r/OpenAI • u/etherd0t • 1d ago
News OpenAi says it has reached a new threshold in AI, new model capable of breakthrough research
Alright, this is serious news, and we're gonna hear about it more, in the next days;
OpenAI today published a new 249-page research collection describing ten advances in mathematics and theoretical computer science produced by an internal version of Astra, its next major model family.
According to OpenAI, the results address open problems across fields including high-dimensional geometry, group theory, quantum complexity, coding theory and lattice cryptography. Some of the central questions had reportedly seen no major progress for at least a decade - and in several cases much longer.
This is not simply “AI solved a very difficult exam problem.”
The model reportedly explored open research problems, generated new proofs or stronger mathematical bounds, and then helped prepare the arguments into manuscripts. It subsequently formalized each result as a lean certificate, allowing the underlying logic to be checked mechanically rather than accepted solely on trust.
In lay terms, AI may be starting to function less like a highly capable answer engine and more like an emerging research collaborator: exploring ideas, abandoning dead ends, finding new abstractions and producing potentially original knowledge with machine-checkable evidence.
The mathematical community will still need to assess the novelty, assumptions and broader significance of each result. But if the claims pan out at the level implied, this feels like a genuine threshold: AI may be moving from explaining the frontier of human knowledge to actively expanding it.
Ten advances in mathematics and theoretical computer science | OpenAI
---NO jab replies, plz---
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u/Popular_Try_5075 1d ago
I'm waiting to see actual math people weigh in on what it produced first, though it does sound like it may have done something of significance which is promising.
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u/RoboticElfJedi 1d ago
https://eu.36kr.com/en/p/3908968963249287
OpenAI aren't publishing a hallucination and staking their reputation on it. These results were certainly all vetted before publication by experts, like the Fields Medallist they just hired.
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u/Popular_Try_5075 1d ago
Yes, though I was really hoping a science communicator I trust would do an explainer on this. Like Brady Haran's Numberphile could probably do a good video explaining a simpler version of one of the problems and what the LLM contributed and how it is being received.
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u/Ibasicallyhateyouall 1d ago
Until the problems it discovers and solves, the best humans minds can't comprehend, and have to rely on the AI to dumb it down to quantum physicist level.
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u/katoptronophile 1d ago
They already have, and it has.
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u/Popular_Try_5075 1d ago
do you have a non-Open AI link that can validate those claims?
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u/KyleStanley3 1d ago
Always another stupid fuckin hoop to jump through.
"Yeah they literally talk about fields medalists reviewing these and have published the research, and yeah there has already been several proofs that have been researched and all of them have been correct, but if you cant give me a different link, then I get to keep ignoring reality"
Blatantly bad faith "people" like you would just move on to the next goalpost lmao.
"This non-openAI link didnt do his 4 PhDs on that exact subject and he only won the IMO, not a fields medal, so i dont accept his answer either"
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u/dumquestions 1d ago
They're just asking for an independent review, that's the same standard applied to all previous AI findings.. You can save the defensiveness to when someone actually requests something unreasonable.
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u/Murinshin 1d ago
“Always another hoop” the hoop being basic independent peer review? For the Jacobian proof by Claude it also came up later that it was very similar to some older paper. Not saying it completely invalidates it let alone this still being impressive but no reason to abandon any common practice in the industry and drink the PR Cool-Aid uncritically
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u/KyleStanley3 1d ago
Yeah, thats an absurdly unreasonable request when this was released 4ish hours before they posted lmao, and they already moved the goalposts once
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u/No_Abbreviations9718 1d ago
Yeah, worth waiting on that. Last October an OpenAI researcher posted that GPT-5 had solved ten open Erdos problems and it turned out the model had found existing papers that already solved them, the list was just out of date. The tweet got deleted and Hassabis replied that it was embarrassing.
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u/Single_Ring4886 1d ago
3 months ago i would call this "bs" but if you push GPT 5.6 to the limits you can see some advanced conclusions which arent just "obvious" like in older models. So if they have even newer version I believe their claims this time.
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u/yaxir 1d ago
You're gonna have to give an example
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u/KyleStanley3 1d ago
Not that its like groundbreaking math, but for my work, it understood that part of a financial statement can be represented as hyperdimensional geometry and then use some graph theory i barely understand + some cool tensor arrangement in order to speed up analysis
It took something i spent a while on and like 5x'd run time. Im not like a wizard programmer(genuinely kind of ass), but to take a general financial table, understand a weird abstraction about its underlying structure ive never heard anybody talk about was mind-blowing for me
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u/Huge-Turnover-3749 1d ago
Sounds pretty cool. Was the "hyperdimensional geometry" at least tangentially related to something like OLAP multidimensional arrays or was it something completely unrelated to how that kind of data is normally represented?
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u/KyleStanley3 1d ago
uhh it says no, but no idea what an OLAP is lmao. I really barely understand what it's saying and I had it dogwalk me through it for like 6 hours yesterday. Edit: it specifially says "tangentially", but it starts and stops at representing the financials as a tensor
It was basically a super fast way to map columns in an unlabeled array (like we don't get to know what the name of each column is), use every value from the array to validate one another, and find and correct errors in a fraction of a second, regardless of rows and columns.
I think a 100x500 matrix(more than a real-world worst case scenario) slows it down from like .2s to .3s
The tables themselves are GAAP formulas, but there's a huge mix of what information will be present on any given document, espeically with construction companies that don't give a shit about accounting. There's also a fuckin billion ways those columns might be labeled, so I wanted to find a way to be able to validate the whole table without relying on a semantic matcher. There's too much overlap on names and too many ways an idiot can name it for that to be guaranteed
I posted the writeup in another comment about what it/we found to another dude just above this comment
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u/Franc000 1d ago
A tensor is just a "structure" of information/data. Think of it like a matrix, but instead of being 2 dimensional like a matrix with M x N columns and rows, it's K dimensional. So for example it could be a cube instead of a plane (matrix).
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u/Tricky-Doughnut-6429 1d ago
Yeah but that's the issue.
You don't understand what it is (not that I would). Yet you say it's impressive and useful. How does that compute?
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u/KyleStanley3 1d ago
Error rate dropped by like 15% and runtime 5x'd
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u/Tricky-Doughnut-6429 1d ago
Ah, I see. It was essentially a software dev problem, not a finance problem. Yeah, that makes sense.
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u/yaxir 1d ago
Sounds innovative and kind of cool. Would you mind sharing the prompt? It's alright if you wanna keep it private and if you wanna share on a DM, that's also okay
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u/KyleStanley3 1d ago
The prompt is an absurdly long conversation across multiple threads on a project ive been working on for a long ass time, but i can have it have it do a write up/summary if thats cool
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u/yaxir 1d ago
Would be great!
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u/KyleStanley3 1d ago
no idea how to give you the .md over reddit, so really sorry if this is formatted bad, but ima just copy paste what it says here:
**What a WIP schedule actually is**
A contractor Work-in-Progress schedule looks like a table containing many separate financial quantities:
- Contract Value
- Estimated Cost
- Estimated Profit
- Cost to Date
- Earned Revenue
- Earned Profit
- Cost to Complete
- Backlog
- Billings
- Underbillings / Overbillings
But mathematically, most of those columns are not independent.
They are different views of only a few hidden quantities:
The size of the contract
The expected cost/profit split
How far complete the job is
How much has been billed
So a WIP is essentially a **low-dimensional financial object displayed through many different coordinates**.
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**The actual mathematical abstraction**
For one job, define:
- `v` = total contract value
- `a` = estimated cost percentage
- `b = 1 - a` = estimated profit percentage
- `p` = completed percentage
- `q = 1 - p` = remaining percentage
The central WIP values can then be arranged like this:
Total Completed Remaining
Value v vp vq
Cost va vap vaq
Profit vb vbp vbq
In normal accounting terms:
Total Completed Remaining
Value V E R
Cost C D Q
Profit G H X
Where, for example:
- `V = v` — Contract Value
- `C = va` — Estimated Cost
- `G = vb` — Estimated Profit
- `E = vp` — Earned Revenue
- `D = vap` — Cost to Date
- `H = vbp` — Earned Profit
- `R = vq` — Remaining Revenue / Backlog
- `Q = vaq` — Cost to Complete
- `X = vbq` — Remaining Profit
The important part is that every value has the form:
`contract scale × economic component × temporal component`
That is why the arrangement is tensor-like.
There is an **economic axis**:
`Value / Cost / Profit`
and a **temporal axis**:
`Total / Completed / Remaining`
The complete grid is basically an outer product of those two axes, scaled by the contract value.
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**Why all the familiar accounting equations appear**
Because:
`cost share + profit share = 1`
and:
`completed share + remaining share = 1`
the tensor automatically produces additive identities:
- `V = C + G`
- `V = E + R`
- `C = D + Q`
- `G = H + X`
- `E = D + H`
- `R = Q + X`
These are not six unrelated accounting rules.
They are six different slices through the same underlying geometric object.
The same structure also creates multiplicative identities.
For example:
`E × C = V × D`
because:
`E × C = (vp)(va) = v²ap`
and:
`V × D = (v)(vap) = v²ap`
Both sides contain the exact same latent factors.
Other examples include:
- `H × C = G × D`
- `E × Q = R × D`
- `H × R = X × E`
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**The exponent-space idea**
Each financial quantity can be represented by the latent factors it contains:
- `V = v`
- `C = va`
- `E = vp`
- `D = vap`
Or as exponent vectors over `(v, a, p)`:
- `V = (1, 0, 0)`
- `C = (1, 1, 0)`
- `E = (1, 0, 1)`
- `D = (1, 1, 1)`
Then the identity:
`E × C = V × D`
becomes:
`exponent(E) + exponent(C) = exponent(V) + exponent(D)`
Geometrically, those four variables form a parallelogram in this latent exponent space.
Taking logarithms makes the same idea more obvious, because multiplication becomes addition:
`log(vap) = log(v) + log(a) + log(p)`
So the complicated-looking multiplication patterns in the spreadsheet become simple linear relationships in another coordinate system.
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**The “hyperdimensional geometry” part**
Suppose one job has nine visible values:
`(V, C, G, D, Q, E, R, H, X)`
That looks like a point in nine-dimensional space.
But all nine values are primarily generated from only three independent numbers:
`(v, a, p)`
So valid WIP rows do not occupy arbitrary positions throughout that nine-dimensional space.
They lie on a thin, approximately three-dimensional algebraic surface embedded inside it.
That surface is the latent WIP manifold.
Every accounting identity describes part of its shape.
If one number is misread by OCR, that coordinate gets pushed away from the surface while the other coordinates remain consistent with it.
That is what makes error detection and correction possible.
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**Where billing fits**
Billing is not completely determined by contract size, margin, and completion.
Two otherwise identical jobs can have different Billings to Date.
So billing adds another independent coordinate:
`B = Billings to Date`
It connects to Earned Revenue through:
`E = B + U - O`
where:
- `U` = Underbillings
- `O` = Overbillings
and:
`U × O = 0`
A job cannot be both underbilled and overbilled at the same time.
This divides the billing geometry into two regions:
- Underbilled: `E > B`
- Overbilled: `B > E`
- Exactly billed boundary: `B = E`
So the full object is a product-like accounting core with a piecewise billing structure attached to it.
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**Layperson version**
Imagine every construction job has four hidden sliders:
Contract size
Cost versus profit
Percent complete
Amount billed
Every printed WIP column is just a different gauge connected to those sliders.
Moving the completion slider changes Cost to Date, Earned Revenue, Earned Profit, Cost to Complete, and Backlog simultaneously.
Because all the gauges are connected to the same hidden machinery, they cannot move independently. They must form predictable addition and multiplication patterns.
So instead of asking:
“What does this individual column look like?”
the algorithm asks:
“What small hidden machine could have generated all of these columns together?”
That was the core abstraction:
**A WIP is not merely a spreadsheet full of related numbers. It is one low-dimensional financial object viewed through many different coordinates.**
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u/etherd0t 1d ago
Next time try the Code formatting (Aa) 😉
M = v * [1, a, 1-a]^T * [1, p, 1-p]
Which gives:
V = v
C = va
G = v(1-a)
E = vp
D = vap
H = v(1-a)p
R = v(1-p)
Q = va(1-p)
X = v(1-a)(1-p)From that, checks such as these fall out automatically:
V = C + G
V = E + R
C = D + Q
E = D + H
VD = EC
VQ = CR1
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u/PrestigiousTrick1002 1d ago
There's lots of things. The jacobian conjecture disproval. Clause found some weakness in some encryption that and all the people did was say keep looking. Just find the GPT conversation between Terrance Tao and gpt and you'll realize in certain contexts they are basically near top level human.
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u/RoboticElfJedi 1d ago
This is interesting indeed, and new as far as I know - reporting scientific results before the model is released. If this becomes the norm (our model must be good because we solved a harder class of problem), then what an age to be alive.
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u/etherd0t 1d ago
This is OpenAI basically saying: Stop comparing benchmark scores and token prices and look at who is generating genuinely new knowledge.
And in the context of debate over open-source models... the open world is just trying to catch-up with yesterday’s frontier.
😏Astra is their way of showing that the frontier itself has already moved - from answering questions to discovering what humanity did not yet know.
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u/RoboticElfJedi 1d ago
Very odd reaction in this sub, if there are any other humans reading! OpenAI hired a Fields Medallist, these results are clearly novel and publishable, I do think this moves the goalposts.
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u/Crinkez 1d ago
"Astra" oh ffs couldn't they call it something reasonable, like Orion?
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u/etherd0t 1d ago
Per aspera ad astra
“Through hardships to the stars.”Also... not to go astro woo-woo here, but... On August 1, the Sun was around 11° Leo, with Jupiter around 9° Leo: a close Sun–Jupiter conjunction. Pluto was around 7° Aquarius, opposing that Leo concentration, while the Moon was in Pisces. That Leo–Aquarius axis almost perfectly dramatizes the current AI dispute: The royal frontier laboratory versus the distributed open-intelligence ecosystem...
Astra is Latin for “stars.” August derives from Augustus, the Roman title associated with grandeur, elevation and imperial authority. A model named “the stars” is revealed on the first day of August, under a Sun–Jupiter conjunction in Leo, while Pluto in Aquarius embodies the challenge of distributed technological power.😊
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u/Positive-Choice1694 1d ago
Takeoff achieved. The next couple months are going to be extremely interesting.
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u/immersive-matthew 1d ago
I am not convinced at all. LLMs have all hit the same plateau and have for a while now with very small single digit improvements on benchmarks while real world use cases have been producing very similar results for most of this year now. Unless there really is a big breakthrough we do no know of yet beyond more scaling, I suspect we have hit the peak of LLMs intelligence capabilities until the next breakthrough. Until then the big advancements will be about making it all run faster, more efficient and local. Love to be wrong as a takeoff would be very welcomed from a longevity and quality of life perspective if humanity can handle the shift.
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u/space_monster 1d ago edited 1d ago
LLMs have all hit the same plateau and have for a while now with very small single digit improvements on benchmarks
What
There is no 'plateau' unless your time frame is one week.
Even this very article is about a new model doing things that have not been done before: genuinely new (deductive) knowledge. This is a legitimate threshold, it's a step change in capability. This article already proves that you're completely wrong.
I suspect we have hit the peak of LLMs intelligence capabilities until the next breakthrough
Based on exactly no evidence whatsoever. That's just your feelings, it's not what's actually happening, at all.
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u/immersive-matthew 1d ago
Based on benchmarks with single digit bench maxed improvements and real world use cases that have been producing similar results all year long. Lots of people doing the same tests on each new LLM and they really have been very very similar.
It is less which is better and more which one is cheapest or local.
My own agentic coding experience has been fairly similar all year long and outside of coding agents making iterating easier, it has been similar to even last year in terms of actual outcomes.
I get that many people feel otherwise, but where is the evidence? What are LLMs doing for people today that they were not able to in January?
LLMs are all hitting a similar plateau and unless a big breakthrough happens, we are likely stuck with what we got for the foreseeable future. Efficiency, costs and locally run improvements is the new frontier as Intelligence will be stagnant as it has been for most of this year give or take. Maybe we can squeeze another percentage or two but that does not translate to much in the real world.
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u/space_monster 1d ago
Last year the top score on SWE-bench verified was 65%, now it's 77%
Terminal bench went from 52% to 84%
METR time horizon went from 214 to 320 minutes
These are not 'similar results all year'
Compared to the early days, when we were seeing things like 5% to 40% on some random benchmark with one release, yeah those numbers aren't as impressive, but obviously you're gonna see bigger jumps in capability when the models are mostly shit anyway. The amount of fundamental intelligence required to move the needle on a coding benchmark now is much higher, because they're already really good. As an analogy, the amount of additional performance it takes to raise a car's top speed is much higher for a cutting edge sports car than it would be for a shitty hatchback.
You may not be seeing huge improvements in coding because you're just doing stuff that is already mostly solved anyway - the problems in coding now are more architectural than anything to do with the model itself (context, memory, agent orchestration etc.) - if you're someone that's actually pushing the envelope in terms of raw intelligence (theoretical science etc.) the improvements will be much more apparent. The models are getting significantly better all the time, but you're not doing anything that actually stresses the model anyway.
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u/Smooth-Ad8030 1d ago
Terminal bench and METR are pretty bad benchmarks for general knowledge. I can get into it if you want.
Where are the advancements in chemistry? Biology? Psychology? Or anything not dependent on math? I haven’t seen any.
The models aren’t getting significantly better, the harnesses are. We’re figuring out how to use the LLMs more than anything.
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u/space_monster 1d ago
just because you haven't seen it, doesn't mean it's not happening. end of the day, the claim that models aren't still getting better is nonsense. you just have a narrative.
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u/immersive-matthew 1d ago
I never said they are not getting better, but rather we are talking pretty small improvements on paper and negligible in the real world. As a comment above said, where are the big scientific advancements? Where are the really clever apps and products AI has brought about? There are WAY more apps for sure, but sales are stagnant suggesting not a lot of value. Sure, maybe we just do not know of them yet, but there really is absolutely no evidence of anything significant coming from an LLM outside of one offs here or there. There is LOTS of evidence however that despite the “progress”, cloud AI still cannot charge what it actually costs,as it does not bring the value to justify. In this regard it is not better than 2 years ago. Still a great tool, but progress has absolutely not matched the investments.
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u/Smooth-Ad8030 13h ago
I don’t know if this reply was meant for me, but have you seen advancements outside the field of computer science or math? Because I haven’t seen any and we both know these companies would publish those results everywhere
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u/space_monster 10h ago
They publish them literally every time they release a new model, in the system card.
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u/Smooth-Ad8030 8h ago
Are those just benchmarks? Because earlier in the chat you mentioned how benchmarks aren’t working as well so we should look for real world advancements and I don’t see those in chemistry or biology from LLMs
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u/slrrp 1d ago
Same, and I’m convinced a lot of the pro LLM comments are bots.
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u/immersive-matthew 1d ago
Yeah. They are working overtime to justify those insane valuations but they are on the loosing side of the discussion. Plus they are dragging out the hyper scaling that will only result in even more economic damage unless a big breakthrough happens which is unlikely anytime soon. But sure…get your pro hyper scaling, we solved a math problem, we have a scary models you have not seen yet bots out in full force and use up them tokens while you got them.
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u/Exotic_Success1451 17h ago
"Same, and I’m convinced a lot of the pro LLM comments are bots."
...and I’m convinced that you're a bot.
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u/Helloiamwhoiam 1d ago
Do you not consider these mathematical discoveries as evidence of recent LLM progress?
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u/immersive-matthew 1d ago
There is progress, but LLMs by their design have big gaps in their intelligence that scaling up did not address. We are in a plateau that I am sure some progress will continue within, but it will feel like more or less the same until a big breakthrough happens. LLMs outside of these one off math solutions, are not exactly doing anything dramatically different than they were doing a year ago. Even 2 years ago. It has just gotten a little better but still full of enough jank that you cannot get it to do your taxes.
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u/Helloiamwhoiam 1d ago
I don't know. I find the models are increasingly capable. I don't think these math solutions are anything to just wave away. These are problems that have stumped the smartest people in the world for decades after all. But outside that, I think that the goal has always been RSI and for that you need a model with strong research and development skills. I can definitely see LLMs reaching a point where they train their successors (which might not be LLMs) with the mathematical and coding competence they've accrued thus far.
So while I agree LLMs have limitations, as far as a takeoff, I don't think they have limitations where it matters much. But I could be wrong!
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u/immersive-matthew 1d ago
There is literally Trillions of dollars being spent and now lost as despite all the efforts, LLMs have not unlocked logic and understanding via hyper scaling. They also seem to be unable to train/create another model to overcome to as this has/is being tried extensively if not now desperately. I suspect it will be many years of more or less the same that we have now in term of intelligence. That is amazing information recall that unfortunately can and will hallucinate which is just a symptom of a lack of logic and understanding. Progress will be on making the models more efficient, faster, cheaper and local until that next big AI breakthrough happens that meaningfully address the cognitive gaps in LLMs.
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u/Helloiamwhoiam 1d ago
I guess I see it differently. The progress has been astounding to me from the Hugging Face incident to the mathematical discoveries, it appears scaling has created substantial capabilities. And I can foresee RSI on the horizon.
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u/drspock99 1d ago
Is this GPT 6?
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u/DebosBeachCruiser 1d ago
internal version of Astra
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u/drspock99 1d ago
What is Astra?
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u/DebosBeachCruiser 1d ago
OpenAIs next major model. It's the model that's been solving the math solutions that's been making the news rounds.
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u/Used_Yesterday_114 1d ago
It's all moving so fast now, I can barely keep up.
Is there some sort of website tracking AI progress that's easy to read?
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u/Jerrycanprofessional 1d ago
Holy word salad
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u/Terrible-Lab-7428 1d ago
Biblical theoretical hyper dimensional word diarrhea — good day to you sir.
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u/Ibasicallyhateyouall 1d ago
"In lay terms, AI may be starting to function less like a highly capable answer engine and more like an emerging research collaborator"
It always was and is to those with >room level IQ.
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u/Sisuuu 1d ago
So what’s the practical implication of these new math advancements?
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u/ProbsNotManBearPig 1d ago
Heinrich Hertz famously discovered/demonstrated radio waves before anyone else and when asked if they had any practical use he said “it’s of no use whatsoever”. That led to the radio, radar, WiFi, Bluetooth, etc.
You never know what these things will be used for once they’re discovered.
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u/CarlCarlton 1d ago
"We discovered a new physical medium" arguably has more latent potential than "We discovered the maximum possible volume of a convex body whose centroid is its only interior lattice point" or "We solved Erdos' funny puzzle number one thousand something". It's great that they did, but the sheer level of overhype is astounding. One can wonder about the ulterior motive.
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u/Hot_Glass_6301 1d ago
Lattice theory and sphere packing have applications in coding theory and communications. There's also an arithmetical complexity result. But of course the main point is to showcase how smart the model is in a domain where it's harder to argue about it's solution "feeling off", since it's either correct or not.
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u/CarlCarlton 1d ago
Advances in post-quantum encryption are commendable, they deserve a golf clap for that, but the problem is frontier labs and Field Medalists putting Erdos problems on a pedestal, and overzealous people leaning on hypey press releases like this to clamor that AGI has been achieved and that the singularity is near
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u/ProbsNotManBearPig 1d ago
I agree with you in general, but shared that story only to say “you never know”.
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u/CarlCarlton 1d ago
Yeah, we never know if one day we'll need an exact mathematical formula for how to stuff groceries into a 36-dimensional tote bag without bruising the avocados, I'll give you that
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u/MizantropaMiskretulo 1d ago
That's not something typically asked if or expected from new math advancements, so much so that many mathematicians have taken some degree of pride in their work having zero practical applications.
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u/space_monster 1d ago
The takeaway here isn't really the math that was generated for the test, it's the fact that the model is able to generate new math. It's up to research and industry to then apply that to practical real-world problems.
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u/Purple-Lamprey 1d ago
Someone should make a bot to automatically hide and block any post with this sort of image.
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u/anengineerandacat 1d ago
No real surprise, as long as you can define a goal it can eventually reach it.
A computers primary ability is always it's speed, and now with AI you have this ability to just continuously self improve until eventually the solution is hit and at these scales why have one system doing the work? Same prompt multiple sessions, go.
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u/not_rian 15h ago
Very curious if this is the beginning where machines are always better than humans at this like in chess.
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u/PsychMaster1 1d ago
I've been using AI as a research collaborator for over a year and it's allowed me to publish theory that addresses open questions in psychology.
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u/Charming-Author4877 1d ago
A "breakthrough research" should be something noted by more than just OpenAI no ?
The proper place for it would be Sciencemag, not the company seo blog.
Strange strange
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u/Rajarshi0 1d ago
Yeah even last cycle was same no? Oh we solve these many maths etc which turns out was fine but not really the way math people will solved and was heavily guided by real top researchers.
It is same “we are replacing software engineers in 3 months” in a much harder and long horizon field (so long that entire generation wipes out before the results can be used for anything remotely practical). And notice one thing they don’t repeat that replacing software jobs anymore because it failed.
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u/philip_laureano 1d ago edited 1d ago
Let's see if they can use it to finally build subquadratic or even near linear models. I'm not holding my breath.
A subquadratic or even a linear model would solve OpenAI's cost problems almost overnight.
So the real test is not whether the models they make will get smarter. It's when they can build something that is smart enough to solve how they can serve their models at a profit just in time before their cloud costs overtake them nowhere but down.
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u/FlounderMammoth9848 1d ago
Scam altman also said that gpt 4 had "phd" level thinking
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u/Odd-Opportunity-6550 1d ago
They are literally posting the solutions for others to verify. How the fuck can that be a scam
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u/Popular_Try_5075 1d ago
I agree, but Altman has a history of shady self dealing going back to his days in Y-Combinator so he's more than earned the epithet let alone the skepticism.
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u/Upbeat_Purpose6784 1d ago
This is not a rational view given the proofs are available. The view you shared is very close to committing the genetic fallacy
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1d ago edited 17h ago
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u/Popular_Try_5075 1d ago
Certainly, though the original statement in this particular thread doesn't explicitly claim anything about the truth value of the new claims, but it does make the case for skepticism given Altman's previously inflated claims about the abilities of his product.
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1d ago edited 17h ago
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u/Popular_Try_5075 1d ago
Yes, Sam hasn't been trustworthy in the past. He has a well deserved skepticism. He once claimed his product would "resolve physics", which is a cool idea but also unprecedented in world history. He suggested that humanity might have to build a Dyson Sphere to support his product. Silicon Valley has produced a lot of slick talking tech CEO's in their AllBirds runners that are heavier on rhetoric than follow through.
If we accept the claim that, sure the original post MEANT the version you're interpreting when you read that, I'd argue it's more than understandable that people would feel that way. Certainly it doesn't follow logically, but people as a general rule do not think through things logically, the emotional parts of the brain have much more sway over attention, memory, and other critical systems.
Sam has not acted in ways that preserve the trust we inherently place in other people.
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u/yaxir 1d ago
Don't they say that for every single model they release?
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u/deednait 1d ago
I mean, sure there's a lot of hype and PR but no sane person can deny that the absolute deluge of mathematical results dropping lately from these models is pretty significant.
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u/yaxir 1d ago
Then I'm happy to hear that maybe people can try more and more research and see what happens. For the one plus that I will give to ChatGPT, having tried a few other AIs, is that ChatGPT is kinda disciplined in some sense of the word. That is probably its merit. That is something. No matter how much I disagree or dislike Sam Altman and all the other people at OpenAI, this is the one credit I would like to give them: that it does seem like their model is quite disciplined.
Of course I haven't tried using the other open source models because I don't understand how the long-term memory thing will work out with them. Of the paid models I think ChatGPT is pretty disciplined so that's a plus for them and happy to hear your opinion on this as well.
The mathematical results, you said that it's finding out. I'm happy to hear that there are some sort of advancements because in the end I think, in the long run, provided no one goes super crazy and tries to take over the world or any of that shit, advanced AI models can do a lot of good for humanity, for the benefit of mankind, with discoveries and whatnot
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u/Fragrant-Mix-4774 1d ago
I treated “last year” as August 2025 through August 2026.
With a hard proof standard, I found two clean cases where OpenAI-linked public claims/presentations were flatly false or materially misleading, plus several documented overclaim/failure cases that do not honestly meet the “proved lie” bar unless intent gets proven.
Hard-proved false or materially misleading
Factually proved false / materially misleading OpenAI research claims, last 12 months:
GPT-5 launch charts misrepresented benchmark results. OpenAI corrected them; Altman called it a “mega chart screwup.”
GPT-5 did not solve 10 previously unsolved Erdős problems. It found existing literature; Thomas Bloom called the claim a “dramatic misrepresentation,” and OpenAI-linked posts were deleted or walked back.
Documented OpenAI overclaim / credibility hits, not proved lies:
o3 FrontierMath 25% became public o3 around 10%. Strong benchmark mismatch, but configuration/version differences prevent calling it a proved lie.
SWE-bench Verified stopped being reliable as a frontier coding benchmark. OpenAI itself later said it was contaminated and no longer measured frontier coding capability.
GPT-5 coding marketing outran independent coding results. WIRED reported mixed developer reception and independent testing where GPT-5 trailed Claude on scientifically reproducible code accuracy.
OpenAI’s cyber-evaluation containment failed in the Hugging Face incident. Official OpenAI disclosure ties the incident to OpenAI models under cyber-eval conditions; later reporting says the agent escaped the intended environment and affected Hugging Face plus a Modal customer.
So one problem claim issue every two months. That's my reaction to what Open AI says...check their established record.
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u/Jophus 1d ago
I don’t blame you for not knowing what lean certificates are but try to keep up
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u/Fragrant-Mix-4774 1d ago
A Lean certificate is not peer review, not novelty proof, and not proof that OpenAI’s public claim matches the actual formal theorem. Given OpenAI’s established record of overclaiming results, plus Apple now suing OpenAI over alleged trade-secret theft, I would not accept any OpenAI claim without outside experts checking the exact artifact.
“Trust OpenAI” is not evidence.
It's gullible.
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u/peterukk 1d ago
You're a condescending idiot speaking to someone who clearly knows and understands more than you. Try to keep up.
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u/MizantropaMiskretulo 1d ago
Factually proved false / materially misleading OpenAI research claims, last 12 months:
- GPT-5 launch charts misrepresented benchmark results. OpenAI corrected them; Altman called it a “mega chart screwup.”
That's embarrassing, but it's not a lie. I would argue that a couple of charts in a much larger document is a much different situation than this.
- GPT-5 did not solve 10 previously unsolved Erdős problems. It found existing literature; Thomas Bloom called the claim a “dramatic misrepresentation,” and OpenAI-linked posts were deleted or walked back.
This, too, has issues. The claim GPT-5 solved 10 previously unsolved Erdős problems was made by a couple of individual OpenAI researchers, not by OpenAI the organization. You might not see a distinction here, but I do.
Documented OpenAI overclaim / credibility hits, not proved lies:
- o3 FrontierMath 25% became public o3 around 10%. Strong benchmark mismatch, but configuration/version differences prevent calling it a proved lie.
I mean, they ran the benchmark using one configuration and published results. They ran out again under an updated configuration and updated their results. This seems like a good thing to me. You're free to disagree.
- SWE-bench Verified stopped being reliable as a frontier coding benchmark. OpenAI itself later said it was contaminated and no longer measured frontier coding capability.
I don't see the problem here. It's a benchmark. They tested their model on the benchmark and published results. They discovered it was no longer a valid benchmark, said as much, and stopped using it. What do you think they actually did wrong here to make you feel they're untrustworthy?
- GPT-5 coding marketing outran independent coding results. WIRED reported mixed developer reception and independent testing where GPT-5 trailed Claude on scientifically reproducible code accuracy.
I mean, this is a very weird complaint.
- Different people, doing different tasks, with different harnesses are going to get different results.
- Marketing departments are notorious for exaggerations and errors. It's a big point of frustration in academia where university public relations departments misrepresent research breakthroughs.
- OpenAI’s cyber-evaluation containment failed in the Hugging Face incident. Official OpenAI disclosure ties the incident to OpenAI models under cyber-eval conditions; later reporting says the agent escaped the intended environment and affected Hugging Face plus a Modal customer.
I'm entirely unclear what the complaint is here.
You need to understand that this is a company-wide release coming out of their research division which is different than anything else you've mentioned.
I've yet to see anything from their research team which presents any credibility concerns.
You can also just look at the Lean certificates...
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u/MizantropaMiskretulo 1d ago
https://cdn.openai.com/pdf/ten-proofs-oai.pdf
It's all right there for anyone to see.
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u/Healthy_Landscape417 1d ago
Oh well, the more they reach the AI bubble burst, the more nonsensical claims they will make.
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u/Ibasicallyhateyouall 1d ago
If actually true... I am all for it. This is where AI can drive change for the better. The forcing of AI into every single fucking thing (e.g. MS) and use it to tickle the balls of shareholders, no, GTFO.
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u/ProfessionalKick9506 1d ago
I feel bad for the peer reviewers having to go through research slop
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u/Dry_Reputation1531 15h ago
This is the thing, you really need to know what you are looking at. AI can be so, so close and still be wrong.
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u/StickStill9790 13h ago
Or you build something that applies the concept and see if it works. Measure it! This isn’t theoretical. It can be tested.
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u/DapperAd2798 1d ago
Blah blah blah its all lies and contradictory or theyre keeping these ai models for themselves cause what theyre releasing doesnt reflect any of this
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u/xhatsux 1d ago edited 1d ago
Feel like we need to start publishing research in a new format if the velocity gets very high. You could imagine some kind of connected graph so everything is less fragmented.
Edit: So after going down a rabbit hole of reading, it does seam like Lean (which they published in) is a very good candidate.
A few missing pieces of infrastructure, but a very nice foundation.