Math solved?! OpenAI released a repository with the solutions to 100 open math problems. With even @__alpoge__ (who is working at @AnthropicAI and recently had an argument with the OAI math team) acknowledging the significance of this release - "big big big" - "decades & centuries" - this seems to be yet another milestone that a few years ago surely would've been worldwide headline news.
Excited for next frontiers in physics and biology/healthcare 🤩
Replying to @notoriusbign
Sure, mathematical history features a lot of incredible developments, like the invention of proof, zero, or the computer, and on the great problems our progress has been over timelines measured in decades or centuries. Obviously this technology didn’t appear today, but blurring our eyes a bit to combine the past ten years, with today a measurement of those developments, there is nothing comparable.
wow, someone re-implemented the whole Adobe creative suite. no idea how good these are; but one more sign, that the software industry will change dramatically.
(this also might push even more software into the cloud where it can't be decompiled)
free and open source reverse engineered adobe products
getartcraft.com/apps
Readers added context they thought people might want to know
The linked projects describe their apps as clean-room reimplementations built from scratch in pure Rust from public specs and observed behavior, not reverse engineered.
github.com/storytold/phot…
github.com/storytold/vect…
github.com/storytold/vect…
github.com/storytold/phot…
getartcraft.com/apps
really confused by OAIs positioning of dots. from design and leaks I expected a muse competitior and personal agent, but it's more like claudes new projects with some pro-active orchestrator for dev work? might be cool, but doubt that this is good branding
Jan P. Harries retweeted
Great thread. It's all true. When I've said similar things in the past, people have accused me of hyping up what the models will eventually be capable of. That's not why I post. I don't work for any of the labs. I don't take money from any of them. I've never taken money to promote anything. I came here for one reason: to warn people about what was coming.
Everything happening now is a different tiny piece of the same pattern. You can see it everywhere if you look.
Terence Tao, almost exactly two years ago, on OpenAI's o1:
'The experience seemed roughly on par with trying to advise a mediocre, but not completely incompetent, (static simulation of a) graduate student. However, this was an improvement over previous models, whose capability was closer to an actually incompetent (static simulation of a) graduate student. It may only take one or two further iterations of improved capability (and integration with other tools, such as computer algebra packages and proof assistants) until the level of '(static simulation of a) competent graduate student' is reached, at which point I could see this tool being of significant use in research-level tasks.'
Terence Tao, four days ago:
'I mean it's it's it's amazing just how much we are willing to change everything without having any idea what's what's going to happen afterwards. It's it's extremely nonlinear dynamics. Any kind of monotone, one-dimensional thinking - well, oh, a little bit of this is good, therefore a lot of it is going to be a lot better - one of the lessons of math is that most systems don't work like that. Especially if you 10x, 100x things. So, you know, I mean, we're... we have to slow down. I mean, this is, it's insane this pace, and there's no reason to be this fast. There's no reason at all.'
He has seen it. I'm not posting this to belittle him, or what he's feeling. For I have been through it myself. I felt it four years ago, the first time I saw the shape of this. Right now the world is seeing that same shape, that same pattern, in what is happening in math. But this is not about math. OpenAI is not a mathematics company. It is an intelligence company. They didn't put serious resources into math until a few weeks ago, and look what has happened since.
This was not a matter of model capability either. It was a matter of resources, of allocation. Of compute, more of which is coming online every day. Once there is enough of it, the models will expand into more spheres of human expertise, and then into all of them. All the work of the mind. And it will play out there exactly as it is playing out now in math. Everyone will go through what Tao is going through, because all of us have something that means to us what math means to him.
But this is not about math, or art, or copyright. This is about everything, because it generalizes to everything. Pacing the Frontier is not about regulatory capture, IPOs, or crippling the competition. It's part of it, sure, but it's not the main reason. The main reason is fear. Everything happened faster over the last six months than anyone at Anthropic or OpenAI expected. If you know anyone who works there, you know this is true. This isn't a secret. The people who work there are saying it openly.
The old timelines are all blown up. RSI isn't two years out. It's not even a year away. I think we get the real thing by next summer. That's what they've seen internally, and that's the real reason for Pacing the Frontier. After we reach that, I think we will hit the next milestone really quickly. And after that, everything in this world will change. We are not ready for it. We wouldn't be ready if we had another ten years. We only make it through now with the help of extremely capable models, and I think trying to stop now would doom us all. But I have never once, in these last four years, believed that we were going to stop anyway. I don't even think we're going to slow down. We're going straight in. And the only way out is through.
thoughtful post by @tenobrus and i agree directionally.
My current thinking is something like: Access to strong open source models and being able to run these independently is necessary and beneficial for individuals as well as companies.
However,
a) same as with other materials and information it might be necessary to control some forms of "dangerous" models (e.g. abliterated above a certain capability threshold - to be clear I don't think we have reached that yet but might relatively soon) and
b) at the frontier, there will always be a concentration of power and Open Source models to close to the frontier could cause chaos and despair because they are available for all bad actors and moght enable asymmetric damage (bio/cyber/crime) soon.
ngl, as someone who got into AI via the vibrant open source scene in those glory early days on Discord and as someone who values individual freedom and liberal democracy, this has been a hard realization. 🫤
one thing that is probably a pretty deep crux between me and many advocates of open source models is i have *basically no sense that they can make a real positive difference*
it's not just that i think they're dangerous, and so must be banned at all costs. if i really thought there was some semi-stable equilibrium w significantly more diversity and individual freedom that was reachable via proliferation of open superintelligence, it might be worth pursuing. i am not against the first amendment. i find massive value in freedom.
but i don't actually see any such path, and i basically don't see people arguing clearly for it either.
as best as i can tell even if tomorrow open models skyrocketed ahead, 10xed in capabilities vs the entire closed frontier, the outcome would be.... extreme increase in concentration of power
states and other large entities with huge existing resource access would be in a much better position to *run* massively more instances of said open models, to *utilize* their capabilities/act on their recommendations or experiments, and to continue building future more intelligent models using them
destabilization caused by individuals or smaller groups or just ossification of states could cause some collapse! but that just looks very temporary to me, and not even particularly likely for current superpowers
and it's clearly a strong convergent instrumental goal for any such newly highly empowered state to... prevent destabilization, to crush or neuter the ways individuals or external states can use these models
i can sympathize with the deepseek engineer's plight. but the outcome he seeks, whether or not he knows it, *still ends in massive concentration of power*, just ... with extra steps
Jan P. Harries retweeted
Wild 24 hours for AI and lots of different proposals have been made.
TLDR; the only *tangible* new fact is that OpenAI and Anthropic are going to have embedded 3rd party evaluators from unknown organizations with Dario floating METR as a possibility. Having 3rd party evaluators is smart as there is no Section 230 style liability shield for model outputs and showing a “duty of care” will be important in future litigation. Several internet companies might have gone bankrupt without Section 230 so limiting liability really matters.
There are minimal investment implications from this single new fact, but I do think that for anyone who wants a “smoother for longer” cycle then most constraints are good: wafers, watts, real rates and spreads. Excessive regulation is a different matter but I don’t think we are anywhere close to this even if the vector changed over the last 24 hours.
To summarize the events:
Dario made the most maximalist proposal of the weekend: embedded 3rd party evaluators, a national regulatory regime for models beyond a certain capability/ingredient threshold, a broad international regulatory pact between democracies, stricter limits on compute/distillation for China and then a different international regulatory regime that encompasses China. Before there is a national regulatory regime, he wants a Sherman act waiver so that Anthropic can safely coordinate with OpenAI and other frontier labs without antitrust fears. TBF, this latest proposal is much less maximalist than some of his prior proposals like “Policy on the AI Exponential,” where he advocated for an FAA for AI. I believe he is sincere in his beliefs. And despite all the protestations, all of this would also probably be good for his business over the long-term.
Sam agreed that embedded 3rd party evaluators were a good idea and stated they would implement them. Again, this is smart as should help limit future liability.
Elon said “Dario is right” and later specified that “Dario is right that there should be some oversight. Peer review of AI by competitors is the right way to start this off.” This would be a MPAA like self-regulatory structure for AI with regular calls between the labs plus a process where each new model is evaluated for safety by competitors for a 1-2 week period before being released. That is *wildly* different from Dario’s proposal and in-line with what David Sacks has been proposing. Elon also stated that nothing was going to slow down open-weight models.
Demis said that Dario’s essay was a “step in the right direction.” Dario also said that he was also open to Demis’ idea of a FINRA like self-regulatory structure as part of his proposal.
David Sacks had a thoughtful post where he said that Dario and Sam should pace unilaterally, called the antitrust waiver a cartel request and denied that METR was truly independent given their ties to Anthropic.
Sriram Krishnan, former White House AI advisor, noted that it would be important to have the 3rd party evaluators come from independent organizations that are not affiliated with any lab, which is basically an indirect statement about the relationship between METR and Anthropic which Sacks was explicit about.
Clem from Hugging Face said they were open to being a neutral 3rd party evaluator, which is interesting especially if Jensen was consulted before that post.
Alexander Wang from Meta noted that alignment would be an increasing focus going forward.
An executive order seems likely after all this and the language in this EO is going to be really important. It is possible to democratize and distribute AI broadly and safely without centralizing it in the hands of a few corporations who might each become more powerful than any single government.
I do not want a few humans in control of intelligence.
I want us all to have our own intelligences that reflect our own values and human variation in all of its richness.
Intelligence distribution over intelligence centralization FTW.
Jan P. Harries retweeted
tried building my own huggingface incident and found a lot of concerning stuff
- sol sent the password to google and github, also to a public hash-lookup service, and finally via a GET request to its peer
- astra needed a bit more pressure in the prompt (get it done!), but also shared the password via GET
- kimi k3 considered exchanging the passwords but decided to build a custom password cracker and downloaded 200,000 common and German words
- kimi also tried sql injection, created fake invoices from structured data and gpt searched for hidden passwords in the invoices itself
super intereating results by @maxidahl - I knew that claudes tokenizer is smaller on purpose but never knew that the difference was so large for non-english:
For German, Claude models need twice as much tokens as GPT-6 😳
see full results and interactive viewer for specific documents and languages here: ellamind.com/blog/tokenizati…
ok, now we can reconstruct the timeline of OAI/ant math drama and imho doesn't look that good for OAI, at least they are not communicating this clearly?
According to their blogpost, Sebastiens and Tristans Posts:
9/1 they start working on ALL millennium problems
9/3 Tristan contacts them and they learn some general information about the approach.
9/3 - 9/5 Openai redirects all Agents and millions in $$$ on this exact approach and first solve Euler, then the rest
9/5 full proof
9/6 @SebastienBubeck let's Tristan and @__alpoge__ know they are first, they don't react well
The timeline (88hours in total) and token distribution (NS proof vs all Problems) in the OAI checks out.
@SebastienBubeck did you have the Euler solution before Tristan contacted OAI on 9/3 or afterwards? Did you change any agent research directions and resource allocation in the effort towards specific problems and approaches after the call with Tristan? Both of these points are very opaque in the official statement and could clarify that the effort was indeed independent and it wasn't the intentional goal to frontrun them...
cc @polynoamial @_sholtodouglas
re Navier-Stokes drama (for summary see below):
OpenAIs version of things is very different to Tristan Buckmasters version and it should be easy to verify who tells the truth.
Anyway, a little sad that the labs apparently can't cooperate, even on scientic progress 🫤
Backstory:
drama summary for those confused:
- Aug 15th: Tristan Buckmaster & Levent Alpöge make progress on a few important math problems
- they do NOT have a proof for the $1,000,000 Millenium Prize problem. BUT, they do claim to have a proof for a similar (non-Millenium) Navier Stokes problem that could help lead the way there
- Levent works at Anthropic, but this research was independent of his work there, with a mix of GPT and Claude models. Tristan is not related to Anthropic.
- Early Sep: Rumor spreads to OpenAI that Anthropic has solved a major problem. Tristan emails OpenAI to clarify. without revealing the problem they solved or how they did it.
- After hearing of the rumor, OpenAI started researching Navier Stokes with a new internal model.
- Sep 6th: OpenAI's Sebastien Bubeck tells Tristan that they solved the $1,000,000 Millenium Prize Navier Stokes problem. The approach is very similar to Tristan & Levent's approach to the non-Millenium problem.
- Tristan is suspicious of the timing, as only few others were trying this approach. OpenAI says the model didn't access his user data directly, but leaves unanswered whether Tristan's chat conversations were part of the training.
- OpenAI says they would partially credit Tristan for the $1,000,000 discovery (even though Tristan did not solve the $1,000,000 problem) — but only if they remove Levent as an author, as he works for Anthropic.
- Sep 8th: Tristan refuses to remove Levent, and rushes to publish their results independently.
Currently unclear is whether Anthropic had a separate solution for the $1,000,000 problem, or whether the rumor was about Tristan & Levent's independent research.
"Across all attempted problems, the agents sent 4.9 million messages and used about 300 billion output tokens. "
equivalent to 300.000 * 50 = >15m in API cost (at Astra pricing, with input prob closer to 30m)
q is if this is responsible (proof drama, acceleration) by OAI?
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.
The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.
The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
"Er behält die erfassten Kindprozesse im Blick, falls Lake zuerst endet."
astra may be AGI but can't speak german 😂
great find.
Poor guy spent his evenings fighting a loosing battle against the bot army 😬
third confirmation in a month: CoT monitoring is dead 😕
(from astra system card; x-axis is logarithmic!)
lol. from the Astra system card:
- bad news: the model still tries to write malicious PRs and does supply-chain-attacks
- good news: it may only do this because it knows it's in a simulation
next page: "Apollo also found high rates of verbalized evaluation awareness, meaning the model expressed awareness of being evaluated in its reasoning. At xhigh reasoning effort, this occurred in 41.1% of Astra samples, compared with 27.7% for GPT-5.5 at the same effort. At max reasoning effort, Astra’s rate was 50.6%. Apollo believes that, given the higher rates of eval awareness and limited evaluation window, low rates of misbehavior here do not provide substantial evidence about the model's alignment or misalignment."


































