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Introduces CANDOR, a chance-calibrated discordance measure for frozen foundation encoders that corrects for prevalence bias, revealing that no encoder is completely blind but all are weak for certain fine-grained findings.
This paper proposes Adversarial Concept Search, a method that uses the representational geometry of large language models to predict compositional failures without evaluating specific inputs. The approach identifies high-risk scenarios by measuring interference between salient features.