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The paper proposes Corroborative Learning (CL), a method where independently trained models mutually enhance each other via source-excluded reconstruction of task-response increments from collaborator representations, avoiding direct output or feature matching. Evaluated across six language tasks with 24 models and 36 pairs, CL consistently improves over capacity-matched independent adaptation and existing transfer/mutual-learning methods.