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Run-Ze Liu

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#artificial intelligence Preprint Aug 2026

CoRe-MoE: Compact Reusable MoE for Continual Multimodal Instruction Tuning

CoRe-MoE is proposed, a Compact Reusable MoE framework for parameter-efficient continual multimodal instruction tuning that improves final average performance over the strongest competing baseline by up to 5.90 points, while using less than 1% of the trainable parameters required by sequential LoRA for later tasks.

Run-Ze Liu, Naibin Gu, Ming-Xu Ai et al. · 0 citations