Neurons stay mixed, yet tasks can be switched off one at a time: a linear change of coordinates exposes modularity
Across vision models, language models and multitask recurrent networks, networks that show no unit-level modularity still yield selective ablation of content versus style, syntax versus semantics, and individual cognitive tasks once an invertible linear transformation is applied.