arXiv:2604. 27031v2 Announce Type: replace-cross Abstract: In a continual learning setting, we require a model to be plastic enough to learn a new task and stable enough to not disturb previously learned capabilities.
By Karthik Charan Raghunathan, Christian Metzner, Laura Kriener, Melika Payvand
The paper introduces a new task called Multimodal Unsupervised Continual Post-Training (MU‑CPT), which allows multimodal large language models (MLLMs) to continuously learn from streaming unlabeled data. It identifies token‑level visual dependence (VD) as essential for MU‑CPT, using its structural distortion to detect cross‑modal forgetting and its heterogeneity to guide new‑task learning. The proposed Visual Dependence‑Aware (VDA) framework includes Visually Constrained Optimal Transport (VC‑OT) to mitigate forgetting and Visually Modulated Adaptation (VMA) to enhance new‑task plasticity, achieving a balance between stability and adaptability in MU‑CPT.
By Kaichen Li, Zhilin Zhu, Jianhao Huang, Zhengqin Lai, Baochen Xiong, Zibo Shao, Yaguang Song, Linhui Xiao, Xiaoshan Yang, Changsheng Xu
arXiv:2601. 18699v2 Announce Type: replace Abstract: Sequential fine-tuning of Large Language Models (LLMs) adaptation to target tasks often triggers catastrophic forgetting, where the acquisition of novel target skills degrades ancestral capabilities.
By Gustav Olaf Yunus Laitinen-Fredriksson Lundstrom-Imanov
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 p...
arXiv:2511. 18468v2 Announce Type: replace Abstract: Continual Test-Time Adaptation (CTTA) is crucial for deploying models in real-world applications with unseen, evolving target domains.
By Md Akil Raihan Iftee, Mir Sazzat Hossain, Rakibul Hasan Rajib, Tariq Iqbal, Md Mofijul Islam, M Ashraful Amin, Amin Ahsan Ali, AKM Mahbubur Rahman
arXiv:2607. 22556v1 Announce Type: new Abstract: Continual learning (CL) is essential for small language models (SLMs) to adapt to evolving real-world needs in resource-constrained deployments.
By Dong Li, Yanchi Liu, Xujiang Zhao, Wei Cheng, Zhengzhang Chen, Xintao Wu, Zhong Chen, Chen Zhao, Haifeng Chen