arXiv Machine Learning June 2, 2026 By Yu-Cheng Shi, Zhen-Hao Xie, Jun-Tao Tang, Da-Wei Zhou

ProtoAda: Prototype-Guided Adaptive Adapter Expansion and Geometric Consolidation for Multimodal Continual Instruction Tuning

Read the original on arXiv Machine Learning →

arXiv:2606. 02576v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, but real-world deployment requires them to continually acquire new vision-language capabilities, making Multimodal Continual Instruction Tuning (MCIT) essential.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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