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