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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.
LLMs can now emulate debuggers, stepping through code and setting breakpoints, opening the door to more interactive and controllable neural program execution.
