In this paper, we explore a novel task of Multimodal Unsupervised Continual Post-Training (MU-CPT), enabling deployed MLLMs to continually evolve from streaming unlabeled data. Existing unsupervised post-training methods for MLLMs typically optimize target tokens uniformly, overlooking their heterogeneous visual depend...
Kaichen Li, Zhilin Zhu, Jian-Hao Huang et al.· 0 citations
Cross-modal joint unlearning (CMJU) is proposed, a unified framework for balanced cross-modal forgetting that achieves better cross-modal forgetting balance than strong baselines, while maintaining competitive retain-set performance.
AgentPatch is proposed, a training-free coarse-to-fine repair framework that improves diverse merged backbones, alleviates weak-task degradation, and better balances weak-task recovery with the preservation of complementary search and agentic visual processing capabilities.
Zibo Shao, Baochen Xiong, Chengdong Xu et al.· 0 citations
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