Large language models (LLMs) are increasingly used in critical applications, but how do they know when they're right? New research unveils the inner workings of these AI giants, revealing dedicated "confidence regulation neurons" that control how sure the model is about its predictions. These neurons act like tiny judges, constantly evaluating and adjusting the model's certainty. This discovery sheds light on two fascinating mechanisms: entropy neurons, which have recently been discovered, and a new class of "token frequency neurons." Entropy neurons act as careful editors, subtly tweaking the model's output to avoid overconfidence. They achieve this by targeting an unused portion of the model's internal representation—an effective "null space"—allowing them to influence the model's confidence without altering its actual prediction. Meanwhile, token frequency neurons have a different role. They compare the model's predictions to how often words typically appear in language. If a model strays too far from common word usage, these neurons nudge it back towards familiarity, especially when the model is less certain. This is a surprising twist, as it shows LLMs naturally gravitate towards frequent words, even more so than natural language itself. Researchers explored these neurons in action with a "case study" of induction, where the model encounters repeated text sequences. In these situations, entropy neurons act as a safety net, preventing the model from becoming too confident in its predictions. This careful balancing act is crucial, as it allows the model to adapt to unexpected variations in the text. This research opens a window into the intricate machinery of LLMs, revealing a complex interplay of checks and balances that help them learn, adapt, and, crucially, manage their confidence. Understanding these mechanisms isn’t just an academic exercise—it’s a vital step towards building more reliable and trustworthy AI systems.