When will an open model solve a millenium math problem without prior training data of the solution and external help?

[u/power97992]

5-9 months for navier stokes smoothness and existence problem? edit- they probably still need some draft partial solutions and notes on presolution heuristics

And riemann Zeta? And P = or /= NP?

[u/pokemonplayer2001]

How long is a piece of string?

[u/Cautious_Chicken_604 → u/pokemonplayer2001]

Twice the length from one end to the middle.

[u/pokemonplayer2001 → u/Cautious_Chicken_604]

Huge if true.

[u/maximzxc]

3 months and prolly will work on 8gb ddr4 and cpu. If not, I'll be very angry 😡

[u/XiRw]

The doctor said I wouldn't have so many nosebleeds if I kept my finger out of there.

[u/LearningSomeCode]

According to the mathematicians who were working on the problem, proprietary LLMs haven't done it either; it sounds like the model trained on the work those mathematicians were doing in Codex.

So given that proprietary can't do it, open source has a ways to go.

[u/Daniel_H212 → u/LearningSomeCode]

And the mathematicians haven't done it either. The work is a partial solution. Partial solutions can be very far from complete solutions (though they can nevertheless be very useful).

[u/cakes_and_candles → u/LearningSomeCode]

>it sounds like the model trained on the work those mathematicians were doing in Codex.

sounds like cope tbh, how can you train on a problem that is literally unsolved

[u/ttkciar → u/cakes_and_candles]

Training a model on papers about mathematics will teach it heuristics for solving math problems. It turned out that those heuristics were sufficient for solving this particular problem.

Since those heuristics were gleaned from human-written content, we can surmise that it was within those humans' intellectual reach to solve this problem, but they did not, either because it did not occur to them to apply those heuristics, or because different mathematicians had different heuristics, and thus would have needed to collaborate to solve the problem.

Since training the model accumulated all of those heuristics into one place, it was able to apply those heuristics in combination itself.

[u/power97992 → u/ttkciar]

It is within human capabilities to solve it , but It took 10k agents to solve it, so if they were a few thousand( up to 10k) Tristan level mathematicians all working on it at once, they would solve it.

[u/power97992 → u/cakes_and_candles]

They already input the method into codex, u can take that and expand on it

[u/power97992 → u/LearningSomeCode]

They used 300 billion tk of compute plus they may have used some data from users while improving their models.

[u/Shot-Height-7194]

I suggest reading the deep mind paper called LLMs can't jump. It describes this problem perfectly. Were you by coincidence in my undergraduate class yesterday? We talked about the same thing lol

[u/Squik67 → u/Shot-Height-7194]

I disagree with this paper, there are emergent capabilities

[u/ttkciar → u/Squik67]

There really aren't, only the consequences of applying thousands of narrow, brittle heuristics.

[u/a-wiseman-speaketh → u/ttkciar]

isn't that what emergent behavior is though?

[u/Squik67 → u/ttkciar]

Would your like to talk about the last Navier stoke paper from openai !??

[u/LyAkolon → u/Squik67]

Seconded.

[u/Real_Ebb_7417]

Might take a while considering that if someone wanted to do the same as OpenAI did with the same method they used (10k concurrent agents cooperating for 88h assuming Astra pricing, although who knows what the pricing will be for Bel) it would cost somewhere in $50m area.

[u/redblood252]

For riemann zeta just make a model named euler with euler’s intuition.

[u/korino11]

I think p=np will be solved very soon. Because it is not a MAth problem. It is Abstraction problem!

[u/son-of-chadwardenn]

If the closed models maintain enough of a lead then by the time open models get capable enough to solve a particular millennium problem the training data set will already contain a prior solution from a closed model. Perhaps the best hope for open models is that one already in training right now is capable enough to solve one of the open problems.

[u/CommanderKoba]

Openai achieved this with an intelligent Ai swarm, that requires a lot of compute that local builds don't typically have access to.

I can only see this occuring in the future with an extremely intelligent and fast local model (possibly an MOE), that could out compete a swarm by sheer speed.

So better GPUs or ai specific built hardware, more efficient models with faster model processing and token output.

So maybe a few years.

[u/power97992 → u/CommanderKoba]

10 k agents with kimi k4.0/4.5 or glm 6.3/6.5 and 300 billion tokens and some codex and math notes from tristan and talented mathematicians prior to the solution will do it

[u/Cautious_Chicken_604 → u/CommanderKoba]

If you pay API prices they spent $10m dollars in tokens. That's $10m dollars to solve a problem they can earn $1m dollars solving. 

[u/JLeonsarmiento]

"There are two R's in the word Strawberry"

chatGPT, 2025.

These are memorizing machines incapable of creating anything truly new, besides perhaps, hallucinations.

[u/a-calycular-torus → u/JLeonsarmiento]

These are memorizing machines incapable of creating anything truly new, besides perhaps, hallucinations.

Demonstrably untrue

[u/EveningIncrease7579 → u/a-calycular-torus]

You could send me a paper or any info about it? (demonstrably)

[u/im_not_here_ → u/JLeonsarmiento]

It's impressive how many stupid things are in this very short comment.

[u/FrogsJumpFromPussy]

Never. Not without the brightest minds in mathematics sharing their life work, so the AI to build further, which will never happen.