Confidence Comes From Experience: What XConf Changes About How We Measure LLM Confidence

Last Updated on September 25, 2026 by Editorial Team Author(s): luisacsfreitas Originally published on Towards AI. Confidence Comes From Experience: What XConf Changes About How We Measure LLM Confidence Anyone who has put an LLM in charge of a real decision knows the question that comes right after the demo: when do we trust it? Routing an email to the right team, approving a patch, answering a customer without review. In all of these we need a number that says “this one can go, this one goes to a person”. The trouble is that the number usually comes from the model itself, and the model is not a good judge of itself. On 15 September 2026, a team from the University of Cambridge and Google DeepMind published “Confidence Comes from Experience: Experiential Confidence Estimation from Reasoning to Agents” (Zhang, Zhu, Li, Chen, Kumaran and Collier). The method, called XConf, is simple to state and changes the starting point: confidence is not read from the current answer. It is read from the history of past answers. In this post I explain the idea, what the results actually show, how it compares with the rest of the confidence-estimation toolbox, and what it takes to use it in a real…

≈