Detecting and Controlling Sycophancy with Cascading Linear Features
arXiv:2606.26155v1 Announce Type: new Abstract: Interpreting and controlling model behaviors through activation steering methods requires many pairs of contrastive samples that clearly exhibit desired or undesired behavior. These data pairs determine the degree to which interpretability frameworks can reliably detect model features responsible for a behavior, and therefore the ability to steer models toward or away from such behavior. In this work, we present an iterative data generation pipelin
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Computer Science > Artificial Intelligence arXiv:2606.26155 (cs) [Submitted on 23 Jun 2026] Title:Detecting and Controlling Sycophancy with Cascading Linear Features Authors:Maty Bohacek, Rishub Jain, Nicholas Dufour, Thomas Leung, Chris Bregler, Roma Patel View a PDF of the paper titled Detecting and Controlling Sycophancy with Cascading Linear Features, by Maty Bohacek and 5 other authors View PDF Abstract:Interpreting and controlling model behaviors through activation steering methods requires many pairs of contrastive samples that clearly exhibit desired or undesired behavior.
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