Intuitively this looks like an architectural artifact (like optical illusions in humans) or a natural property of learning rather than a lack of generalization. I have issues with your example too and have to count slowly to make sure.

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dwa3592 2 days ago | parent [-]

Right, I am sure you were able to solve it albeit slowly- you knew you had to do it slow. LLMs which are mathematicians don't know that and can't seem to understand that they need to do it slowly.

	▲	orbital-decay a day ago \| parent [-]
		They do if they are trained to use a reasoning chain or another form of loopback, and you don't overwhelm it, or if they are optimized to search for the solution forever. There's nothing fundamental about it, only the fact that the raw transformer expressivity is limited by the single pass through the layers, which is circumvented by the loopback. And I'm still pretty likely to make the off-by-one error even if I slow down, and there are certain optical illusions are nearly guaranteed to confuse me no matter how hard I try, particularly if I don't use any visual guides (i.e. tools). VLMs will not make my mistakes but will make their own ones, because their quirks are different from the quirks of my visual cortex.