Intuitions for Tranformer Circuits

This post shares intuitions gained from studying transformer circuits through the 'A Mathematical Framework for Transformer Circuits' paper and ARENA's mechanistic interpretability course. The author motivates this work by connecting mechanistic interpretability—reverse-engineering ML model internals—to the broader AI alignment goal of understanding and controlling large language models. The technical focus is on attention-only transformers (no MLPs or layernorms), building a mental model for how the residual stream operates.