As a scientific researcher, I use LLMs all the time. Mainly in place of Google search, to help write code and maybe summarize a paper here and there etc. But I definitely don't use it for the actual scientific process e.g. hypothesis generation or planning analyses etc. It tends to produce a lot of vague bullshit for this kind of thing; while not wrong not entirely useful either. I have a few colleagues that do though with more success. Although I think the success comes from articulating their problem in detail (by actually writing it out to the LLM) which I think is the source of "inspiration" rather than the resulting content from the LLM.

Are ML researchers "scientific researchers"? Then yes, they are using LLMs. AlphaEvolve is one such example.

Are mathematicians? Then yes, for example Terrence Tao is using LLMs. AlphaProve / geometry are also examples of this, using LLMs to generate lean proofs, translate from NL to lean, and so on.

And for the general "researchers", they use code generation to speed up stuff. Many scientists can code, but aren't "coders" in the professional sense. So they can use the advances in code generation to speed up their own research efforts.