zlacker

>>amrrs+(OP)
I think one of the reason we are confused about what LLMs can do is because they use language. And we look at the "reasoning traces" and the tokens there look human, but what is actually happening is very alien to us, as shown by "Biology of Large Language Models"[1] and "Safety Alignment Should Be Made More Than Just a Few Tokens Deep"[2]

I am struggling a lot to see what the tech can and can not do, particularly designing systems with them, and how to build systems where the whole is bigger than the sum of its parts. And I think this is because I am constantly confused by their capabilities, despite understanding their machinery and how they work, their use of language just seems like magic. I even wrote https://punkx.org/jackdoe/language.html just to remind myself how to think about it.

I think this kind of research is amazing and we have to spend tremendous more effort into understanding how to use the tokens and how to build with them.

[1]: https://transformer-circuits.pub/2025/attribution-graphs/bio...

[2]: https://arxiv.org/pdf/2406.05946

>>jackdo+vm1
> we are confused about what LLMs can do is because they use language.

But they can also do math, logic, music notation, write code, LaTeX, SVG, etc.