GPT-5.6 used a prompt to close a 30-year gap in convex optimization

https://old.reddit.com/r/math/comments/1uxj3cy/after_openais_cdc_proof_announcement_gpt56_used_a/

by mbustamanter • 4 days ago

600 378 4 days ago

old.reddit.com

baal80spam• 4 days ago

Waiting for comments saying that LLMs can't produce anything new and general goalpost moving.

qsera4• 4 days ago

From the post lol

So I wouldn't really say that this result is using or creating some fundamentally new techniques in convex geometry or optimization theory. What this means from my perspective is that if a result is attainable with existing techniques, modern AI methods will be able to solve those problems. I don't think researchers in math/TCS will be made obsolete, but I think it will instead no longer make sense to work on any low-hanging, or even medium-hanging (you know what I mean) fruit. We'll be needed for problems where actual novel approaches are needed.

monster_truck• 4 days ago

so it seems like The New Big Question In Math is

How's It Hanging, Brother?

WA• 4 days ago

If knowledge is a Swiss cheese, LLMs can help fill the holes, but not make the cheese bigger.

throw3108223• 4 days ago

The author explains he's an expert in the domain and that he had worked sporadically on the problem for about a year, also with the help of previous LLMs. So whatever he means by "I wouldn't really say that this result is using or creating some fundamentally new techniques" it doesn't mean that the result was trivial. Also, says it might not make sense to work on low or even medium hanging fruits in the future- and I bet that's by far the largest share of work for most mathematicians.

Sure, it's not a breakthrough that opens new roads in mathematics- is this where the goalpost has moved now?

tripleee5• 4 days ago

this is a fairly bleak outlook even when you're trying to make it sound the opposite. Only the cream of the crop talent will have value going on?

Most of us aren't Terence Tao

greenhat76• 4 days ago

Oh brother I can tell you didn't read the entire article.

qarl27• 4 days ago

HEH. Don't know why you're getting downvoted. It's painfully obvious that there is a vicious AI backlash now, where every amazing advancement is met with denial and loathing.

Oh wait, sorry, I do know why you're getting downvoted. Fear.

piloto_ciego15• 4 days ago

A lot of people who thought they were special and “better” than mere blue-collar workers are realizing that in fact;l they are the same just working with a different medium.

jdw645• 4 days ago

What I'm feeling is that there's a need to study how to use AI well. I've seen professors using AI, and it was amazing. In that sense, I think AI prompt input will become stratified. In the past, implementation skills were very important, but these days, concepts feel more important this is one of those things.

It's not that AI brings equality, but rather that the output varies depending on how much background knowledge you have. You could call it a stratification of input

I'm starting to feel like there's no place left for programmers like me who focus on quickly churning out MVPs.

slifin• 4 days ago

I think there's a lot of interesting things to the side of development that don't get the resources they deserve

Debuggers, testing techniques, testing layers

Essentially things that could be used to ground your ai back to reality and work good for humans too

semiquaver4• 4 days ago

You’re at least 18 months out of date claiming that prompting will be the new hot skill. Turns out LLMs are also good at prompting other LLMs.

aprilthird20212• 4 days ago

And yet in this case a human prompted the LLM for this result, not another LLM

brookst• 4 days ago

Ah, but who prompts the prompters?

cromka3• 4 days ago

That doesn't make any sense; you can't have one LLM to read your mind to prompt another LLM.

jdw645• 4 days ago

Rather than prompt engineering, I think it should be called overall harness engineering. Anyway, that's how I feel these days

throwup238• 4 days ago

Calling it prompt engineer is doing it a disservice. With agents we’re well into process engineering, which is a ton more interesting.

The obvious baby’s first process is “plan -> execute” but as we learn about the strengths and weaknesses of LLMs you have to start unpacking that process into planning, prototyping, testing, validation, reviews, and tons of research. If you treat it like an extension of your brain that can automate some thought processes, it becomes a lot more powerful.

jdw645• 4 days ago

I find it strange that people sometimes think of knowledge as 'public property for everyone.' The essence may be one, but the mental model of knowledge is individual. For an LLM's knowledge to become mine, I need to digest it to some extent.

And programming, as the programmer who created Eliza once said, is the act of becoming a legislator of your own universe. So even if there are black boxes, if you want to build a program that fits your own worldview, studying is essential.

jltsiren• 4 days ago

One of the key skills of a professor is asking the right questions. Figuring out something worth working on, and then framing it in an appropriate way and asking questions that allow someone with specific tools and skills to make progress in the topic. Usually the tools and skills are those available to a new student, but working with an LLM is similar.

That skill comes with experience. Most people don't have it immediately after PhD.

aprilthird20212• 4 days ago

I'm starting to feel like there's no place left for programmers like me who focus on quickly churning out MVPs.

Of course there is. The same way this was only possible as a result from the professor who prompted it with his specialized 10 page prompt and most importantly his deep knowledge of the problem space, the muscle memory and intuition you've built over the years is what will allow you to get more out of any AI than some guy who says "make a door dash clone" as the entire prompt

jdw645• 4 days ago

So these days I've been writing down my thoughts on my personal homepage. Things I've learned, my background knowledge, and so on.

I've been realizing that there are more books tied to my background knowledge than I expected, but I'm not sure what will happen as AI advances further.

These days, I'm living for the fun of building my own personal wiki on my homepage

neonbjb• 4 days ago

I actually think people who are great at understanding problems, coming up with requirements and designing solutions (all things I would expect someone who is good at churning out MVPs would be good at) are exactly the people most empowered by the current batch of LLMs. Its the people who are only good at working on small chunks of problems that I'm concerned about..

applfanboysbgon2• 4 days ago

Two points:

- Hasn't been peer reviewed yet, so take with a grain of salt. This applies to all claimed proofs, not just AI-generated ones. Even humans hallucinate proofs too!

- The prompt is on page 27 here[1]. It is ten pages of advanced mathematics priming the model in the right direction, apparently informed by a year of prior research. That doesn't invalidate the result if it is genuine, but it is worth noting that this wasn't a matter of "ChatGPT, solve this unsolved problem. Make no mistakes." and required substantial domain expertise and human research beforehand.

[1]https://arxiv.org/pdf/2607.13335

throwthrowuknow• 4 days ago

Saying “solve this problem” doesn’t get good results most of the time with humans either, it’s entirely underspecified so the person assigned that problem may solve it in a variety of unacceptable ways or not at all or perhaps worse solve the wrong problem because you weren’t clear about its definition. This actually happens all the time. What matters is the ability to communicate clearly and with precision as well as the “harness” which for humans is procedure, training, planning and management.

applfanboysbgon2• 4 days ago

Saying “solve this problem” doesn’t get good results most of the time with humans either

Sure. That is not even remotely the point I was getting at. Already we see the thread filling up with comments about how human skills are irrelevant, using a mathematics PhD applying his expert skills in a way that the people who are saying that could never have done to justify their inane conclusion.

camdenreslink• 4 days ago

The subtext of this whole post (or at least a subtext that some might read), is "we don't need mathematicians/programmers anymore" or "we will need much fewer mathematicians/programmers". So the fact that this result required a year of prior research and a 10 page prompt of specialized knowledge goes against that subtext. You still needed the human just as much to get to the result, and the LLM ended up being a tool to find the last bit.

lozenge• 4 days ago

It is lean-verified, so it can be trusted unless the Lean statement of the hypothesis is not an accurate description of the hypothesis.

andy12_2• 4 days ago

but it is worth noting that this wasn't a matter of "ChatGPT, solve this unsolved problem. Make no mistakes."

It wasn't the case for this, but when OpenAI disproved the Unit Distance Conjecture, it was really done autonomously by an automated AI pipeline with a completely AI-generated prompt. No human expertise required at all in the process (well, except for the final human verification).

YeGoblynQueenne4• 4 days ago

Without human verification, an LLM can generate correct or incorrect proofs but it can't tell the difference. A human is necessary to be able to tell one from the other.

Saying that's a solution "done autonomously by an automated AI pipeline" is like saying that a self driving car that can only take you to the nearest train station after which you have to ride the rain to where you're going is "autonomously" driving you to your destination. Which is exaggerating the autonomy of the system, rather.

varenc• 4 days ago

Digression: You can link directly to a page in a pdf with a url like this: https://arxiv.org/pdf/2607.13335#page=27

mw67• 4 days ago

Crazy how intelligence is cheap, efficient and commonplace now. We humans better refocusing our energy on our core values/principles, given most of our skills are becoming irrelevant

esafak3• 4 days ago

Once we figure out the pesky problem of how we're going to pay for housing, food, and healthcare.

timcobb2• 4 days ago

I can't stop wondering myself.... I'm writing some software with AI and wondering, why am I doing this? Will anyone need this? Will anyone have money to buy this?

Best I've come up with is we'll need to be adopted by technofeudlaist overlords to be our patrons like in the roman days

z3t4• 4 days ago

When machines are doing all the work - we no longer have to.

duskdozer• 4 days ago

I think the big names behind the AI companies already have that problem solved. A lot of people probably won't like the solution very much though.

weregiraffe3• 4 days ago

Mathematics is a human-designed game that involves rearranging symbols.

MinimalAction2• 4 days ago

That view is incredibly reductionist. It really is an efficient encoding of how nature behaves. It might be a human construct, but given how best it allows to understand nature (through principles of physics), it is uncanny to be any different from the language of nature.

Reminds me of Wigner's Unreasonable effectiveness of mathematics in natural sciences [0].

[0] https://en.wikipedia.org/wiki/The_Unreasonable_Effectiveness...

JustFinishedBSG2• 4 days ago

At a very high level mathematics is basically 100% text/symbolic rewriting. You start from some set of postulate assumed true and you do your thing to get a new different set of equivalent assertions in a form that is more useful.

I don’t know if LLMs will kill the working-mathematicians but at least seem like that it doesn’t seem absurd to imagine LLMs will be good at math…

fidotron7• 4 days ago

It's still clear that LLMs lack spatial reasoning, either in the concrete or abstract, and while that sort of reasoning has been downplayed by academia for at least a century it is fundamental to technology and industry. (And many would say for science and mathematics too).

They will, however, get there as well either directly or as interfaces to models that do, and your core point stands.

simianwords• 4 days ago

Is there any proof that they are not good at special reasoning? Arc agi 1 and 2 are saturated.

ACCount372• 4 days ago

"Lack" isn't the right word. "Lacking" is more like it.

If there was a deep fundamental inability, we wouldn't see things like newer generations of LLMs consistently improving on ARC-AGI series (heavy spatial reasoning loading) and SimpleBench (a lot of commonsense + spatial reasoning components).

In a way, it's a surprise that LLMs, notoriously lacking any sort of embodied experience, can even get this close to human baselines on tasks like this.

My takeaway is that text is a far richer modality than anyone has expected - and that high end LLMs are often sharp and flexible enough to recognize their weak points and substitute their strengths. I.e. all the LLMs implementing A* to optimally solve pathfinding in ARC-AGI-3 tasks, often unprompted.

There might still be unrealized gains there from true depth-unbounded recurrence, or maybe from finding better ways to integrate modalities in training. But clearly, a "fundamental limit" it ain't.

skeke• 4 days ago

Oh brother

AI hasn’t even taken the class of jobs associated with customer service lmao

fidotron7• 4 days ago

Do we employ mathematicians in customer service roles?

12345hn6789• 4 days ago

Uh.... Have you ever called customer service lately?

codingdave• 4 days ago

If it were commonplace, there wouldn't be a post and discussion about it. Cheap? Arguable - while it didn't cost thousands, it wasn't free. Cheap is in the eye of the beholder. Efficient...How do we even measure that? The massive infrastructure and training to take a product to the point where someone could do this is massive. Ignoring everything behind the scenes and acting like one session and result is the whole picture of efficiency doesn't seem right. And no, nothing produced by AI makes skills irrelevant. That is the whole ongoing argument of whether people are losing cognitive ability by moving their thinking to AI.

Overall, this is an impressive proof of capability. But I wouldn't take that proof as anything more than what it is.

Izmaki2• 4 days ago

Seconded on the "not cheap" argument here. I've spent $25 worth of tokens completing a one-week task in an afternoon, or rather my company spent the money. I would never have personally felt OK with throwing this much money after some prompting back and forth for a few hours, one lazy Saturday afternoon. I ran the risk of not finding the solution before the token usage would be too high for me to want to carry on, if I was my own credit card linked to the account.

Of course money in this situation is a bit of a funny measurement, right, because if I was able to take the rest of the week off as soon as I had solved the one-week problem, then I would have no problem at all throwing even $100 worth of tokens at it, so I could enjoy a nice 4-day "mini-vacation".

How cheap "cheap" is, is indeed "in the eye of the beholder".

yieldcrv2• 4 days ago

I mostly agree, these are things that couldn't or weren't solved with abundant capital at all, and now they are being solved

it went from not having a price, to having one, and we are trying to retroactively transpose economic viability or economic existence to it from some parallel and prior time.

rgoulter• 4 days ago

'Maybe cheap but not free' is a good observation.

If I'm getting paid for the work, I'm happy to leverage the LLMs so I can do more. If I'm paying for the work, I expect more from it.

For hobbyist stuff, where I'm not expecting to receive money? LLMs let me do things I otherwise wouldn't have done.

I've agreed with thoughts like "the LLM wrote the code, surely it's not worth sharing" or "I could just have the LLM write my own version of that". I'd also wondered about my own personal projects, surely "an LLM could have written all of this". -- But how I feel about that changes a bit based on how much it'd take the LLMs to get the same output.

lvl155• 4 days ago

Intelligence was always relatively cheap. You can pick up a phone and get answers for free in most academic settings.

amelius2• 4 days ago

(within limits)

ben_w5• 4 days ago

You've not seen how they react to noobs asking physics questions, I think.

Even when you've got an interesting idea, if you're an enthusiastic amateur who don't yet know enough to phrase the question right but does actually know the basics, they'll put you in the same category as the people who think healing crystals can power hyperspace telepathy with Anubis: "oh no not another one".

LLMs have infinite patience, but unfortunately come (came?) with too much sycophancy, giving even more people far too much confidence.

amelius2• 4 days ago

Everybody can be an armchair mathematician now. Just fling some thoughts in the direction of your AI setup and let it do breadth first search with AI based pruning heuristics.

jethkl• 4 days ago

I generally agree, though direction, intuition, and domain knowledge are still relevant. Your breadth-first-search framing feels right, but you still need a sense of which paths are worth following, and you need to know when to trust the results.

I’ve been doing more math as a hobby in the past few weeks — working on lesser-known conjectures and exploring proofs of hard theorems — than I could have managed over the previous several years. It’s an exciting time.

Jweb_Guru3• 4 days ago

If you really believe this, try to use GPT 5.6 to prove an open problem you know nothing about. You might get lucky, but if you don't, you will soon discover that 5.6 can make "progress" towards a theorem without actually getting anywhere pretty much indefinitely.

slopinthebag2• 4 days ago

I imagine there are hundreds of thousands of people doing this, yet all we seem to get is a handful of solved problems. So it would seem the success rate is extremely low.

witx• 4 days ago

yeah...right. Go touch some grass

William_BB• 4 days ago

Ever heard of the infinite monkey theorem?

This is basically what LLMs do on really hard tasks. Prompt it a million times on a really hard problem and it might output the correct answer once.

ben_w5• 4 days ago

The infinite monkey theorem assumes random distribution of symbols*.

Given the tokenizers have a vocabulary in the 10k-100k range, "a million attempts" will generally still only get the first token of the answer correct.

Even really rubbish models, e.g. talkie, the "what if we only use pre-1930s data to train a model?"** model, had to be almost all the way to the right answer to reach the really low HumanEval pass@100 score of ~0.04 (I'm only eyeballing the relevant chart).

* Actual monkeys not being like this is, while amusing, irrelevant

** https://talkie-lm.com/introducing-talkie

artninja1988• 4 days ago

Ever heard of the infinite monkey theorem?

Even if every atom in the universe were a supercomputer generating a trillion trillion random characters every second since the Big Bang, the chance of producing Hamlet would still be essentially zero.

Levitz• 4 days ago

Intelligence on its own is not very useful though. We put it on a pedestal because it creates huge potential when paired with other things, wisdom, discipline, empathy, but on its own?

ratg13• 4 days ago

AI / LLMs are not intelligence .. they are just a prediction engine that has been branded as 'intelligence' by marketing employees

At the end of the day it is still making a best guess at what the user wants based on data it has seen before.

It still requires someone smarter than the output to be able to evaluate if the result is any good, or just hand waving.

skekke4• 4 days ago

It’s no diff to an educated human.

Stored potential.

But will that potential be converted that contributes to the economy..? That requires other traits.

Might be focus, might be discipline, might be the need to get revenge lmao.

This is what the llm-boosters miss. Progress is willed into existence.

petra3• 4 days ago

Llm's do have discipline and persistence, though.

bwestergard• 4 days ago

A counter argument: A strong distinction between "intelligence" (understanding what is) and "values/principles" (understanding what ought to be) was characteristic of much early modern European philosophy from Descartes to Kant, which received its influential strong formulation from David Hume.

But trying to maintain this distinction leads to insuperable difficulties. Our conceptual framework for understanding the world are always value-laden. There is no "view from nowhere", no historically unconditioned set of values or concepts. Your framing, in which "values" are external to "intelligence" and must be imposed on it (on pain of intelligence being "value-neutral"), leads inevitably to the dead end of "AI Alignment", "superintelligence", etc. Which is a kind of pseudo-theology.

"We humans better [be] refocusing our energy on our core values/principles, given most of our skills are becoming irrelevant."

In light of the untenability of a strong fact/value or intelligence/ethics distinction, I would suggest this alternative advice: humans should focus on critical appropriation and extension of the received wisdom, whether that comes to us directly from human beings or indirectly through an LLM. Perhaps this is compatible with the spirit of your original suggestion.

slashdave4• 4 days ago

Iteratively leaning on lean to prove a conjecture is not intelligence, it is automation

tripleee5• 4 days ago

how much can I bill for having good core values?

rakel_rakel2• 4 days ago

I don't think researchers in math/TCS will be made obsolete, but I think it will instead no longer make sense to work on any low-hanging, or even medium-hanging (you know what I mean) fruit. We'll be needed for problems where actual novel approaches are needed.

I wonder how this compares to what we see happening with "juniors" in software development? In math research, do you also get the training for the profession from working on the low hanging fruits for a while, to then move to the medium-hanging, and later go on to work on previously unsolved stuff?

skybrian2• 4 days ago

This apparently required a 10-page prompt. It seems like someone needs to know enough to write it?

jvanderbot• 4 days ago

Yeah, back to the gold-in-gold out use of LLMs.

ch4s3• 4 days ago

Certainly. This feels similar, to me, to how building complex software with LLMs works today in practice. You need to know a lot to set up goals and guardrails and verify outputs. For me, making the bits change was always the fun part, not tangling with text in my editor, though that had its moments.

dwohnitmok3• 4 days ago

The author also used GPT-5.6 to write the prompt. This did involve giving GPT-5.6 access to his previous work and a back and forth process (so definitely still used the author's expertise to some degree), but the prompt itself is also largely AI generated.

JustFinishedBSG2• 4 days ago

My experience may not be entirely representative because to be entirely honest I’m not exactly a great researcher and there are brilliant PhD students. That said it indeed was my experience that in the pre-PhD / early PhD period ( or even longer … ) your advisor proposes (gives) you pretty low hanging stuff that he mostly already knows how to solve, at least at a high level, with the expectation that it will teach you to use the mathematical tools you need.

Quothling5• 4 days ago

Around here AI isn't really more of a threat to juniors than it is to seniors. It's a threat to the people who have been taught "recipies" rather than applied computer science. You can have excellent seniors who can do TDD, DRY, SOLID and so on, who also happen to have no idea what a L1 cache miss is. The current AI models know all of those things, but they struggle applying them correctly without someone piloting them. Even in the energy industry where I work, where you'd think it would be obvious from the context that you should prioritize runtime safety over debug safety, the current AI models struggle to do so. As far as seniority goes, though. If we can find a young developer with little experience who actually knows computer science, we're much more likely to hire them... Since they are cheaper.

This isn't something which is unique to software development though. We're currently building enterprise AI apps that we can deploy into the AI agents working for anyone of our employees. The key thing we're currently seeing is that the people in a team who are the ones that everyone turn to for advice, are the only people who aren't in "danger". Even people who are great at their jobs are being outperformed by AI in many cases.

I think it'll be a massive challenge for our society in the coming years. Maybe we're even going to get to the point where the AI will also be capable of replacing a lot of the "domain experts". Right now that seems far out, but then, if you had asked me about AI four months ago I would've told you it was all hype.

marcosdumay• 4 days ago

So... The AIs with no model of the world are replacing software developers that have no model of the world?

rakel_rakel2• 4 days ago

Interesting, thanks. I don't know where "around here" is, but the signals I've seen in a lot of articles is that the demand for junior software people has taken a dive since a year or two back, with student programs etc getting cancelled. One googler said they were getting a junior to their team and that was kind of a big deal because it hadn't happened in that whole department for a long time.

In relation to that, I guess my question becomes: if the same thing will happen in math research, who will write the ten page math proof prompts in the future?

p-e-w2• 4 days ago

Unless you’re claiming that AIs will suddenly (and very soon) stop improving, they are obviously a threat to everyone’s job.

Calling notable conjectures that have been open for decades “low-hanging fruit” is an act of desperation. Most professional mathematicians couldn’t have proved those conjectures if their lives depended on it.

zarzavat• 4 days ago

AI is a threat to everyone. People who claim that AI will never be able to do X have consistently been proven wrong.

The only people who are safe are those whose jobs depend in some way on their humanity. e.g. yoga teachers, bouncers, etc

siva7• 4 days ago

It's funny how you applied on your own argument several logical fallacies about why ai is only a threat to people who have been taught "recipies" versus who know what L1 cache miss is.

Actually it's sad there are people out there dumb enough to believe knowing L1 cache is any different than knowing recipies when it comes to the story which jobs AI will take. I'm convinced by now it will be the jobs of those people believing such crap.

vatsachak7• 4 days ago

Math is way more automatable than programming.

In math, a proof is a proof. We don't know if we can get there and so getting there is the hard part.

In software, we always know that we can solve the problem. So HOW to solve the problem is the hard part. Because the type of solution involves maintainability, which involves planning, LLMs suck at it. This leads to "LLM slop code" whereby the LLM creates ad-hoc convoluted logic with redundancies and no reuse of existing standard library batteries.

Unless you're a Grothendieck who gets mad at Deligne for not solving the Weil's conjecture "THE RIGHT WAY", software is fundamentally different than math in this respect.

So I'll say it again, AI will win a fields medal for before managing a McDonald's simply because there are enough big problems within arms reach than their current capacity to plan over time

sashank_15095• 4 days ago

So I'll say it again, AI will win a fields medal for before managing a McDonald's simply because there are enough big problems within arms reach than their current capacity to plan over time

AI can manage a McDonald’s already. If manage means directing humans to do something to ensure the store is running. If manage means running robots, then yes maybe that is 5 years away but just directing humans to run a store, that is possible right now.

fsmv2• 4 days ago

I think the difference is in math the problem is fully specified and easily verifiable and in programming it's not. I don't agree that we always know we can solve the problem.

nicf4• 4 days ago

I've spent some time working both as a math researcher and as a software engineer, and I think this comment actually underrates the similarity between the two fields as they're actually practiced.

Some math research does involve grabbing a single, fully specified conjecture off the shelf and hunting for a proof of it, and it's true that if you manage to solve a long-standing open problem, other mathematicians will be interested no matter how you did it.

But this isn't all of what they do, probably not even most of what they do. Like in software engineering, it's not always obvious which question would be the most useful one to ask. A lot of mathematical work also goes into what we call "theory-building", where you could say that primary work goes into coming up with definitions rather than theorems. Mathematicians also care a great deal about how something is proved; a lot of them are some of the most aesthetically picky people I've ever met. Words like "ugly", "beautiful", "creative", and "boring" are used to describe both definitions and proofs all the time.

From the outside, it can look like all they're doing is pumping out proofs at any cost. But I promise you that when I talk to mathematicians who don't have any experience building software, they have a similarly narrow view of that field as well! Both fields, from the inside, look a lot more human than you might expect.

nicf4• 4 days ago

I was trained as a mathematician and worked as a math researcher for a little while (now working as a private tutor), and based on my experience I'd say this description is basically right, with one extra wrinkle.

In order to get a Ph.D., you have to do some sort of original research, so in that sense you're working on "previously unsolved stuff" basically right from the start. But that doesn't entail doing anything all that ground-breaking; most Ph.D. dissertations (very much including mine!) contain work that a more senior researcher in the same subfield could probably have produced without too much difficulty. The software development analogy is a pretty good one: a lot of the point of getting junior researchers to do research is to help train them to one day become senior researchers, and often the work itself is nothing all that special.

Given the trajectory of these LLM proofs, this seems like it's going to have to change pretty soon, and to be honest I'm pretty grateful that I'm not in charge of deciding what that's going to look like, because I don't have any good ideas! I'm actually pretty worried about the future of the field.

darkstarsys• 4 days ago

Indeed. Perhaps my article here will be of interest to some: https://blog.oberbrunner.com/blog/ai-math-as-humanities/

pfdietz8• 4 days ago

In order to get a Ph.D., you have to do some sort of original research,

China has now introduced "practical PhDs" where you have to build some practical machine instead.

phillip_kerger5• 4 days ago

I would agree with your take. I (author of the post & paper) learned a ton from working on small parts of problems my PhD advisor was doing a lot of the heavy lifting on, and later also from getting some results that were essentially putting together the right pieces that already existed followed by some deep-in-the-weeds analysis.

oulipo• 4 days ago

Except solving problem is probably the least (even though it's important) interesting thing in research...

The most interesting thing in research is finding new questions, that we understand and that we know why they are important. And that's something that humans need to do (by definition)

dash2• 4 days ago

I keep hearing this but lots of maths problems are practically important! We want to know the answer because it will be useful for applied science, or statistics, or engineering. It’s not all just about knowledge for its own sake.

a_imho• 4 days ago

If I recall correctly there was a proposed proof to the abc conjecture by Mochizuki https://en.wikipedia.org/wiki/Abc_conjecture#Claimed_proofs which was rejected due to being rather inpenetrable to humans. Shouldn't this be an ideal target for LLMs?

anorwell• 4 days ago

It was rejected for being wrong (or most charitably, incomplete).

lg5689• 4 days ago

There was recently an announcement that a group trying to formalize it found a gap exactly where other mathematicians were pointing. So to the extent there was any doubt, it should be gone now--the proof was incorrect.

But I agree LLMs have a lot of potential for checking proofs--both informally (they can read quickly and find gaps) and formally (by attempting to formalize).

7373737373• 4 days ago

Similarly, I'd love to see LLMs create a formal proof of the https://en.wikipedia.org/wiki/Classification_of_finite_simpl...

charlieyu12• 4 days ago

I’d like to see four color conjecture and an elementary proof of FLT.

_alternator_5• 4 days ago

I know a bit about this field. This conjecture reads as somewhat more niche than the cyclic double cover conjecture recently proved by OpenAI, but nevertheless represents a real contribution.

You want to know how long it takes to solve an optimization problem, in this case over convex, lipschitz functions. (The restriction to a spherical domain is not really a restriction, you can just change variables for any bounded domain.) Anyway, showing upper bounds on time complexity is "easy" because it's just the runtime of your algorithm. Showing (nontrivial) lower bounds is usually much harder because it requires constraining all algorithms.

This proof apparently shows that the lower bound time complexity is equal to the time complexity of an existing 30-year old algorithm: it requires Omega(d^2) function evaluations to solve over this class of functions.

My gut says likely implies that d is the minimal number of evaluations if you have a gradient oracle because you can approximate a gradient with d function evaluations, but I'm not sure how hard it is to make that rigorous.

LPisGood3• 4 days ago

It should be noted that optimization of a convex bounded lipschitz function is exactly what most modern statistical learning (AI) models are based on.

hodgehog114• 4 days ago

Very confused by this comment. The older (poorer) parts of the ML literature focus on models with convex and (gradient-)Lipschitz objectives, but that's not representative of reality, not even close. Modern objectives for AI models are famously nonconvex (catastrophically, from the point of view of classical optimisation theory), and that's where the interesting research is.

theteapot• 4 days ago

What do you mean by this? A neural network hypothesis space is not typically strictly convex or a lipschitz function.

xeromal2• 4 days ago

Sometimes I read a comment on HN that is so advanced that it's just as readable to me as Greek. Love reading it just to see someone work though!

alexpotato3• 4 days ago

so advanced that it's just as readable to me as Greek

I used to feel this way about statistics.

The language and terms are hard to understand and many of the formulas are taught as "just memorize this" instead of building up from first principles.

But then I started using statistics to analyze something I cared a lot about (paintball) and I quickly realized it's like learning anything new:

- there is jargon

- and core concepts

- when you learn the above, it suddenly makes a lot more sense.

semiquaver4• 4 days ago

Thanks for posting this comment, it makes me proud of myself to be able to partially comprehend the comment :)

spicyusername• 4 days ago

Not to diminish the comment, but most things are not as complex as they sound when phrased in everyday language or sound much more complex than they are when phrased in technical language.

Technical language is a tool that allows insiders to say less and refer to more, and to be specific, but it's just a tool. Most things can be described in accessible ways.

I think you'd be surprised at what you could understand and at just how few domains are truly complex enough that a layman couldn't understand with a little bit of patience and an accessible summary.

Vasbarlog• 4 days ago

Έχεις απόλυτο δίκιο!

georgeburdell• 4 days ago

I don’t think OP made much effort to make the comment accessible to non-experts, and so it should be taken as a gauge of the fundamental difficulty of the topic.

BobbyTables2• 4 days ago

It took me a while to understand a lot of these math concepts.

Turns out people doing Engineering research are using a very small but powerful bag of tricks from a handful of few famous Mathematicians. The concepts are named after them!

phillip_kerger5• 4 days ago

Yes, order d is the minimal number of evaluations of gradients needed for the same problem! That has actually been known since 1979 (Nemirovsky and Yudin showed that), and there are methods with the same complexity so this question in the gradient model has been solved for a long time. "because you can approximate a gradient with d function evaluations" was exactly why d^2 made sense as a lower bound for this case! Basically, the lower bound question can also be thought about as "can you do better than approxing a gradient?", so this result says no.

fooker2• 4 days ago

Any implications for discrete/integer optimization?

spwa42• 4 days ago

The problem is that we're going to have another deepseek moment when someone uses GLM or Kimi K3 to do this.

paytonjjones2• 4 days ago

What was the first DeepSeek moment? (genuine question, I'm out of the loop on what you mean)

theragra• 4 days ago

I guess when cheap Chinese model was very close to SoTA models

bananaflag• 4 days ago

January 2025, release of DeepSeek R1, the first open reasoning model. There was a lot of panic then that it was done with very few resources.

threethirtytwo4• 4 days ago

Genuine question: If you still or did think LLMs are just stochastic parrots that just summarize everything and have no form of creativity, what do you think after seeing results like this?

I'm very curious how people reconcile their fear/hatred of AI with actual objective reality. This is actually what interests me most about the whole AI thing. How we tell ourselves what we tell ourselves.

barnacs2• 4 days ago

I hold my stance that LLMs are stochastic parrots.

Making the parrots ever more complex and training on ever more data produced by intelligent, creative beings may make them more useful or convincing but does at no point give rise to intelligence or creativity.

beering• 4 days ago

With such high standards, most HN commenters also do not have intelligence nor creativity. I don’t think we can set the bar that high.

tctcd62• 4 days ago

Comical human arrogance...

qnleigh2• 4 days ago

I won't touch creativity, but if this and other results like it do not demonstrate intelligence, what does? How was it able to solve problems that specialist mathematicians have tried and failed to solve for years?

in-silico• 3 days ago

What would evidence of "intelligence" or "creativity" look like for you?

pessimizer2• 4 days ago

I'm very curious why people conflate thinking LLMs are stochastic parrots with "fear/hatred" of AI. It seems like you're arguing with people who agree that it works and it helps, but you're trying to insist that this implies that they should kneel down and pray to it.

Is "stochastic parrot" too disrespectful for you? Do you think it is a slur?

edit: and this is a genuine question, also. How do you do stochastic parrot = "just summarize everything" = "no form of creativity" = "fear/hatred" so quickly?

Are summaries not creative? Are Maxwell's equations not summaries? Do people hate and fear parrots?

threethirtytwo4• 4 days ago

I think it's quite clear the proof here shows that it is not a parrot. It objectively isn't.... that's the only rational conclusion. Yet many people claim that it is, so the main conclusion is fear/hatred is causing people to rationalize their logic to fit the narrative they prefer.

qnleigh2• 4 days ago

I have absolutely no problem with people disliking or fearing AI. It's energy consumption, effects on education and potential for displacing good jobs are all quite disturbing. But "stochastic parrot" means that "all it does is randomly repeat things that it has seen before without understanding them." It's infuriating to see this written about an instance of an AI solving an open math probably. Do you think the models are just randomly repeating facts until they accidentally emit a proof? If so, then how do they synthesize that knowledge into something logically coherent?

Alternatively, if you think that even Maxwell was a stochastic parrot, then presumably almost every human who has ever lived was also a stochastic parrot except a few rare examples like Einstein. Not sure what definition you are using but it seems too broad to be useful.

throwaway17073• 4 days ago

I had to create an account to respond to this because I am quite convinced these math problems they are "solving" are pure marketing. Why is it only GPT doing this, why not Claude? Why does Terrance Tao do marketing for OpenAI? I suspect OpenAI has hired math researchers to solve obscure problems and put them in their training set, purely for marketing reasons.

There was a good comment on the Pelican bicycle svg yesterday about how these models aren't getting much better beyond what the companies focus training them on. I think that's what's happening in this case too, they probably put this in the training set.

threethirtytwo4• 4 days ago

Terrence Tao getting paid by openAI is, to you, the most probable conclusion... much more so then the LLM actually being able to come up with math proofs?

Jweb_Guru3• 4 days ago

Why is it only GPT doing this, why not Claude?

Because Claude can't do it. Anyone who tells you that Fable is better than GPT 5.6 at pure math is lying to you.

raincole2• 4 days ago

It's such a weird train of thoughts lol. You're using the fact that

- Claude isn't doing that

as evidence to support the assumption that

- it's a marketing trick

Which is obviously non sequitur, as if it were a marketing trick, Anthropic could do it too. Anthropic isn't known for not spending on marketing.

Honestly, nowadays I question human's reasoning ability more than I question AI's.

vanuatu2• 4 days ago

kind of a hilarious conspiracy theory

You are correct that LLMs are trained on existing proofs but hiring researchers to solve unsolved problems is just unrealistic, both in terms of how none of the mathematicians simply came out and took credit for their own discovery or exposed this, and how training sets are not easily memorized (rather, the meta techniques are learned).

OpenAI just has better training methods and techniques for pure math over Anthropic, it’s one of their biggest strengths

frozenseven• 4 days ago

Terence Tao also uses Anthropic's models in his work. Oh, you didn't know that? Well, now you can pivot to saying that he's getting paid off by both companies. This is actually one of the hallmarks of both conspiratorial nonsense and military-grade cope. Any fact, regardless of how mundane or extraordinary, gets re-imagined as evidence of the same mad-hatter conclusion.

I hope people are screenshotting this stuff. This really needs to be documented. It's remarkable how wild it's getting.

phillip_kerger5• 4 days ago

I (author of the paper) can't speak for others, but I have no affiliation whatsoever with OpenAI and have not received anything from them (I even pay for my subscription lol). I've thrown this problem at each model with new model releases, and 5.6 was the first one to solve it, so that's my side of that story.

slashdave4• 4 days ago

Must be nice knowing you have a clear understanding of "objective reality" that others don't.

threethirtytwo4• 4 days ago

Is it not objective reality that the feat performed by the LLM here is much more then parroting or summarizing something?

It's doing math proofs. At this point, it's fully clear that objective reality is that the LLM is not parroting anything here.

qarl27• 4 days ago

Heh. I see you're being met with screeching and downvotes.

Not much to do about it, I guess, but continue to call it out.

WarmWash6• 4 days ago

No matter what there will always be people who refuse to believe AI is anything beyond a string of if statements.

slopinthebag2• 4 days ago

They aren’t really stochastic parrots, they’re next token prediction machines. They aren’t intelligent nor creative imo. However they are quite useful at some tasks. Why would you assume people who don’t kneel at the alter of AI are somehow fearful or hate AI? Most of the hate I’ve seen have been for the people and companies involved with AI not the technology itself.

perching_aix3• 3 days ago

Those next tokens sure are unlikely despite this.

Like, this is just so silly. Come with me for a little thought experiment.

Picture in front of you a Kibana dashboard. It displays, say, latencies.

Now apply some statistic functions. Let's find the time ranges where we had outlier tail latencies, for example.

You pin down some patterns, cool. But this is post-incident. We want to alert on-incident.

So you begin writing some rules. And then some more rules. And then even more rules. All of a sudden you captured the entire logic of the program emitting these metrics, along with the surrounding dependencies'.

See where I'm going with this? To do sufficiently well at prediction, you'll need to model the entire constitution of the thing you're trying to predict, along with the stuff going through it. Locally, all predictions will be unlikely. But globally, you'll be right.

But if you do that, you quite literally "understand" and simulate the entire thing. That's the whole point, and this is why "just predicting the next token", "stochastic parrot" and other anti-AI dogwhistles are so flagrantly asinine. They imply some sort of rudimentary Markov process, or at best some sort of dozen or so variable statistics research paper type prediction. It's a laughable proposition, given the quite literally trillions of parameters actually in use, and all the research that has already went into identifying countless semantically interpretable latent spaces and activation patterns.

Most of the hate I’ve seen have been for the people and companies involved with AI not the technology itself.

Anecdotes are fun! Visit any Reddit thread where AI is brought up and watch that ratio shift very rapidly. The hate and cope train is incessant there.

d4rkp4ttern• 4 days ago

In the Reddit post there was clarification that this was done with Sol Pro not Ultra - curious what is everyone’s mental model of the difference.

My understanding is that ChatGPT Pro is effectively a multi agent system, or somehow uses multiple LLMs in parallel and selects a best answer. And Ultra is more similar to Claude-Code UltraCode where the main agent can choose to create a dynamic JS workflow that deterministically orchestrates multiple agents to handle different parts of a task and have adversarial checkers etc.

Is that more or less the difference? Any substantiating sources would be great to see.

gizmodo59• 4 days ago

ultra in codex is just a way to run multi agent system, pro is similar to other pro models like 5.5

sashank_15095• 4 days ago

This is all a depressing and bleak future that I don’t look forward to.

One solution is to ban LLM’s, to artificially create a demand for human thought, that just feels like living in an artificially constructed zoo.

Another solution is humans don’t do anything that AI can do better , / doesn’t need the human touch. So I suppose we will all become artists, sportsmen or politicians, the only jobs that will remain except for select few. Maybe this is ok, I don’t know.

Another solution is we find a way to mind-meld with AI so that human + Ai >> AI alone. This is dystopian, who gets to decide who mind melds with AI, how much will it cost etc etc.

For the stupid copes that the prompt required human ingenuity, let me first add that the author used GPT5.6 to write most of the prompt. He just gave some mild direction. That amount of direction does not require deep expertise and the expertise required will keep falling with time, eventually an undergrad can create this loop and then maybe a high school student.

  And prompt engineering / loop engineering nonsense is not real. Calling it engineering is a psy-op because it is something simple, imprecise and future models will be much better at it than you.

In fact, in the future the most likely outcome is you tell the agent what you want (I want this app, or I want this theorem solved) and it will set up the loop, or loop of loops and use all its computing effort to come up with a result. This is completely dystopian to a human life.

slashdave4• 4 days ago

There is more to human life than programming and math proofs.

tripleee5• 4 days ago

unfortunately many won't get to experience it much because we'll be stressed and struggling to make a living

piloto_ciego15• 4 days ago

This is utopian friend!

Literally anything you wanted to make is no plausible to make if not now then in the next couple years.

The thing you’re worried about is capitalism and the connection with working to having the right to keep living. If you can throw off that mental shackle you can start to see how this can be amazing, but you have to drop the idea that everyone has to work at a job for someone else to provide some service in order to do it. It’s hard, I know, but change your mindset some and dream for a better world and we can make it.

WarmWash6• 4 days ago

You ever play a video game with god mode cheats enabled, so you can unlock all the unlocks, get all the best gear, and be an unstoppable force with unlimited money?

Yeah, it's fun for 30 minutes.

paytonjjones2• 4 days ago

People like being needed and important to other people. That isn't some artifact of capitalism; it exists across all times and cultures.

dudul6• 4 days ago

If anything one wants to make is now possible to make, how is anyone supposed to make a living?

Can I make food with LLMs? Can I build a house and make clothes? This is stupid. No real wealth is being created for the general population here.

raincole2• 4 days ago

It genuinely scares me that some people's first reaction to this news is banning LLM.

derac• 4 days ago

Maybe we should also burn books and lobotomize ourselves to make science more fun and challenging. The ego here sickens me. I don't care about your ego. I want accelerated material science, medical science, energy, better outcomes for people across the world.

Shorel2• 4 days ago

Why? No one will be able to effectively ban AI. Pandora's box is open, and we can only witness the consequences.

malthaus• 4 days ago

wasn't that always the reaction historically whenever people felt like their livelihood was threatened by new technology?

arjie• 4 days ago

A rational reaction would be to identify the political coalition that would act in this manner and place yourself in opposition to it if you do not see yourself being able to change it.

I wonder which one that is.

natsucks2• 4 days ago

call me naive, but i really think the pursuit for the new, the better, the interesting, is boundless and humans will never stop. and so for those of us that dream and push and are plain curious will never run out of ways to be useful. and hopefully the fruits of all this tech will be ample enough to support the ones that don't want to or can't participate in that way.

petra3• 4 days ago

We already drown in useful new things and especially knowledge. There's a limit to how fast we can adopt them.

thfuran• 4 days ago

If AI is better at being (or simulating; I think the distinction is irrelevant in this context) curious, better at examining that curiosity, better at pursuing it to discovery, better at applying the discoveries, better at sharing the discoveries, and able to push the frontier of knowledge (if that’s even the right word) literally beyond human comprehension in every domain, what usefulness exactly is left? Or are you just implicitly saying you don’t believe AI will ever get all that superhuman?

skekke4• 4 days ago

What a load of absolute nonsense.

I’m convinced many of you barely go outside and have the capacity for original thoughts.

Sloppity slop slop.

adamtaylor_13• 4 days ago

Why is this depressing and bleak? No one is stopping you from doing math if that's what you want to do.

Should I ban table saws because they make people too good at woodworking? Should we ban right-angles?

This line of thought doesn't lead anywhere fruitful.

moomoo113• 4 days ago

just ignore these people

gig economy is a blessing to keep the LCD busy and doing something productive instead of being nihilistic losers

it’s better to focus on how to keep these “knowledge” workers busy for the AI age

they don’t like to work and complain even on a w2 job (where someone else had to figure out how to capitalize them) so in many ways they’re far more dangerous and hopeless.

pfdietz8• 4 days ago

This is all a depressing and bleak future that I don’t look forward to.

Or, for the vast majority of humanity who aren't professional mathematicians, it's a liberating and empowering advancement.

I'm sorry if the automated digital theorem looms are eliminating the market for your handwoven proofs.

My_Name5• 3 days ago

Indeed. Where do you throw your wooden shoes these days? (Not that it was an effective tactic then anyway)

sdwvit• 4 days ago

Not yet peer reviewed

ck2• 4 days ago

could machine-learning even handle a TEN PAGE PROMPT just a year ago?

this is changing my mind, at least about experts using advanced tools like any profession where it's like the magic of watching a lifetime of hard-earned skill at work

After seeing OpenAI’s CDC result, I wrote a much more elaborate prompt following the same general methodology. My prompt is about ten pages long and attached at the end of the preprint (see collection of links below). There is a lot baked into this prompt, on approaches to try and also on how exactly the model should proceed, but it's built exactly in the style of OpenAI's CDC prompt. One note is that I gave it a relatively small error requirement, to prove the quadratic lower bound under order d⁻⁴ accuracy.
After 148 minutes, GPT-5.6 Sol Pro returned a proposed proof resolving the quadratic dimension dependence at accuracy of order d⁻³. After checking things myself, I formally verified the proof in Lean, and it passed the formal verification check.

nilamo2• 4 days ago

Is this interesting? AI does what we made it to do, news at 8?

ChrisArchitect• 4 days ago

Non-reddit: https://medium.com/@kerger.p/an-ai-assisted-breakthrough-in-... (https://news.ycombinator.com/item?id=48939768)

Kirillekko• 4 days ago

The amount of people several months ago stating that no one cares about the "unsolved" mathematical problems that AI is able to solve is funny.

Noe2097• 4 days ago

Can't wait for GPT to prove that P=NP (or not)!

stfnon• 4 days ago

nah, scientist with the name Shakey Onail found this and all creds are given to LLMs is crazy

VonTum• 4 days ago

Do you have a link to Shakey's publication?

charlieyu12• 4 days ago

I tried using AI to solve some advanced math problems. One thing I see is that they can throw an enormous amount of brute force into a problem. When mathematical logic can be brute forced we will see some interesting advances.

macwhisperer• 4 days ago

Basically, he proved that *information is power.* If you don't know which way to go (the subgradient), you're gonna be calculating forever!

YeGoblynQueenne4• 4 days ago

So if you dig down a bit it turns out the author had been trying to solve that problem for a year with GPT 5.4 and 5.5 and he fed all that information to the prompt he gave to Sol Pro which may or may not had direct access to the author's chat history. So the claimed "148 minutes" was really "a year plus 148 minutes".

Moreover, it seems the prompt included the technique used to solve the problem:

https://old.reddit.com/r/math/comments/1uxj3cy/after_openais...

In the prompt I basically just throw all reasonable approaches at it, without making a big distinction for what to explore most, and these approaches would all be reasonable for someone who knows the area. Sol helped me with the prompt as well, for which I gave it the CDC prompt, some ideas and specifications, a crystal clear problem description, and then modified things slightly myself after. One thing I do wonder is how much it accessed memory of previous chats, since as mentioned I had worked with 5.5 and 5.4 on this previously, and the main construction is not so different from something I discussed there. But, the function class max of affine functions that worked in the end was also in my prompt, so I'm not totally sure.

So it's not clear to me the degree to which "GPT-5.6 used a prompt" to close the gap etc, or the author basically did all the work himself and assigned it to GPT-5.6 out of enthusiasm.

dwohnitmok3• 4 days ago

Moreover, it seems the prompt included the technique used to solve the problem:

I don't believe this is true. The author sent techniques he used, but I don't believe any of those were ultimately what GPT-5.6 used.

GPT-5.6 also provided the Lean formalization, which was not provided at all by the author.

goldylochness• 4 days ago

sounds like a feature, not a bug

it might imply using weaker models to attempt the problem first is a good supplementary prompt to a more advanced model trying to do the same

phillip_kerger5• 4 days ago

I (author of the original post and paper) can add a few things here: 1. My previous approaches with GPT 5.5 were really not very sophisticated in terms of my input. I threw the problem at it, and just kept encouraging it to go iterate through ideas without any success. 2. The approaches that are in the prompt, though they will seem cryptic to someone not in the field, are relatively natural ideas. In fact, the construction that worked was something that even 5.5 initially looked at but was just too weak to see how to make it work. From my view, I would have never gotten this result myself. Imagine you are telling a contractor to build the empire state building, and you say: "You should explore approaches that can include building materials like steel, wood, concrete, or clay, and any combinations of those. You can use arcs, columns, supportive beams, and anything else you can think of to solve load-bearing issues. Do not stop until you've completed a viable plan to construct the empire state building." And then the contractor shows you the finished empire state building using reinforced concrete and steel beams with all kinds of crazy ways of making everything stable; that's kinda how I feel.

My_Name5• 3 days ago

There is a firm opinion in some circles that an AI can never do anything but recombine already known things despite a rising number of cases where that very much appears to be an inaccurate view.

Any slim possibility that vital information was given to the AI to make the task just one of recombination, rather than coming up with anything original, is grabbed with both hands no matter how tenuous.

I can understand why, nobody likes the idea of a machine capable of doing the job of a human, and jobs requiring the mind are especially sacrosanct. But like farm workers in the early part of the C20th, not liking the incoming technology won't change the effect it will have when companies get the idea they can make more money and hire less people.

YeGoblynQueenne4• 3 days ago

Thank you for replying and for reporting your results. My schtick is AI (i.e. I publish in AI conferences and journals), not maths and I'm interested in the question of autonomy from a professional point of view: how close are we to a machine that can carry out the job of a mathematician like yourself, by itself?

If you, e.g. got an LLM (any one) to one-shot the problem with a prompt that said "solve this problem", then we're much closer to that, than if you spent a year trying with different LLMs and then finally got it to work with a lot of hand-holding and even suggesting the ultimate solution. An autonomous system can't succeed once a year, if it's going to be of any use. It should also be able to identify the right tools on its own, not rely on a human to tell it what to do.

This should also go to address some of the questions you pose on reddit, about the future of mathematics. If LLMs can already do your job fully autonomously (as I would explain the term) then ... you're out of a job. You and all other mathematicians, young or old.

I personally don't think we're there yet.

I hear what you say, btw, about never being able to get there by yourself etc. Maybe you would, maybe you wouldn't. What we know is you used a tool to get there, in fact a series of tools, and it took you many tries before you did. The fact that an earlier LLM tried and failed is interesting, but that may just mean you were capable of crafting a better prompt after your interaction with the earlier LLMs.

Eridanus2• 4 days ago

Genuinely asking... How do you get chatgpt to work for 148 mins when I can't get gemini to think for even a minute at a time?

seizethecheese• 4 days ago

ChatGPT pro will work 20-30 min in my experience pretty regularly in my experience. Never had 148min but it seems plausibly in the very right tail.

phillip_kerger5• 4 days ago

clear specifications for what counts as completing the tasks, and an explicit list of what does not count as completing the task, and clearly stating to not return until the task has been completed. I've had agents run for almost 12h!

marshray2• 4 days ago

It says I've been "blocked by network security". MSIE on Windows. Reddit must really be circling the drain.

pastakatsu• 4 days ago

it blocks vpns and tor

marshray2• 4 days ago

Not using either. Just my US-based home ISP that works for everything else.

pona-a• 4 days ago

We have a non peer reviewed proof in a niche area of mathematics claiming to have been "co-written" by an LLM. What are the comments about? Lamenting or celebrating humanity's intellectual death... Very insightful.

bjt12345• 4 days ago

I can't see this article as Reddit doesn't allow me to view it. Do we have to use Reddit though? It's a horrible website.

rurban• 2 days ago

You have to replace www. with old. Then it works. When I submitted the very same link, HN replaced the good old prefix with the bad prefix www.

GPT-5.6 used a prompt to close a 30-year gap in convex optimization

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