Mechanistic interpretability researchers bring causality theory to LLM analysis

A group of researchers is applying causal inference methods to understand how large language models actually reason internally, moving beyond black-box behavior observation. For indie AI builders, better interpretability tools could mean more predictable model behavior and easier debugging when things go wrong.