Detecting and Controlling Sycophancy with Cascading Linear Features
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 pipeline that isolates cascading linear features responsible for a behavior. Specifically, we show how moving beyond simple binary pairs of samples, and instead isolating samples that show degrees of features that scale linearly with behavior, allows for better disentanglement of features.
- ▪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 pipeline that isolates cascading linear features responsible for a behavior.
- ▪Specifically, we show how moving beyond simple binary pairs of samples, and instead isolating samples that show degrees of features that scale linearly with behavior, allows for better disentanglement of features.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2606.26155 |
| Publication time | Fri, 26 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-26T05:20:40.881Z |
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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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