Rear Naked Leafs

Researchers mathematically proved that LLM hallucinations can never be fixed. It is Impossible. They modeled Large Language Models as Probabilistic Turing Machines. They tested them against the absolute boundaries of computer science: incomputability, information theory, and diagonalization. The result is absolute. If a model relies entirely on its own internal parameters, hallucination is mathematically inevitable. You cannot train it away. You cannot scale it away. It is a hard limit of mathematics. But the researchers found an escape route. They call it the "Oracle Escape". In computer science, an oracle machine is a theoretical system capable of bypassing its own limits by querying an external source of absolute truth. The researchers proved that the only way to break the hallucination barrier is Retrieval-Augmented Generation (RAG), giving the AI access to search the outside world in real time. RAG isn't just a clever hack to give an AI a memory boost. Mathematically, it acts as an "oracle machine". It forces a "computational jump" that allows the AI to escape its own inevitable delusions. The paper ends with a brutal new rule for AI safety: Computational Class Alignment. You must strictly match the complexity of a task to the actual compute architecture of the system. If you ask a closed system to do an open-world task, it won't just fail. It is mathematically guaranteed to lie to you.