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 traditional activation steering. Practitioners can now more reliably steer LLMs away from flattering users to increase factual accuracy.