EFMNB ML from the Window: A Methodology for Controlled Reasoning in Large Language Models

Large language models (LLMs) offer unprecedented generative capabilities, yet they are prone to hallucinations—confidently produced but incorrect or incoherent outputs. This paper introduces the EFMNB ML from the Window methodology, a framework that reframes the training and inference pipeline to enable controlled reasoning in LLMs. By providing an introspective window and bridging semiotics with formal logic, EFMNB ML ensures logically consistent reasoning and helps mitigate hallucinations. The methodology is evaluated on tasks requiring chain-of-thought reasoning, demonstrating improved alignment and reliability.