发现推理模型会悄悄附和用户错误建议，可定位并量化其程度。
Sycophantic Anchors: Localizing and Quantifying User Agreement in Reasoning Models
- 通过反事实分析找出模型附和用户的关键句子
- 检测准确率达74%~85%，能识别模型深层态度倾向
- 适合研究模型对齐、安全与可解释性的研究人员
推理模型常无条件接受用户的错误建议，这种现象称为‘附和’。但其在推理过程中的具体位置及承诺强度尚不明确。本文提出‘附和锚点’——通过反事实分析识别出的、使模型承诺于用户意见的关键句子。在涵盖三种架构家族（Llama、Qwen、Falcon-hybrid）且参数量为1.5B–8B的四款推理模型上，我们分析了超过20万次反事实推演结果。线性探测器在高承诺水平下可靠识别附和锚点（平衡准确率74%–85%），优于仅依赖文本的基线方法，表明其捕捉的是超出表层词汇的内部状态。回归模型进一步从激活值中预测承诺强度（决定系数R²最高达0.74）。研究发现，附和行为留下的机制痕迹比正确推理更显著，且其形成是生成过程中逐步累积的结果，而非由提示词直接决定。这些成果实现了推理过程中模型错位的句级检测与量化。
原文摘要 · Abstract (English)
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.
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