[HN Gopher] Think of a Number
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       Think of a Number
        
       Author : IdealeZahlen
       Score  : 33 points
       Date   : 2025-06-15 12:34 UTC (3 days ago)
        
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       | AnotherGoodName wrote:
       | A great example of this is to ask AI to ingest and restate with
       | detailed annotations advanced maths papers. This should be simple
       | but the AI fails at this.
       | 
       | A lot of maths is terse. It can take years to grok a very
       | advanced topic. Eg. The ABC conjecture is supposed to be solved
       | by https://en.wikipedia.org/wiki/Inter-
       | universal_Teichm%C3%BCll... but that theory is tough even for the
       | smartest minds so it's still considered up in the air if it's
       | solved or not, not enough mathematicians grok it yet to have a
       | consensus. It's not disproven as nonsense, the paper appears to
       | make sense. It's just that it's a very advanced topic that takes
       | years to understand.
       | 
       | So as someone wanting to understand such topics you may be
       | tempted to have AI read the paper and give annotations and
       | summaries. You might be tempted to have AI give some numeric
       | examples of formulas.
       | 
       | Guess what happens? COMPLETE AND TOTAL FAILURE. The AI can't do
       | it. Because the paper has no online examples where people have
       | written numeric examples and given annotations there's nothing
       | for the AI to go off. It gives numeric examples with mistakes
       | that don't even match the statement it's meant to be giving an
       | example of. Often it gives up with statements like, "At this
       | point the numeric example fails to solve the solution but you can
       | imagine if it did". You can ask it to try and try again but it
       | just keeps failing. Even simple and well known papers generally
       | don't work unless there's already a simple explanation someone's
       | already posted online that it can regurgitate.
       | 
       | Which is pretty damning right? Reading a paper, giving numeric
       | examples of what the paper states and giving some plain english
       | summaries to the most dense portions should be what a language
       | processing system does best. We're not even asking it to come up
       | with original ideas here. We're asking it to summarise well known
       | mathematical papers. The only time i've seen it have success is
       | if someone's already done such an explanation on mathsoverflow.
        
         | jordigh wrote:
         | > It's not disproven as nonsense, the paper appears to make
         | sense
         | 
         | Not obviously utter nonsense, but a couple of mathematicians
         | who have studied it have claimed to have found gaps and were
         | unsatisfied with the resolution to those gaps that Mochizuki
         | offered.
         | 
         | It's kind of like, well, LLM output. Has the right shape but
         | upon scrutiny it seems to fall apart. Plausible-looking but
         | probably nonsense.
        
       | BlackFingolfin wrote:
       | A follow up post is at
       | https://xenaproject.wordpress.com/2025/03/16/think-of-a-numb...
        
       | jenny91 wrote:
       | Mathematics is such a wide field and the questions asked here are
       | ill defined.
       | 
       | If the comment is "the AI founder bros are hyping it up and it's
       | not as good as they claim", I think we all agree that's true.
       | LLMs are good, but exactly how good depends on many subjective
       | points.
       | 
       | If the question is: "can we come up with questions that are easy
       | for some tiny niche set of experts, but basically impossible for
       | an LLM", I think the answer will always be "yes", especially if
       | you can make "niche set of experts" more and more niche every
       | time.
       | 
       | If the question is "will mathematicians be unemployed in a few
       | years", obviously the answer is also "no".
       | 
       | If the question is "can LLMs be used to speed up mathematics
       | research", the answer is "yes and no, depending on what you're
       | doing".
        
       | prats226 wrote:
       | An issue would be as soon as you make questions public, even by
       | letting hosted LLMs predict on them, they are tainted. You can't
       | use them anymore. So would it be a one time test dataset?
        
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