New method detects AI uncertainty before it gives wrong answers

Researchers have developed a technique called InnerExpert that identifies when an AI model is internally uncertain as it generates text. The method analyzes signals from "Mixture-of-Experts" models, where specialized sub-networks handle different topics. It detects early warning signs like router uncertainty or disagreement among the model's internal experts. This allows the system to flag potentially unreliable parts of an answer before it is fully delivered. The approach aims to provide a low-cost warning system for AI hallucinations without needing additional, expensive verification models.