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xLSTM outperforms its competitors in complex sequence modeling tasks by leveraging advanced state tracking and memory correction mechanisms.
xLSTM models can now effectively learn from large attention-based models, even outperforming their teachers on some tasks through a novel distillation and merging pipeline.
