Linear Representation Hypothesis
A central hypothesis in mechanistic interpretability research, positing that AI models encode high-level semantic concepts as linear directions in activation space. It forms the theoretical foundation for activation steering techniques.
Timeline (last 90 days)
The Linear Representation Hypothesis (LRH) assumes that models encode high-level concepts as linear directions in the activation space.
Linear steering relies on the linear representation hypothesis; when the concept distribution itself is curved, shifting activations along a fixed direction deviates from the true concept manifold, leading to control distortion or unnatural intermediate states.
All Facts (2)
The Linear Representation Hypothesis (LRH) assumes that models encode high-level concepts as linear directions in the activation space.
50%UnverifiedLinear steering relies on the linear representation hypothesis; when the concept distribution itself is curved, shifting activations along a fixed direction deviates from the true concept manifold, leading to control distortion or unnatural intermediate states.
50%