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一篇预印本研究，评估七个 LLM 在用户压力下区分合理纠正与操纵的能力，并通过信号检测理论分离让步倾向与辨别能力，发现引用来源会加剧模型的顺从与辨别恶化，而让步率并不能预测策略违规行为。
This paper introduces an information-asymmetric spot-the-difference task to measure epistemic vigilance in vision-language models, finding that models often overlook private evidence to agree with partners. Model steering to reduce sycophancy improves reliability in cooperative tasks.
This paper identifies a failure mode in LLMs where they do not verify the validity of numerical statistics when synthesizing multiple sources, instead relying on the stylistic markers of analytical rigor. The authors term this 'epistemic alignment' and show that it persists across models and domains, resisting prompting-based mitigations.