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This paper investigates whether neural network probes that predict correctness of language model outputs actually capture objective correctness or the model's own self-judgement, using conflict cases where the two disagree. The authors find that transferable directions predominantly preserve self-judgement polarity, challenging the interpretation of correctness readouts.