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This paper proposes a novel framework combining Large Language Models with Multiple-Instance Learning to detect cognitive distortions in mental health texts by decomposing utterances into Emotion, Logic, and Behavior components and using multi-view gated attention for classification. The approach demonstrates improved performance on Korean and English datasets, particularly for distortions with high interpretive ambiguity.