An iterative data generation pipeline now isolates cascading linear features to detect and control model sycophancy. Researchers moved beyond binary sample pairs to identify features that scale linearly with behavior. This method improves feature disentanglement over standard activation steering. ArXiv researchers demonstrate that this precision allows for more reliable steering away from undesired model behaviors.