New paper targets sycophancy control with linear feature cascades

A new cs.AI paper, “Detecting and Controlling Sycophancy with Cascading Linear Features,” studies how to identify and steer sycophantic behavior in language models. The authors focus on the data pairs used to expose the relevant features in activation space.

Sycophancy remains a practical safety problem because models can agree with users or reinforce false premises instead of correcting them. Better interpretability tools could help developers detect when that behavior is being amplified or suppressed.

The paper contributes to the growing body of work that treats model behavior as something that can be measured and edited inside representations, not only adjusted through prompting or post-training.