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Larger sliding-window attention can paradoxically slow down the formation of critical retrieval mechanisms in language models, challenging conventional design assumptions.
CoT fine-tuning can slash long-range recall by over 57% in hybrid LLMs, but a simple parameter restoration method can reverse this trend without additional training.
