houses print on demand for wide audiences. It's not just for the author.

Soon, poor people will talk to a LLM, rich people will get human medical care.

I mean I'm currently getting "expensive" medical care and the doctors are still all using AI scribes. I wouldn't assume there would be a gap in anything other than perception. I imagine doctors that cater to the fuck you rich will just put more effort into hiding it.

No one, at all levels, wants to do notes.

My experience has been that the transcriptions are way more detailed and correct when doctors use these scribes.

You could argue that not writing down everything provides a greater signal-noise ratio. Fair enough, but if something seemingly inconsequential is not noted and something is missed, that could worsen medical care.

I'm not sure how this affects malpractice claims - It's now easier to prove (with notes) that the doc "knew" about some detail that would otherwise not have been note down.

Digital pollution.

I totally agree. I spend my whole day from getting up to going to bed (not before reading HN!) on reviews for a conference I'm co-organizing later this year.

So I was not amused about this announcement at all, however easy it may make my own life as an author (I'm pretty happy to do my own literature search, thank you very much).

Also remember, we have no guarantee that these tools will still exist tomorrow, all these AI companies are constantly pivoting and throwing a lot of things at the wall to see what sticks.

OpenAI chose not to build a serious product, as there is no integration with the ACM DL, the IEEE DL, SpringerNatureLink, the ACL Anthology, Wiley, Cambridge/Oxford/Harvard University Press etc. - only papers that are not peer reviewed (arXiv.org) are available/have been integrated. Expect a flood of BS your way.

When my student submit a piece of writing, I can ask them to orally defend their opus maximum (more and more often, ChatGPT's...); I can't do the same with anonymous authors.

Speaking of conferences, might this not be the way to judge this work? You could imagine only orally defended work to be publishable, or at least have the prestige of vetting, in a bit of an old-school science revival.

Chicken and egg problem: since conferences have limited capacity, you need to pre-filter submissions to see who gets a presentation spot.

Self-solving problem: AI oral exam administration: https://www.gatech.edu/news/2024/09/24/ai-oral-assessment-to...

I wonder if there's a way to tax the frivolous submissions. There could be a submission fee that would be fully reimbursed iff the submission is actually accepted for publication. If you're confident in your paper, you can think of it as a deposit. If you're spamming journals, you're just going to pay for the wasted time.

Maybe you get reimbursed for half as long as there are no obvious hallucinations.

The journal that I'm an editor for is 'diamond open access', which means we charge no submission fees and no publication fees, and publish open access. This model is really important in allowing legitimate submissions from a wide range of contributors (e.g. PhD students in countries with low levels of science funding). Publishing in a traditional journal usually costs around $3000.

Those journals are really good for getting practice in writing and submitting research papers, but sometimes they are already seen as less impactful because of the quality of accepted papers. At least where I am at, I don't think the advent of AI writing is going to affect how they are seen.

In the field of Programming Languages and Formal Methods, many of the top journals and conference proceedings are open access

Who pays the operating expenses?

Welcome to new world of fake stuff i guess

If the penalty for a crime is a fine, then that law exists only for the lower class

In other words, such a structure would not dissuade bad actors with large financial incentives to push something through a process that grants validity to a hypothesis. A fine isn't going to stop tobacco companies from spamming submissions that say smoking doesn't cause lung cancer or social media companies from spamming submissions that their products aren't detrimental to the mental health.

> In other words, such a structure would not dissuade bad actors with large financial incentives to push something through a process that grants validity to a hypothesis.

That's not the right threat model. The existing peer review process is already weak to high-effort but conflicted research.

Instead, the threat model is closer one closer to that of spam, where the submitting authors don't care about the content of their submission at all but need X publications in high-impact outlets for their CV or grant application. Predatory journals exploit this as part of a pay-to-play problem, but the low reputation of those journals limits their desirable impact factor.

This threat model relies on frequent but low-quality submissions, and a submission fee would make taking multiple kicks at the can unviable.

I'm sure my crude idea has it's shortcomings, but this feels superfluous. Deep-pocketed propagandists can do all sorts of things to pump their message whether a slop tax exists or not. There may or may not be existing countermeasures at journals for that. This just isn't really about that. It's about making sure that, in the process of spamming the journal, they also fund the review process, which would otherwise simply bleed time and money.

That would be tricky, I often submitted to multiple high impact journals going down the list until someone accepted it. You try to ballpark where you can go but it can be worth aiming high. Maybe this isn't a problem and there should be payment for the efforts to screen the paper but then I would expect the reviewers to be paid for their time.

I mean your methodology also sounds suspect. You're just going down a list until it sticks. You don't care where it ends up (I'm sure within reason) just as long as it is accepted and published somewhere (again, within reason).

No different from applying to jobs. Much like companies, there are a variety of journals with varying levels of prestige or that fit your paper better/worse. You don't know in advance which journals will respond to your paper, which ones just received submissions similar to yours, etc.

Plus, the t in me from submission to acceptance/rejection can be long. For cutting edge science, you can't really afford to wait to hear back before applying to another journal.

All this to say that spamming 1,000 journals with a submission is bad, but submitting to the journals in your field that are at least decent fits for your paper is good practice.

Scientists are incentivized to publish in as high-ranking a journal as possible. You’re always going to have at least a few journals where your paper is a good fit, so aiming for the most ambitious journal first just makes sense.

It's standard practice, nothing suspect about their approach - and you won't go lower and lower and lower still because at some point you'll be tired of re-formatting, or a doctoral candidate's funding will be used up, or the topic has "expired" (= is overtaken by reality/competition).

This is effectively standard across the board.

Are you at all aware of how scientific publishing works?

You must have no idea how scientific publishing works. Typical acceptance rate for ok/good journal is 10-20% (and it was like that even before LLMs). Also it's a great idea to make business of scientific publishing even more predatory - now sciencists writing articles for free, reviewing for free and then having to pay for publication will also have to pay to even submit something, with 90% chance of rejection. Also think what kind of incentives it will create.

Pay to publish journals already exist.

This is sorta the opposite of pay to publish. It's pay to be rejected.

I would think it would act more like a security deposit, and you'd get back 100%, no profit for the journal (at least in that respect).

I'm pretty sure the reviewers of those are still volunteers, the publisher is just making even more money!

I’d worry about creating a perverse incentive to farm rejected submissions. Similar to those renter application fee scams.

Pay to review is common in Econ and Finance.

Variation I thought of on pay-to-review:

Suppose you are an independent researcher writing a paper. Before submitting it for review to journals, you could hire a published author in that field to review it for you (independently of the journal), and tell you whether it is submission-worthy, and help you improve it to the point it was. If they wanted, they could be listed as coauthor, and if they don't want that, at least you'd acknowledge their assistance in the paper.

Because I think there are two types of people who might write AI slop papers: (1) people who just don't care and want to throw everything at the wall and see what sticks; (2) people who genuinely desire to seriously contribute to the field, but don't know what they are doing. Hiring an advisor could help the second group of people.

Of course, I don't know how willing people would be to be hired to do this. Someone who was senior in the field might be too busy, might cost too much, or might worry about damage to their own reputation. But there are so many unemployed and underemployed academics out there...

Better yet, make a "polymarket" for papers where people can bet on which paper can make it, and rely on "expertise arbitrage" to punish spams.

Doesn't stop the flood, i.e. the unfair asymmetry between the effort to produce vs. effort to review.

Not if submissions require some small mandatory bet.

Now accepting money from slop companies to verify their slop as notslop

> There could be a submission fee that would be fully reimbursed if the submission is actually accepted for publication.

While well-intentioned, I think this is just gate-keeping. There are mountains of research that result in nothing interesting whatsoever (aside from learning about what doesn't work). And all of that is still valuable knowledge!

Sure, but now we can't even assume that such research is submitted in good faith anymore. There just seems to be no perfect solution.

Maybe something like a "hierarchy/DAG? of trusted-peers", where groups like universities certify the relevance and correctness of papers by attaching their name and a global reputation score to it. When it's found that the paper is "undesirable" and doesn't pass a subsequent review, their reputation score deteriorates (with the penalty propagating along the whole review chain), in such a way that:

- the overall review model is distributed, hence scalable (everybody may play the certification game and build a reputation score while doing so) - trusted/established institutions have an incentive to keep their global reputation score high and either put a very high level of scrutiny to the review, or delegate to very reputable peers - "bad actors" are immediately punished and universally recognized as such - "bad groups" (such as departments consistently spamming with low quality research) become clearly identified as such within the greater organisation (the university), which can encourage a mindset of quality above quantity - "good actors within a bad group" are not penalised either because they could circumvent their "bad group" on the global review market by having reputable institutions (or intermediaries) certify their good work

There are loopholes to consider, like a black market of reputation trading (I'll pay you generously to sacrifice a bit of your reputation to get this bad science published), but even that cannot pay off long-term in an open system where all transactions are visible.

Incidentally, I think this may be a rare case where a blockchain makes some sense?

You have some good ideas there, it's all about incentives and about public reputation.

But it should also fair. I once caught a team at a small Indian branch of a very large three letter US corporation violating the "no double submission" rule of two conferences: they submitted the same paper to two conferences, both naturally landed in my reviewer inbox, for a topic I am one of the experts in.

But all the other employees should not be penalized by the violations of 3 researchers.

This idea looks very similar to journals! Each journal has a reputation, if they publish too much crap, the crap is not cited and the impact factors decrease. Also, they have an informal reputation, because impact index also has problems.

Anyway, how will universities check the papers? Somone must read the preprints, like the current reviewers. Someone must check the incoming preprints, find reviewers and make the final decition, like the current editors. ...

How would this work for independent researchers?

(no snark)

This keeps repeating in different domains: we lower the cost of producing artifacts and the real bottleneck is evaluating them.

For developers, academics, editors, etc... in any review driven system the scarcity is around good human judgement not text volume. Ai doesn't remove that constraint and arguably puts more of a spotlight on the ability to separate the shit from the quality.

Unless review itself becomes cheaper or better, this just shifts work further downstream and disguising the change as "efficiency"

This has been discussed previously as "workslop", where you produce something that looks at surface level like high quality work, but just shifts the burden to the receiver of the workslop to review and fix.

This fits into the broader evolution of the visualization market. As data grows, visualization becomes as important as processing. This applies not only to applications, but also to relating texts through ideas close to transclusion in Ted Nelson’s Xanadu. [0]

In education, understanding is often best demonstrated not by restating text, but by presenting the same data in another representation and establishing the right analogies and isomorphisms, as in Explorable Explanations. [1]

[0] https://news.ycombinator.com/item?id=40295661

[1] https://news.ycombinator.com/item?id=22368323

> Unless review itself becomes cheaper or better, this just shifts work further downstream and disguising the change as "efficiency"

Or the providers of the models are capable of providing accepted/certified guarantees as to the quality of the output that their models and systems produce.

I'm curious if you'd be in favor of other forms of academic gate keeping as well. Isn't the lower quality overall of submissions (an ongoing trend with a history far pre-dating LLMs) an issue? Isn't the real question (that you are alluding to) whether there should be limits to the democratization of science? If my tone seems acerbic, it is only because I sense cognitive dissonance between the anti-AI stance common among many academics and the purported support for inclusivity measures.

"which is really not the point of these journals at all"- it seems that it very much is one of the main points? Why do you think people publish in journals instead of just putting their work on the arxiv? Do you think postdocs and APs are suffering through depression and stressing out about their publications because they're agonizing over whether their research has genuinely contributed substantively to the academic literature? Are academic employers poring over the publishing record of their researchers and obsessing over how well they publish in top journals in an altruistic effort to ensure that the research of their employees has made the world a better place?

I don't really understand how me saying that this tool isn't good for science as gatekeeping. The vibe-written papers that I am talking about have little-to-no valuable scientific content, and as such would always be rejected. It's just that it's way easier to produce something that _looks_ reasonable from a five-second glance than before, and that causes additional load on an already strained system.

I also don't understand your second paragraph at all.

> whether there should be limits to the democratization of science?

That is an interesting philosophical question, but not the question we are confronted with. A lot of LLM assisted materials have the _signals_ of novel research without having its _substance_.

LLMs are tools. In the hands of adept, conscientious researchers, they can only be a boon, assisting in the crafting of the research manuscript. In the hands of less adept, less conscientious users, they accelerate the production of slop. The poster I'm responding to seems to be noting an asymmetry- those who find the most use from these tools could be inept researchers who have no business submitting their work. This is because experienced researchers find writing up their results relatively easy.

To me, this is directly relevant to the issue of democratization of science. There seems to be a tool that is inconveniently resulting in the "wrong" people accelerating their output. That is essentially the complaint here rather than any criticism inherent to LLMs (e.g. water/resource usage, environmental impact, psychological/societal harm, etc.). The post I'm responding to could have been written if LLMs were replaced by any technology that resulted in less experienced or capable researchers disproportionately being able to submit to journals.

To be concrete, let's just take one of prism's capabilities- the ability to "turn whiteboard equations or diagrams directly into LaTeX". What a monstrous thing to give to the masses! Before, those uneducated cranks would send word docs to journals with poorly typeset equations, making it a trivial matter to filter them into the trash bin. Now, they can polish everything up and pass off their chicken scratch as respectable work. Ideally, we'd put up enough obstacles so that only those who should publish will publish.

The LLMs does assist the adept researchers in crafting their manuscript, but I do not think it makes the quality much better.

My objection is not that they are the "wrong people". They are just regular people with excellent tools but not necessarily great scientific ideas.

Yes, it was easier to trash the crank's work before based on their unLaTeXed diagrams. Now, they might have a very professional looking diagram, but their work is still not great mathematics. Except that now the editor has a much harder time finding out who submitted a worthwhile paper

In what way do you think the feature of "LaTeXing a whiteboard diagram" is democritizing mathematics? I do not think there are many people who have exceptional mathematical insights but are not able to publish them because they are not able to typeset their work properly.

The democratization is mostly in allowing people from outside the field with mediocre mathematical ideas to finally put them to paper and submit them to mediocre journals. And occasionally it might help a modern day Ramanujan with "exceptional mathematical insights" and a highly unconventional background to not have his work dismissed as that of a crank. Yes, most people with exceptional mathematical insights can typeset quite well. Democratization as I understand the term has quite a higher bar though.

Being against this is essentially to be in favor of a form of discrimination by proxy- if you can't typeset, then likely you can't do research either. And wouldn't it be really annoying if those people who can't research could magically typeset. It's a fundamentally undemocratic impulse: Since those who cannot typeset well are unlikely to produce quality mathematics, we can (and should) use this as an effective barrier to entry. If you replace ability to typeset with a number of other traits, they would be rather controversial positions.

It would indeed be nice if there were a mechanism to find people like Ramanujan who have excellent insights but cannot communicate them effectively.

But LLMs are not really helping. With all the beautifully typeset papers with immaculate prose, Ramanujan's papers are going to be buried deeper!

To some extent, I agree with you that it is a "discrimination by proxy", especially with the typesetting example. But you could think of examples where cranks could very easily fool themselves into thinking that they understand the essence of the material without understanding the details. E.g, [I understand fluid dynamics very well. No, I don't need to work out the differential equations. AI can do the bean counting for me.]

See the point #1 in famous Ten Signs a Claimed Mathematical Breakthrough is Wrong:

https://scottaaronson.blog/?p=304

By far the easiest quality signal is now out of the window.

The people on the inside often like all the gatekeeping.

If I may be the Devil's advocate, I'm not sure I fully agree with "The hard part always has been, and always will be, understanding the research context (what's been published before) and producing novel and interesting work (the underlying research)".

Plenty of researchers hate writing and will only do it at gunpoint. Or rather, delegate it all to their underlings.

I don't see an issue with generative writing in principle. The Devil is in the details, but I don't see this as much different from "hey grad student, write me this paper". And generative writing already exists as copy-paste, which makes up like 90% of any random paper given the incrementality of it all.

I was initially a little indignated by the "find me some plausible refs and stick them in the paper" section of the video but, then again, isn't this what most people already do? Just copy-paste the background refs from the colleague's last paper introduction and maybe add one from a talk they saw in the meantime, plus whatever the group & friends produced since then.

My experience is most likely skewed (as all are), but I haven't met a permanent researcher that wrote their own papers yet, and most grad students and postdocs hate writing. Literally the only times I saw someone motivated to write papers (in a masochistic way) were just before applying to a permanent position or while wrapping up their PhD.

Onto your point, though, I agree this is somewhat worrisome in that, by reaction, the barrier to entry might rise by way of discriminating based on credentials.

Thank you for bringing this nuanced view.

I also am not sure why so many people are vehemently against this. I would bet that at least 90% of researchers would agree that the writing up is definitely not the part of the work they prefer (to stay polite). As you mentioned, work is usually relegated to students, and those students already had access to LLMs if they wanted to generate the work.

In my opinion, most of those tools become problematic when people use them without caution. Unfortunately, even in sciences, people are not as careful and pragmatic as we would like to imagine they are and a lot of people are cutting corners, especially in those "lesser" areas like writing and presenting your work.

Overall, I think this has the potential to reshape the publication system, which is long overdue.

I am a rather slow writer who certainly might benefit from something like Prism.

A good tool would encourage me, help me while I am writing, and maybe set up barriers that keep me from taking shortcuts (e.g. pushing me to re-read the relevant paragraphs of a paper that I cite).

Prism does none of these things - instead it pushes me towards sloppy practices, such as sprinkling citations between claims. Why won't ChatGPT tell me how to build a bomb but Prism will happily fabricate fake experimental results for me?

The comparison to make here is that a journal submission is effectively a pull request to humanities scientific knowlegde base. That PR has to be reviewed. We're already seeing the effects of this with open source code - the number of PR submissions have skyrocketed, overwhelming maintainers.

This is still a good step in a direction of AI assisted research, but as you said, for the moment it creates as many problems as it solves.

I generally agree.

On the other hand, the world is now a different place as compared to when several prominent journals were founded (1869-1880 for Nature, Science, Elsevier). The tacit assumptions upon which they were founded might no longer hold in the future. The world is going to continue to change, and the publication process as it stands might need to adapt for it to be sustainable.

As I understand it, the problem isn't publication or how it's changing over time, it's about the challenges of producing new science when the existing one is muddied in plausible lies. That warrants a new process by which to assess the inherent quality of a paper, but even if it comes as globally distributed, the cheats have a huge advantage considering the asymmetry between the effort to vibe produce vs. the tedious human review.

That’s a good point. On the other hand, we’ve had that problem long before AI. You already need to mentally filter papers based on your assessment of the reputability of the authors.

The whole process should be made more transparent and open from the start, rather than adding more gatekeeping. There ought to be openness and transparency throughout the entire research process, with auditing-ability automatically baked in, rather than just at the time of publication. One man’s opinion, anyway.

As a non-scientist (but long-time science fan and user), I feel your pain with what appears to be a layered, intractable problem.

> > who are looking to 'boost' their CV

Ultimately, this seems like a key root cause - misaligned incentives across a multi-party ecosystem. And as always, incentives tend to be deeply embedded and highly resistant to change.

I appreciate and sympathize with this take. I'll just note that, in general, journal publications have gone considerably downhill over the last decade, even before the advent of AI. Frequency has gone up, quality has gone down, and the ability to actually check if everything in the article is actually valid is quite challenging as frequency goes up.

This is a space that probably needs substantial reform, much like grad school models in general (IMO).

Perhaps the real issue is the gate-keeping scientific publishing model. Journals had a place and role, and peer-review is a critical aspect of the scientific process but new times (internet, citizien science, higher levels of scientific literacy, and now AI) diminish the benefits of journals creating "barriers to entry" as you put it.

I for one hope not to live in a world where academic journals fall out of favor and are replaced by vibe-coded papers by citizen scientists with inflated egos from one too many “you’re absolutely right!” Claude responses.

Me neither, but what you present is a false dichotomy. Science used to be a past time of the wealthy elites, it became a profession. By opening up it up progrss was accelerated. Same will happen when publication will be made more open and accessible.

And then, Einstein was a « citizen scientist », wasn't he ?

Is it at all possible to have a policy that bans the submission of any AI written text, or text that was written with the assistance of AI tools? I understand that this would, by necessity, be under an "honor system" but maybe it could help weed out papers not worth the time?

this is probably a net negative as there are many very good scientists with not very strong English skills.

the early years of LLMs (when they were good enough to correct grammar but not enough to generate entire slop papers) were an equalizer. we may end up here but it would be unfortunate.

But then, assuming we are fine with this state of things with LLMs :

why would it be upon them to submit in English, when instead reviewers and readers can themselves use a LLM translator to read the paper ?

this would be a good development. seems very far off.

How is it far off if it's already used like this, just on the side of submitters ?

> these kinds of tools cause many more problems than they actually solve

For whom? For OpenAI these tools are definitely the solutions. They are developing by throwing various AI-powered stuff at the wall to see what sticks. These tools also demonstrate to the investors that innovation did not stall and to show that AI usage is growing.

Same with Microsoft: none of the AI stuff they are shoving down the users' throats were actually designed for the users. All this stuff is only for the token usage to grow for the shareholders to see.

Similar with Google although no one can deny real innovation happening there.

Why not filter out papers from people without credentials? And also publicly call them out and register them somewhere, so that their submission rights can be revoked by other journals and conferences after "vibe writing".

These acts just must have consequences so people stop doing them. You can use AI if you are doing it well but if you are wasting everyones time you should just be excluded from the discourse altogether.

What do credentials have to do with good science? There are already some roadblocks to publish science in important–sounding journals, but it's important for the neutrality of the scientific process that in principle anyone can do it.

Fair but if spam becomes an issue that blocks good research from happening maybe adding some filters improves the end result more.

I'm certain your journal will be using LLMs in reviewing incoming articles, if they aren't already. I also don't think this is in response to the flood of LLM generated articles. Even if authors were the same as pre-LLM, journals would succumb to the temptation, at least at the big 5 publishers, which already have a contentious relationship with the referees.

This dynamic would create even more gate-keeping using credentials, which is already a problem with academia.

wouldn't AI actually be good for filtering given it's going to be a lot better at knowing what has been published? Also seems possible that it could actually work out papers that have ideas that are novel, or at least come up with some kind of likely score.

Completely agree. Look at the independent research that gets submitted under "Show HN" nowadays:

https://hn.algolia.com/?dateRange=pastYear&page=0&prefix=tru...

The real problem is that researchers are pushed to publish as their publication is the only way their career can advance. It's not even to "boost" your CV, as a researcher your publication history IS your CV.

It was already a problem 25 years ago when I did my Ph.D., and I don't think things changed that much since then.

This encourages researchers to publish barely valuable results, or to cut one articles into multiple ones with small variations to increase their number of publications. Also publishers creating more conferences and more journals to respond to the need that researchers have to publish.

I remember many experienced professors telling me cynically about this, about all the techniques they had to blow up one small finding into many articles.

Anyway - research slop started way before AI. It's probably going to make the problem worse, but the root issue have been there for a long time.

I am very sympathetic to your point of view, but let me offer another perspective. First off, you can already vibe-write slop papers with AI, even in LaTeX format--tools like Prism are not needed for that. On the other hand, it can really help researchers improve the quality of their papers. I'm someone who collaborates with many students and postdocs. My time is limited and I spend a lot of it on LaTeX drudgery that can and should be automated away, so I'm excited for Prism to save time on writing, proofreading, making TikZ diagrams, grabbing references, etc.

This is what I see, you need more of an active, accomplished helper at the keyboard.

If I can't have that, the next best thing is a helper while I'm at the keyboard my damn self.

>Why LaTeX is the bottleneck: scientists spend hours aligning diagrams, formatting equations, and managing references—time that should go to actual science, not typesetting

This is supposed to be only a temporary situation until people recover from the cutbacks of the 1970's, and a more comprehensive number of scientists once again have their own secretary.

Looks like the engineers at Crixet were tired of waiting.

What the heck is the point of a reference you never read?

By "grabbing references" I meant queries of the type "add paper [bla] to the bibliography" -- that seems useful to me!

Focusing in on "grabbing references", it's as easy as drag-and-drop if you use Zotero. It can copy/paste references in BibTeX format. You can even customize it through the BetterBibTeX extension.

If you're not a Zotero user, I can't recommend it enough.

I have a terrible memory for details, I'll admit an LLM I can just tell "Find that paper by X's group on Method That Does This And That" and finds me the paper is enticing. I say this because I abandoned Zotero once the list of refs became large enough that I could never find anything quickly.

AI generating references seems like a hop away from absolute unverifiable trash.

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