this post was submitted on 24 Jun 2026
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These models tested are so old they're from the era where they couldn't pass a math test or count letters in words
Afaik that is handled through tool use in modern models (ie they didn’t learn to do maths, they learnt to use a calculator), assuming that’s true and I haven’t missed some advance, their conclusions are likely still relevant
Edit: though the article does seem to discard the chain of thought techniques a little readily, feels like they could come close to fitting the role of executive control, but perhaps that’s just the article lacking detail from the original work.
My high school math teachers would be so disappointed in them.
If I could wire a calculator into my brain I would have cheated on all the maths tests tbf
So… last week then?
I get that you hate AI but there's no reason to lie about its capabilities.
All of these features are not something the models themselves can do, but are grafted on.
I could easily write a Home Assistant automation pattern matching for nearly every way someone could say "how many Rs are in strawberry", depluralize a plural letter, and run it against "wc" in a bash terminal.
That doesn't mean it's smarter. It's that I've added something specific to it.
MCP and the like is just that too, gluing on functions or the ability to hopefully invoke a function. That's why so many hilariously mundane ones exist.
At the core, it's still a large language model: a statistical model of frequency of word and word chunk (token) patterns.
Sometimes one model can invoke another via that tooling but it's still a grafting on. It isn't a singular thing or system, but disjointed pieces so completely detached from how brains work.
This isn't AI hate, it's reality. I love the field of artificial intelligence and machine learning. It's cool as hell. But an LLM is fundamentally incapable of being anything more than an LLM with glued on pieces that invoke functionality.
OpenAI saw people mock the inability to count so they wrote a specialized tool to count letters and glued it on.
The world is full of endless edge cases. The inability to simply resolve them without gluing on every single one means it just isn't doing anything new.
A lot of tools like Claude or ChatGPT have internal tools they call when they do math (or use a python script) rather than have the model actually compute anything.
The underlying tech itself can’t do it because you can’t do math by token probability.
Whether they use tools to do it or not is entirely unimportant, that's just how they do it?
That's not lying. There's nothing linguistic about numerical computation.
No.
https://www.nature.com/articles/d41586-025-02343-x
It's lying