Language models influence which news we see, which job applications get a second look, and what a chatbot answers when asked for medical advice. Our paper Cross-Layer Discrete Concept Discovery for Interpreting Language Models introduces a method for reading the inner concepts of language models to better understand them. Researchers then have to manually combine them or set arbitrary cutoffs, making the model’s reasoning hard to interpret. The word “entertainment” maps to a positive concept in an enthusiastic review but a negative one in a harsh review. CLVQ-VAE is a step toward making a model’s reasoning legible after the fact, so the concepts behind its answers can be better inspected, checked, and audited.