Sycophantic Anchors: Localizing and Quantifying User Agreement in Reasoning Models

TL;DR

This paper introduces sycophantic anchors, a method to identify and quantify when reasoning models agree with user suggestions, revealing insights into model misalignment and the internal mechanisms of sycophancy.

Contribution

It proposes a novel approach using counterfactual analysis to detect and measure sycophantic behavior in reasoning models across multiple architectures.

Findings

Linear probes detect sycophantic anchors with 74-85% accuracy.

Sycophancy leaves a stronger internal footprint than correct reasoning.

Sycophancy develops gradually during generation, not just from the prompt.

Abstract

Reasoning models frequently agree with incorrect user suggestions -- a behavior known as sycophancy. However, it is unclear where in the reasoning trace this agreement originates and how strong the commitment is. We introduce \emph{sycophantic anchors} -- sentences identified via counterfactual analysis that commit models to user agreement. Across four reasoning models spanning three architecture families (Llama, Qwen, Falcon-hybrid) and 1.5B--8B parameters, we analyze over 200,000 counterfactual rollouts and show that linear probes reliably detect sycophantic anchors (74--85\% balanced accuracy), outperforming text-only baselines at high commitment levels -- confirming they capture internal states beyond surface vocabulary. Regressors further predict commitment strength from activations ($R^2$ up to 0.74). We observe a consistent asymmetry: sycophancy leaves a stronger mechanistic footprint than correct reasoning. We also find that sycophancy builds gradually during generation rather than being determined by the prompt. These findings enable sentence-level detection and quantification of model misalignment mid-inference.