This article discusses a new approach to making neural networks more interpretable by training them to use simpler, sparse circuits. These models are designed to isolate specific behaviors, allowing researchers to better understand how they arrive at their decisions. The work aims to bridge the gap between complex AI behaviors and human comprehension.