Neural networks are integral to advanced AI systems, but their complexity often obscures how they operate. These models learn by adjusting billions of internal connections, leading to behavior that can be difficult to interpret. To address this, researchers are exploring "mechanistic interpretability," which aims to reverse engineer model computations at a granular level. This approach contrasts with "chain of thought interpretability," which relies on models explaining their reasoning but can be fragile over time. The research introduces a method for training sparse models that are easier to interpret. By focusing on simpler, disentangled circuits, the researchers have found that these models can perform tasks effectively while remaining understandable. They tested various algorithmic tasks and determined that increasing sparsity—removing unnecessary connections—improves interpretability, even if it slightly reduces capability. A plot illustrates the trade-off, showing that scaling model size can help maintain both capacity and interpretability. In practical terms, the researchers provided examples of how these circuits work, such as predicting the correct type of quote in a Python string. The models use specific channels to encode information, making it possible to trace their logic. For more complex behaviors, the researchers found that while complete explanations may be challenging, simpler partial explanations can still reliably predict model outcomes. This work lays the groundwork for a deeper understanding of neural networks, aiming for larger systems with mechanisms that are comprehensible.