LLMs Are Still Toxic, Stuck in the Past, and Bad at Math(www.eyosias.dev)

13 pointsby Eyosias_x52 hours ago4 comments

imglorp2 hours ago
Arithmetic and symbolic math are not the same skills. Anyway it seems to have gotten past its Dyscalculia.
> what is 126347832164398 * 127430123748901234
> Compute large integer multiplication precisely 16,100,519,888,114,640,782,096,553,067,132
- ekjhgkejhgk2 hours ago
  Yes, it's telling that the "LLMs are bad a math" crow seem to thing that being good at math means doing ever bigger multiplications.
  - saberience2 hours ago
    It's a standard trope that anti-ai folk love to draw upon.
    Hey look! I managed to get the AI to fail at some basic thing (after trying 1000s of times), it means AI is failing!!!!!
    It's a total red herring and mispresents the current state of the models completely. It's like meeting a child prodigy in maths and then saying he's actually dumb because can't drive a car yet.
    trescenzian hour ago
    The truth is that they are tools for language manipulation. Advanced mathematics is language manipulation. Multiplication is not. Using it to describe them as having a critical flaw is a red herring. Using it to help people understand what these tools are capable of and how they function is valuable.
    SirFattyan hour ago
    And the fanbois can't admit that AI is flawed.
2 hours ago
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ekjhgkejhgk2 hours ago
LLMs are bad at math? Sorry, I'd rather take the opinion of Terrence Tao than that of some rando on the internet.
https://terrytao.wordpress.com/2026/07/21/a-digestion-of-the...
https://chatgpt.com/share/6a5fdc7a-d6f8-83e8-bbea-8deb42cfed...
saberience2 hours ago
Is there a term for bloggers who try to get attention by participating in this sort of dated, inaccurate "AI doomerism"?
The idea that AIs are "bad at math" because if you send it 100s of basic arithmetic questions, it can get one wrong, is frankly laughable.
It's like me saying Terence Tao is bad at math because I sent him 100 long division problems and he made a mistake in one. Yes, we know models think in tokens, and if you give it a bunch of math problems (and its not using tools like Python to deterministically work on the problems) then of course you cannot guarantee accuracy.
But no one thought tool-less LLM calls were a solution for arithmetic in the first place. So the author is constructing a great big straw-man and attacking it vigorously.
The reality is this, the frontier models are as good as (OR BETTER THAN) the leading mathematicians in the world right now. Leading mathemeticians (like Terence Tao) are using Fable and GPT5.6 as partners in doing research.
As for being stuck in past? Again, weird Anti AI/AI Doomerism because models have fixed weights. So what? They can use tools (and do so very well) if they need up to date data and information.
Again it's like the author wants to paint a picture, based on their own biased beliefs and chooses to represent the current state of AI in an entirely inaccurate fashion.