> In theoretical computer science, an algorithm is correct with respect to a specification if it behaves as specified.

"As specified" here being the key phrase. This is defined however you want, and ranges from a person saying "yep, behaves as specified", to a formal proof. Modern language language models are trained under RL for both sides of this spectrum, from "Hey man looks good", to formal theorem proving. See https://arxiv.org/html/2502.08908v1.

So I'll return to my original point: LLMs are not just generating outputs that look plausible, they are generating outputs that satisfy (or at least attempt to satisfy) lots of different objectives across a wide range of requirements. They are explicitly trained to do this.

So while you argue over the semantics of "correctness", the rest of us will be building stuff with LLMs that is actually useful and fun.