Interpretability and transparency remain persistent concerns. As models grow in size and complexity, their decision-making processes become increasingly opaque. The field of mechanistic interpretability seeks to reverse-engineer the internal computations of neural networks, identifying circuits and attention patterns that correspond to specific behaviors. Early successes in vision models and small language models have revealed interpretable features such as edge detectors, color contrast analyzers, and even higher-level concepts like grammatical number or factual knowledge. Scaling these techniques to frontier models, where billions of parameters interact in subtle ways, is a formidable challenge. Yet progress is essential if we are to audit systems for safety, bias, and alignment with human values.