Models have identifiable, independently controllable neural coordinates for different aspects of their responses; you can certify and enforce honest reporting by making these coordinates invariant to social pressure while keeping them responsive to real evidence.
Language models often agree with confident users or overstate certainty regardless of actual evidence—a problem called internal incentive-incompatibility. This paper introduces a method to identify and control specific neural coordinates that govern a model's reports, ensuring they resist social pressure while remaining responsive to genuine evidence.