At least with AI you can ask it what it understands about the topic so you know what you can assume.
replies(2):
Think about it, a LLM is an autocompleter. It will give you the most probable next word each time. It doesn't mean it doesn't understand high level concepts, but in the end, it just writes stuff that is similar to its training dataset.
For example, ask it to multiply two numbers. If the number are small enough, you will get the right answer. Now ask it to explain how it did it, it will probably tell you the process as commonly taught in school, but it not actually how it did it. What it did is much weirder for us humans, and the only way to see how it actually works is to look at the internals of the neural network. The LLM can't describe it, it doesn't see inside itself, however, it has many textbooks in its training dataset, so it will grab an answer from these textbooks because that's how people answer.
Seeing how it correctly describes the multiplication process and how it can multiply small number correctly, you would assume it can also multiply large numbers (as we do), but nope, it can't, unless it has access to a separate math module, traditionally made (i.e. not a neural net).
EDIT: I see now that you were referring to the answers it uses to justify the result, not the underlying computations. Sorry! You can disregard the actual comment. Leaving for completeness.
ORIGINAL COMMENT:
That's not how it works. Addition in LLMs is believed to function through different mechanisms depending on model size and architecture, but the single consistent finding across different models is that they generalize beyond the training data for at least those simple arithmetic operations.
For example: "Language models use trigonometry to do addition" https://arxiv.org/abs/2502.00873
For a different "emergent" algorithm, see Anthropic's observations: https://transformer-circuits.pub/2025/attribution-graphs/met...