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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.