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
Evidence-driven Online Preference Adaptation (EOPA), which grounds a user's interaction-timing preferences in measurable contextual evidence through two evidence carriers: temporal preference anchors and evidence-bearing activity prototypes, is proposed.
Yufeng Wang, Wei Zhang, Zhi-Quan Wen et al.· 1 citation
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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