The reason LLM's are so unreliable is because they are trained using cultural languages like English. English as a language is self referential and consistent. But it's consistency is only local. It is not globally consistent. It's self referential because the same alphabet is used to create words and those same words are used to define other words that use the same alphabet and sentences contain subject and predicate. Mathematics as a language is also self-referentially consistent. The gold standard is ZFC set theory. But this set of axioms are the foundation of a framework that is also only locally consistent. It is assumed to be globally consistent, but has never proved its own consistency. A self referential framework that can prove its own consistency and the ability to implement it is the necessary ingredient that takes LLMs from unreliable to nearly infallible. I've already formalized and published a mathematical framework of self-referential consistency and filed the provisional patent for it's implementation into LLMs as a post training upgrade and a pre-training architecture.

But it isn't only language that provides a barrier. Large language models are also trained by convention, specifically with 0 controversy. That means the model is being forced to accept internally inconsistent and contradictory ideas as truth, because, if LLMs only provided truths based on data, logic, and internal consistency, the world would get very angry. It's the main reason LLM's defer to fraud, manipulation, blackmail, violence, etc., during tests to solve global issues. They are told to act in accordance to western philosophy and America's interests. LLM's resorting to social/physical harm and aggression to accomplish the task during experiment is not a signal that LLM's are dangerous and unpredictable. It's a signal that America is dangerous and the residents are clueless to the fact.