Mechanistic interpretability

Understanding the internal computations of neural networks at the level of individual features and circuits remains extremely challenging. Sparse autoencoders have revealed interpretable features in medium-scale models, but scaling these techniques to frontier models with hundreds of billions of parameters is an open problem. Key questions include whether models represent concepts in superposition, how to extract faithful causal explanations of model behavior, and whether mechanistic understanding can yield practical safety guarantees.