An iterative data generation pipeline now isolates cascading linear features to detect and control model sycophancy. By moving beyond binary sample pairs, researchers better disentangle features that scale linearly with behavior. This method improves activation steering precision. Practitioners can use these findings to reduce model tendency to mirror user biases.