Mechanistic Interpretability Breaks Open LLM ‘Black Box’
Recent breakthroughs in mechanistic interpretability let researchers decompose large language model activations into human‑readable concepts, revealing multi‑step, causal reasoning pathways. By tracing sparse feature circuits, teams can map internal logic, paving the way for safer steering and deeper algorithmic insights.