I was confused too. Whoever wrote the title(s) doesn’t know a second language, otherwise, they would have realized how confusing that was, even to native English speakers.
If AI is so good at research then maybe instead of people researching comparatively easy problems which only get you a publication, they will research problems that matter. (I'm not dismissing here problems just because they are "easy" or "not practical", I am sure that there are a ton of worthwile ones among those, I am dismissing just those which are artificial and only researched out of publication incentives.)
AI is only good at a subset of problems. However in that subset it is often very good. There is a lot of "low hanging fruit" in that subset and we can learn a lot quickly by having AI work in that area.
We do of course need to watch for hallucinations, but I in general expect AI to be very good at theoretical physics. AI can take a lot of known equations and prove/propose (these are different things!) generalizations to that may or may not match reality. AI can suggest experiments to see if the predictions match the real world. Maybe string theory can finely make a non-trivial prediction that we can test in the real world...
However AI is terrible for other parts of physics (at least so far) and those are also important we shouldn't lose track of them despite the excitement that we can get from progress in things AI is good at.
I will change my mind when that happens. LLMs have progressed greatly this year, I've already changed my mind several times to account for how much better they are.
Reminds me of this - https://statmodeling.stat.columbia.edu/2026/08/27/258/ - which I think was on HN at some point in the last week. The fact that (according to another comment here) these papers were in areas outside his specialism, and therefore things he doesn't have the domain knowledge to critique, makes me think we're going to see more of this special kind of madness that seems to have some kind of grandiosity at its heart. It really does feel like some people are falling hard for plausibility rather than rigour.
Edit: the papers do seem to have other names on them, and he says that all papers "were reviewed by humans for accuracy", but I still think there's a very serious risk of people being gulled into okaying something that's plausible rather than rigorously checking everything. There's enough human-generated slop in science already!
A very high number of Redditors think they know more/better than a Harvard professor and dismiss him out of hand. May someone grant us all a measure of their confidence.
As a former hep-th guy from a very reputable institution I was ready to at least skim and briefly evaluate 36 particle physics papers. Turns out most of them have nothing to do with physics. It takes a special kind of personality to have the hubris to publish in so many domains at once, and most Harvard/Princeton/etc. physicists I know would probably be critical of this. Not that the disapproval would mean much.
(I did learn much of my QFT from Schwartz’s QFT textbook though, so I hold him in higher regard than <insert random Harvard Physics prof here>.)
Seems like you're doing the same thing, dismissing the commenters out of hand (even though many of them make good or decent points - the quantity, the absurd number of unrelated fields, the weird meaningless LLMisms) while using the professor's title as a battering ram to destroy the notion that he could ever be wrong, even on things outside of his field of expertise.
LLMs making otherwise extremely intelligent people veer off in a strange direction isn't new. When reassured by LLMs, there have been many cases where someone starts posting papers on a plethora of fields they know nothing about, making unified theories that try to tie several fields together, or get convinced they made a real breakthrough when they haven't. I don't think there's been a single counterexample so far where someone used LLMs to push out so many papers on so many topics and have them be correct. So until someone who knows something in these fields pitches in, I'm remaining skeptical by default.
I know it's a HN rule that one must take people who don't RTFA seriously and not point it out, fortunately that doesn't extend to Reddit users.
I read the Anthropic post yesterday. I read the Reddit comments today. I observed that many of them are being overzealously anti AI and likely didn't even click the link they were commenting on, let alone look beyond it.
It's good to be skeptical. It's bad to jump to conclusions based on an emotional response to AI in general. It's bad to be a Redditor.
Should they pretend that the papers are great and the guy is infallible just because of his position? No, but his prior achievements are probably a signal that they should not jump straight into the usual circle jerk.
Good judgement takes time, but if the time to judge exceeds the time to spam -- and it does, by many orders of magnitude -- then we should expect the approach "judge each work by its merits" to result in being overwhelmed by spam, which we already are. Reputations let you amortize the cost of acceptance and reduce the cost of rejection, giving you a fighting chance against spam that extreme open-mindedness does not.
"50 year old tenured teacher that slaps his name on literally everything his students put out believes he's uniquely qualified to criticize hegelian dialectics" is a headline you can read approximately every year in academia. He's out of his zone, with absolutely zero ability to review his "research" nor credibility to push it out.
May someone grant me an ounce of his hubris and I'll become president of the united states. And I'm not even a citizen.
Wisdom of the crowd has finally proven to be more valuable than some obscure title in academia. Crowds are relentless and ruthless, kind of a red team instead of a cronyism.
PS: The infamous replication crisis deserves a honorable mention here.
The professor co-wrote 36 papers (40+page each) with Claude on things ranging from clinical trials, to shared authorship between Shakespeare and the Bible, to decyphering the Voynich Manuscript.
I didn't read the papers, and I am no specialist in any of the fields that guy meddles in, but it really does sound too good to be true. And if not, well, we're entering a new era of humanity right now.
It's extremely likely that a very high number of Redditors do actually know more/better than a Harvard Particle Physics professor on the topics on which he published papers, which include ecology, health, economy, geology, etc.
> In these other areas, I could not rely on my own expertise to know whether what Claude found was interesting. So I found some experts and asked. With their guidance, BootLoops was able to make substantive advances in many research areas.
Seems like he realized that as well. I suspect that you may be suffering under the same burden as the Redditors.
> Last December, I tried using Claude as a research assistant, and found that Claude Opus 4.5 performed like a strong graduate student at 20 times the speed. Despite Claude producing a high-quality paper at the end of the experiment, it was a slog to get there. I had to correct every sentence it wrote, steer it away from irrelevant threads, and pull it back from dead ends.
Doesn't sound like someone that's producing "slop". But we probably have different definitions of the word, because I've noticed a lot of people use to mean anything AI was involved with at all.
Surely, this is not correct. The monkeys are drastically less likely to produce valid words; the set of monkeys that produce valid words are much less likely to produce valid sentences, etc. etc. Very low bar to clear, but of course this is vastly better than infinite monkeys at producing good research.
It relies entirely on infinity. Even a much smaller probability like flipping heads on a coin n times in a row quickly becomes practically impossible. How many monkeys would you need flipping coins to get 30 heads in a row?
Now imagine that’s hitting the right letter on a keyboard many thousands of times in a row.
I once read the criticism that science is already moving too fast and that wrong results get published and accepted as true because nobody reaps any laurels for verifying or reproducing results, even if that's an indispensable part of the scientific method.
Now in the age of AI this problem will be heavily accentuated. Who's going to review the quality and validity of dozens and dozens of papers being generated by a researcher who used to publish sporadically?
It's not hard to see science going through a "slop crisis" in the next few years, in the same way that now projects are getting inundated with PRs.
How about this: instead of going through publishers, researchers are required (by their funding organizations) to put a bounty on falsifying the result (for, say, 10 years). The higher the bounty, the higher the 'impact factor'. Put your money where your mouth is. A bounty of at least the cost of the study should be expected for serious research.
I can imagine a 'falsification insure' industry. They'd look at reputation as well as employ people close to the subject matter to asses risk, taking over the role of peer review. But with aligned incentives.
This way researchers and institutions who don't care too much about truth are marginalized; they either have low impact factors or they'll need to pay huge falsification insure fees.
And researchers who are sceptical of certain results will have an incentive (money!) to try to replicate it.
Remaining question: who is the authority on whether a publication has been falsified?
> nobody reaps any laurels for verifying or reproducing results
That's not entirely true. There's a whole genre of "response" and "response to response" papers that voice concerns when there's a methodological issue. That's somewhat common in my fields (computational biology and biomedical research).
And even if a "response" isn't published, people still try to reproduce published techniques if those seem useful.
A lot of useful studies take weeks, months, or years to do the first time, and require a staff, a grant, etc. at least in medicine, physics, chemistry...
You'd be surprised how rarely (overall) reproduction / verification is attempted. Sadly, a lot of research is taken at face value. That's done quite often with AI/tech stuff these days, and even more so for scientific research. It's a hard problem.
I agree, will be interesting to see the evolution.
I know notable sources from the US academia that points to the problem since way before the AI, even if the they are from human science and economics, don't know much about the STEM status. Accordingly, the first slop crisis got a big hit with the initial woke wave in the mid 2000', then another hit during COVID, in which thousands of pointless papers about the effect where produced and an immense amount of research effort was towards it, and now AI.
Science will keep progressing, even at a faster piece, the real problem will be to justify the work and output of thousand of people and researcher that currently need a salary. It may be that the academia will adjust to split between professionals focused on the pure teaching skill, while the research and publication aspect of academia will go to have much higher barriers and restricted to less people. Relevant publication will not become a standard requirement for a successful academic career but also teaching skills.
Which is interesting because the solution should be obvious: use AI to reproduce results and test what's published, instead of using bogus AI to publish more bogus results.
Is this actually good? Seems like they created a new tool that allows LLM to do a lot of numerical analysis. The papers seem somewhat legit?
https://bootloops.ai/papers.html
I suppose if this framework allows physics and other sciences to rapidly progress (e.g. publish more findings), that is good? It destroys a certain model we had, that science should be arduous and require a genius many years of work to uncover something, but if these papers truly add knowledge via this new tool, then this is for the good.
Not sure implications for researchers, but from the public perspective, now we have 36 new papers that increase our knowledge. But maybe an expert can weigh in, the papers might be slop?
I think we can only say we are forwarding human knowledge if a human understands the content they’re putting out in the world. I don’t know if that applies in this case, it I think it’s an important bar to clear.
Yes but people doing research will be asking LLMs to review many papers prior to their reading most likely. If more humans come to understand the findings of these papers, then its good right?
I guess on a theoretical level, if you had a button which would create breakthroughs in a field, but you yourself couldn't understand it, would you push it? I would because otherwise the field may not discover it, and others will be able to understand the breakthrough.
As long as engineers that are AI understand it when they put it to use that shouldn't matter for the practical aspects. Most people already don't understand physics or engineering. It's just keeping the models aligned that's the big issue.
I see the point, but also if these papers provide proof that human can eventually use, or another AI system can use to make further progress, then is it for the best? A great deal of academic papers are drivel couched in dense language and prose to obfuscate the lack of substance. So far, humans rely on search engines and heuristics to avoid wasting time and energy on them. Now, humans are already using LLMs to assist in reading and understanding papers, so perhaps it still contributes to knowledge.
I believe the bar for publication should be "can this be understood by (expert) humans". If that's not the case, it may even be true, but who cares? It's not improving knowledge, which is by definition human understanding.
on desktop, it's going to be usable for a while longer if you can access old.reddit.com. they've finally set their sights on killing it off though.
on mobile, it's cancer like the rest of the internet, unapologetically growth-hacked with shit like "warning!!1 mature content!!1 log in with official reddit app to keep our community safe" modal you get regardless of which benign subreddit you try to browse.
Did he drop them because they were hallucinated, or did he millennial-drop them? I still can't tell.
“Drops” as in “Releases”, not “Retracts”.
I was confused too. Whoever wrote the title(s) doesn’t know a second language, otherwise, they would have realized how confusing that was, even to native English speakers.
Maybe he should be "sanctioned".
If AI is so good at research then maybe instead of people researching comparatively easy problems which only get you a publication, they will research problems that matter. (I'm not dismissing here problems just because they are "easy" or "not practical", I am sure that there are a ton of worthwile ones among those, I am dismissing just those which are artificial and only researched out of publication incentives.)
AI is only good at a subset of problems. However in that subset it is often very good. There is a lot of "low hanging fruit" in that subset and we can learn a lot quickly by having AI work in that area.
We do of course need to watch for hallucinations, but I in general expect AI to be very good at theoretical physics. AI can take a lot of known equations and prove/propose (these are different things!) generalizations to that may or may not match reality. AI can suggest experiments to see if the predictions match the real world. Maybe string theory can finely make a non-trivial prediction that we can test in the real world...
However AI is terrible for other parts of physics (at least so far) and those are also important we shouldn't lose track of them despite the excitement that we can get from progress in things AI is good at.
> AI is only good at a subset of problems.
I wouldn't count on that to last in any area.
I will change my mind when that happens. LLMs have progressed greatly this year, I've already changed my mind several times to account for how much better they are.
Can't you just change your mind in advance for all the future progress?
Interesting submission history.
Not one sided at all
Reminds me of this - https://statmodeling.stat.columbia.edu/2026/08/27/258/ - which I think was on HN at some point in the last week. The fact that (according to another comment here) these papers were in areas outside his specialism, and therefore things he doesn't have the domain knowledge to critique, makes me think we're going to see more of this special kind of madness that seems to have some kind of grandiosity at its heart. It really does feel like some people are falling hard for plausibility rather than rigour.
Edit: the papers do seem to have other names on them, and he says that all papers "were reviewed by humans for accuracy", but I still think there's a very serious risk of people being gulled into okaying something that's plausible rather than rigorously checking everything. There's enough human-generated slop in science already!
A very high number of Redditors think they know more/better than a Harvard professor and dismiss him out of hand. May someone grant us all a measure of their confidence.
As a former hep-th guy from a very reputable institution I was ready to at least skim and briefly evaluate 36 particle physics papers. Turns out most of them have nothing to do with physics. It takes a special kind of personality to have the hubris to publish in so many domains at once, and most Harvard/Princeton/etc. physicists I know would probably be critical of this. Not that the disapproval would mean much.
(I did learn much of my QFT from Schwartz’s QFT textbook though, so I hold him in higher regard than <insert random Harvard Physics prof here>.)
The blog post should have been posted instead I think - https://www.anthropic.com/research/claude-shaped-science.
It's worth a read, and very interesting. All of the papers he posted have relevant experts of said fields on them as I understand it.
At the very least, many of the papers in other domains are co-authored with experts in that domain.
Seems like you're doing the same thing, dismissing the commenters out of hand (even though many of them make good or decent points - the quantity, the absurd number of unrelated fields, the weird meaningless LLMisms) while using the professor's title as a battering ram to destroy the notion that he could ever be wrong, even on things outside of his field of expertise.
LLMs making otherwise extremely intelligent people veer off in a strange direction isn't new. When reassured by LLMs, there have been many cases where someone starts posting papers on a plethora of fields they know nothing about, making unified theories that try to tie several fields together, or get convinced they made a real breakthrough when they haven't. I don't think there's been a single counterexample so far where someone used LLMs to push out so many papers on so many topics and have them be correct. So until someone who knows something in these fields pitches in, I'm remaining skeptical by default.
I know it's a HN rule that one must take people who don't RTFA seriously and not point it out, fortunately that doesn't extend to Reddit users.
I read the Anthropic post yesterday. I read the Reddit comments today. I observed that many of them are being overzealously anti AI and likely didn't even click the link they were commenting on, let alone look beyond it.
It's good to be skeptical. It's bad to jump to conclusions based on an emotional response to AI in general. It's bad to be a Redditor.
Should they pretend that the papers are great and the guy is infallible just because of his position? No, but his prior achievements are probably a signal that they should not jump straight into the usual circle jerk.
Isn't HN similar, albeit with even more confidence?!
Yeah but I’ve earned it by learning JavaScript
Appeal to authority is a logical fallacy. Each paper will need to be judged on its own merits, irrespective of the source.
Reputations are the answer to spam.
Good judgement takes time, but if the time to judge exceeds the time to spam -- and it does, by many orders of magnitude -- then we should expect the approach "judge each work by its merits" to result in being overwhelmed by spam, which we already are. Reputations let you amortize the cost of acceptance and reduce the cost of rejection, giving you a fighting chance against spam that extreme open-mindedness does not.
From my own experience, I'm not sure that is necessarily the case. Retraction Watch is also an interesting read.
I suspect there will be citations galore shortly, faster than anyone can think.
"50 year old tenured teacher that slaps his name on literally everything his students put out believes he's uniquely qualified to criticize hegelian dialectics" is a headline you can read approximately every year in academia. He's out of his zone, with absolutely zero ability to review his "research" nor credibility to push it out.
May someone grant me an ounce of his hubris and I'll become president of the united states. And I'm not even a citizen.
Remedial Math - I love it.
Wisdom of the crowd has finally proven to be more valuable than some obscure title in academia. Crowds are relentless and ruthless, kind of a red team instead of a cronyism.
PS: The infamous replication crisis deserves a honorable mention here.
Brand names don’t mean much anymore.
Three words: Cambridge, Jason Arday.
As Cambridge alumni, that one hurt when it happened. You're not wrong.
Stanford, Marc Tessier-Lavigne.
At Harvard itself, Claudine Gay.
It is a really good exercise to go through the individual things she was accused of one by one. There is one case that is maybe questionable.
The professor co-wrote 36 papers (40+page each) with Claude on things ranging from clinical trials, to shared authorship between Shakespeare and the Bible, to decyphering the Voynich Manuscript.
I didn't read the papers, and I am no specialist in any of the fields that guy meddles in, but it really does sound too good to be true. And if not, well, we're entering a new era of humanity right now.
Too good to be true, or an accurate reflection of the unfortunate state of the world right now?
what do you mean?
Well, many of us in Hackernews lived through grad school. We’ve seen the games. May our AI lords enshittify things even more.
> A very high number of Redditors think they know more/better than a Harvard professor and dismiss him out of hand.
How many different fields would you imagine a Harvard professor to be an expert in?
It's extremely likely that a very high number of Redditors do actually know more/better than a Harvard Particle Physics professor on the topics on which he published papers, which include ecology, health, economy, geology, etc.
This is literal slop.
> In these other areas, I could not rely on my own expertise to know whether what Claude found was interesting. So I found some experts and asked. With their guidance, BootLoops was able to make substantive advances in many research areas.
Seems like he realized that as well. I suspect that you may be suffering under the same burden as the Redditors.
> Last December, I tried using Claude as a research assistant, and found that Claude Opus 4.5 performed like a strong graduate student at 20 times the speed. Despite Claude producing a high-quality paper at the end of the experiment, it was a slog to get there. I had to correct every sentence it wrote, steer it away from irrelevant threads, and pull it back from dead ends.
Doesn't sound like someone that's producing "slop". But we probably have different definitions of the word, because I've noticed a lot of people use to mean anything AI was involved with at all.
Is this the same as the infinite monkeys with infinite typewriters eventually writing Shakespeare theory?
No, because the monkeys do end up writing the work correctly. There's absolutely no guarantee that any of those papers are correct in any way.
It literally is just spam, and nothing else.
It's like the Nobel disease: https://en.wikipedia.org/wiki/Nobel_disease
?
Surely, this is not correct. The monkeys are drastically less likely to produce valid words; the set of monkeys that produce valid words are much less likely to produce valid sentences, etc. etc. Very low bar to clear, but of course this is vastly better than infinite monkeys at producing good research.
It relies entirely on infinity. Even a much smaller probability like flipping heads on a coin n times in a row quickly becomes practically impossible. How many monkeys would you need flipping coins to get 30 heads in a row?
Now imagine that’s hitting the right letter on a keyboard many thousands of times in a row.
I recommend the story A Short Stay in Hell
Infinite monkeys will eventually produce shakespeares work. One monkey can with infinite time.
Thanks. Amazing examples in that weblink.
Schwartz explains what he did in an Anthropic post: https://news.ycombinator.com/item?id=49933386
I once read the criticism that science is already moving too fast and that wrong results get published and accepted as true because nobody reaps any laurels for verifying or reproducing results, even if that's an indispensable part of the scientific method.
Now in the age of AI this problem will be heavily accentuated. Who's going to review the quality and validity of dozens and dozens of papers being generated by a researcher who used to publish sporadically?
It's not hard to see science going through a "slop crisis" in the next few years, in the same way that now projects are getting inundated with PRs.
Yeah, science needs to reinvent itself.
How about this: instead of going through publishers, researchers are required (by their funding organizations) to put a bounty on falsifying the result (for, say, 10 years). The higher the bounty, the higher the 'impact factor'. Put your money where your mouth is. A bounty of at least the cost of the study should be expected for serious research.
I can imagine a 'falsification insure' industry. They'd look at reputation as well as employ people close to the subject matter to asses risk, taking over the role of peer review. But with aligned incentives.
This way researchers and institutions who don't care too much about truth are marginalized; they either have low impact factors or they'll need to pay huge falsification insure fees.
And researchers who are sceptical of certain results will have an incentive (money!) to try to replicate it.
Remaining question: who is the authority on whether a publication has been falsified?
> nobody reaps any laurels for verifying or reproducing results
That's not entirely true. There's a whole genre of "response" and "response to response" papers that voice concerns when there's a methodological issue. That's somewhat common in my fields (computational biology and biomedical research).
And even if a "response" isn't published, people still try to reproduce published techniques if those seem useful.
A lot of useful studies take weeks, months, or years to do the first time, and require a staff, a grant, etc. at least in medicine, physics, chemistry...
You'd be surprised how rarely (overall) reproduction / verification is attempted. Sadly, a lot of research is taken at face value. That's done quite often with AI/tech stuff these days, and even more so for scientific research. It's a hard problem.
> You'd be surprised how rarely (overall) reproduction / verification is attempted.
I didn't say it's common, I said that stating it's "never" reproduced is false.
I agree, will be interesting to see the evolution. I know notable sources from the US academia that points to the problem since way before the AI, even if the they are from human science and economics, don't know much about the STEM status. Accordingly, the first slop crisis got a big hit with the initial woke wave in the mid 2000', then another hit during COVID, in which thousands of pointless papers about the effect where produced and an immense amount of research effort was towards it, and now AI. Science will keep progressing, even at a faster piece, the real problem will be to justify the work and output of thousand of people and researcher that currently need a salary. It may be that the academia will adjust to split between professionals focused on the pure teaching skill, while the research and publication aspect of academia will go to have much higher barriers and restricted to less people. Relevant publication will not become a standard requirement for a successful academic career but also teaching skills.
Which is interesting because the solution should be obvious: use AI to reproduce results and test what's published, instead of using bogus AI to publish more bogus results.
Well, he sure met his papper publishing quota for the year.
Is this actually good? Seems like they created a new tool that allows LLM to do a lot of numerical analysis. The papers seem somewhat legit? https://bootloops.ai/papers.html
I suppose if this framework allows physics and other sciences to rapidly progress (e.g. publish more findings), that is good? It destroys a certain model we had, that science should be arduous and require a genius many years of work to uncover something, but if these papers truly add knowledge via this new tool, then this is for the good.
Not sure implications for researchers, but from the public perspective, now we have 36 new papers that increase our knowledge. But maybe an expert can weigh in, the papers might be slop?
I think we can only say we are forwarding human knowledge if a human understands the content they’re putting out in the world. I don’t know if that applies in this case, it I think it’s an important bar to clear.
Yes but people doing research will be asking LLMs to review many papers prior to their reading most likely. If more humans come to understand the findings of these papers, then its good right?
I guess on a theoretical level, if you had a button which would create breakthroughs in a field, but you yourself couldn't understand it, would you push it? I would because otherwise the field may not discover it, and others will be able to understand the breakthrough.
As long as engineers that are AI understand it when they put it to use that shouldn't matter for the practical aspects. Most people already don't understand physics or engineering. It's just keeping the models aligned that's the big issue.
who is going to parse and distill these AI generated papers? who is going review them even? Science without humans isn't science
Scientific progress only requires the scientific method, not humans being in-the-loop.
I see the point, but also if these papers provide proof that human can eventually use, or another AI system can use to make further progress, then is it for the best? A great deal of academic papers are drivel couched in dense language and prose to obfuscate the lack of substance. So far, humans rely on search engines and heuristics to avoid wasting time and energy on them. Now, humans are already using LLMs to assist in reading and understanding papers, so perhaps it still contributes to knowledge.
I believe the bar for publication should be "can this be understood by (expert) humans". If that's not the case, it may even be true, but who cares? It's not improving knowledge, which is by definition human understanding.
More discussion on the related Anthropic post: https://news.ycombinator.com/item?id=49933386
Anus Mirabilis
If the guy knows his shit, Claude acts as a cadre of cheap, fast, undergrads.
"drops" is unclear here - it could mean either "publishes" or "retracts"
Can you just not use Reddit at all anymore without the app?
on desktop, it's going to be usable for a while longer if you can access old.reddit.com. they've finally set their sights on killing it off though.
on mobile, it's cancer like the rest of the internet, unapologetically growth-hacked with shit like "warning!!1 mature content!!1 log in with official reddit app to keep our community safe" modal you get regardless of which benign subreddit you try to browse.
You can use old reddit on mobile, use Firefox and ublock origin to hide the sidebar or videos get clipped wrong.
I generally can on Safari on iOS. Sometimes I have to force desktop mode. They're making it very difficult...
We need xcancel but for reddit.
Something like https://github.com/redlib-org/redlib?
They just closed all public APIs, including RSS. Time to leave the ship soon.
You can use a web browser.