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 for activation steering. Practitioners can now more precisely steer LLMs away from echoing user biases.