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 approach improves feature disentanglement over standard activation steering. Practitioners can now more reliably steer LLMs away from agreeable but incorrect responses using linear features.