>The results of this paper should not be interpreted as suggesting that AI can consistently solve research-level mathematics questions. In fact, our anecdotal experience is the opposite: success cases are rare, and an apt intuition for autonomous capabilities (and limitations) may currently be important for finding such cases. The papers (ACGKMP26; Feng26; LeeSeo26) grew out of spontaneous positive outcomes in a wider benchmarking effort on research-level problems; for most of these problems, no autonomous progress was made.

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noosphr 15 minutes ago | parent | next [-]

I've been at this longer than just about anyone.

After three major generations of models the "intuition" I've build isn't about what AI can do, but about what a specific model family can do.

No one cares what the gotchas in gpt3 are because it's a stupid model. In two years no one will care what they were for gpt5 or Claude 4 for the same reason.

We currently have the option of wasting months of our lives to get good at a specific model, or burn millions to try and get those models to do things by themselves.

Neither option is viable long term.

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getnormality an hour ago | parent | prev | next [-]

The ridiculous resources being thrown at this, and the ability through RLVR to throw gigatons of spaghetti at the wall to see what sticks, should make it very clear just how incredibly inefficient frontier AI reasoning is - however spectacular it may be that it can reason at this level at all.

	▲	asdff 10 minutes ago \| parent [-]
		Long term though, AI will win out. The thing is that you can improve capability. You can make the context window bigger. You can throw more compute at it. Improve efficiency of chips. Throw more power at it. And indeed, that has worked so far to turn the gpts of 2017 into the gpts of 2026 that can actually do stuff. Meanwhile, human thoughtpower cannot really be improved. Once the tipping point is reached where computers exceed humans, humans will never be able to catch up by definition. Humans can also only maintain so much contextual information and scope. They can only learn so much in the time scale they have to get up to speed. They can only do so much within the timescale of their own mental peak before they fall off and go senile or die. While these limits are bound by evolution, they change on the orders of thousands of generations, and require strong selection for these changes at that. The turtle has marched far already, but the hare in the speeding car they continually improve is not far behind. Efficiency doesn't matter. What is inefficient now will be trivial to parallelize and scale in the future as its always been in the history of compute. We'd have to engage in something like the Bene Gesserit breeding program if we are to have human thoughtpower be competitive against compute in the future.

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thereitgoes456 34 minutes ago | parent | prev [-]

I credit them for acknowledging their limitations and not actively trying to be misleading. Unlike a certain other company in the space.