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Personalized modality routing in TimeRoute leads to up to 9.8% better recommendation performance by adapting to temporal shifts in user preferences.
Increasing non-thinking supervision can actually hinder the accuracy of thinking mode in large language models, revealing a critical trade-off in training dynamics.
