Installing Natural Values Transfers Only a Small Fraction of the Known–Unknown Abstention Contrast: Auditing LLM Features for Sufficiency Versus Use
The work proposes an empirical contract that evaluates LLM internal features at values observed on natural inputs through installation, removal, and downstream rescue: installation measures how far a feature suffices for a behavior, while removal and rescue measure how much the model uses it. Applied to published entity-recognition latents, dense known–unknown directions, and a subject–verb agreement feature set, sufficiency and use strengths separate sharply, showing that tracking a concept and steering a behavior do not by themselves show that the model uses a feature.