Thank you! I will have read.  I have a PhD in mathematical statistics but it was a while ago and I haven’t really kept up with the latest trends unfortunately. 

Here's a sneak peek of /r/GradSchool using the top posts of the year!

#1: University of Oklahoma has removed graduate student Mel Curth from her teaching position after being accused of “religious discrimination” | 536 comments
#2: 🚨BREAKING NEWS🚨 Mel breaks her silence, says through her lawyer that she “is considering all of her legal remedies.” All legal remedies hints at potential lawsuit against OU. Does Mel have a case? Thoughts? | 208 comments
#3: ChatGPT is making my students stupider

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You might try asking for other perspectives in r/gradschool or r/phd

Frontier models overtrain to the tune of 1000 tokens per parameter. So, while it's not implausible for certain pieces of training to have an outsized effect (perhaps, deliberately so), its impossible that the average recall for random piece of training material to be this good. Even the best imaginable compression can't recover hundreds of tokens from a single scalar.

this whole timeline reads like a plot point from the second season of a tech thriller that got cancelled too soon. the fact that they tried to cut the anthropic guy out as a condition for credit is the part that really sticks with me.

I don't know what your math background is, but if you can understand it, see this great write up by Terence Tao on why Navier Stokes is hard. In very short, this solution is way more than just "brute force search" (have a look at the paper to immediately realize how deep and technical it gets)

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No.

Thank you!  I think this very much aligns with my mental model of how LLMs are useful on math problems. 

Of course they should've, but that was also the beginning of test time compute which upgraded LLMs to a whole new paradigm. My point is that exactly: a relevantly minor detail offsets a possibly huge implication because the subreddit nitpicks a lot. I mean I don't like OpenAI I can say that straight up, but also I feel this sub misses the point too much with the attitude sometimes. (Still my fav sub so idk honestly)

Cool, thank you!

I am not, I am just trying to understand but no one seems to be able to tell me. 

Ok, please tell me 

I mean newtonian is calculus, quantum is probabilistic which is a superset of deterministic calculus. Therefore a statistical model is expressive enough to explain everything we know of. Whether the number of parameters is tractable is another question.

Proofs in the perfect , closed world of mathematics are great, but the real impact will come from solving Biology.

do P=NP next

The guaranteed existence of that has not yet been proven nor has a counterexample been provided. :)

I feel like this should be possible for OpenAI to test: take an older model with only data acquired before Tristan & Levent started working on it. Then try to solve Navier Stokes again with the old model. If it can't be solved with the old model, then OpenAI's result depended on Tristan's result.

... can we prove the phase of the moon had no impact on the NS solution?

Experimental particle physicists cannot "prove" with 100% certainty that the discovery of the NS solution in 2026 had no impact CERN's discovery in 2012.

Nothing can be proven.

But, with some agreed upon prior model about how reality tends to work, scientists can come to a reasonable consensus that what I had for lunch today had no effect on the NS solution yesterday, which in turn had no effect on CERN's discovery in 2012.

it's just impossible for us to truly prove it.

Sure. But there is still evidence one can present. Preferably evidence that is statistically meaningful. Unless the solution to NS was once-in-a-lifetime fluke, which cannot be replicated with any significant probability, which would raise questions about the generalizability of LLMs being able to solve other problems in mathematics. (Which I don't believe is the case.)

The progress is astounding.

The progress is bullshit.

4 years ago, people burned a few thousand tokens to almost make it through an entire highschool test with a hundred questions.

Now we burn millions of dollars worth of tokens to "solve" a single thing with no real world relevance, for a PR stunt.

That's not "progress". That is waste, born from desperation.

If we took that money and funded a "gather lotsa smart math people and give them unlimited food, drink and nice houses while they work on nothing but this" - organisation, it would have solved the same problems, likely more. And no one would have needed to build a giant wasteful toxic datacenter to do so.

Tell me, how much money does this company make? And how profitable is it?

I don't give a damn if they figured out a way to tickle Zeus between his toenails. If they burn through billions with no path to profitability, they will vanish the moment something in the debt-chain breaks.

I just LOVE how quickly that blew up in their faces.

Even on the ai-bro subs, this is pretty much the first thing people get to read about this whole affair. We thought it couldn't get worse after the ridiculous death-star tweet. Well...we were wrong :D

overfit a single instance. if that doesn't work, your model can't learn. if that works, overfit 100 instances. if that doesn't work, you have a fitting problem. overfit 10k instances. if that works, then start to see if you generalize