All Collections
Model Internals: Research
Research into the controls, representations, and evaluation methods that make model behavior steerable by people.
These works ask what should become visible when a person steers a generative model. Instead of treating the model as a black-box prompt machine, this collection follows the internal representations, control surfaces, and evaluation loops that let a person move through a model's output space by recognition.
