How to adapt a pre-trained model continuously for sequential tasks with different prediction class labels and domains and finally learn a generalizable model across diverse tasks is a long-lasting challenge. Continual learning (CL) has emerged as a promising approach to leverage pre-trained models (e.g., Transformers) for sequential tasks. While many existing CL methods incrementally store additional learned structures, such as Low-Rank Adaptation (LoRA) adapters or prompts and sometimes even preserve features from previous samples to maintain performance. This leads to unsustainable parameter growth and escalating storage costs as the number of tasks increases. Moreover, current approaches often lack task similarity awareness, which further hinders the models ability to effectively adapt to new tasks without interfering with previously acquired knowledge. To address these challenges, we propose FM-LoRA, a novel and efficient low-rank adaptation method that integrates both a dynamic rank selector (DRS) and dynamic meta-prompting (DMP). This framework allocates model capacity more effectively across tasks by leveraging a shared low-rank subspace critical for preserving knowledge, thereby avoiding continual parameter expansion. Extensive experiments on various CL benchmarks, including ImageNet-R, CIFAR100, and CUB200 for class-incremental learning (CIL), and DomainNet for domain-incremental learning (DIL), with Transformers backbone demonstrate that FM-LoRA effectively mitigates catastrophic forgetting while delivering robust performance across a diverse range of tasks and domains.
Dual-LoRA and Quality-Enhanced Pseudo Replay for Multimodal Continual Food Learning
College of Computer Science and Artificial Intelligence, Fudan University, China（复旦大学计算机科学与人工智能学院）；Singapore Management University, Singapore（新加坡国立管理学院）
LoRA Subtraction for Drift-Resistant Space in Exemplar-Free Continual Learning
School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）；Key Laboratory of Intelligent Assessment Technology for Sustainable Tourism, Ministry of Culture and Tourism, Sun Yat-sen University（文化和旅游部可持续旅游智能评估技术重点实验室）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China（教育部机器智能与先进计算重点实验室）
C-LoRA: Continual Low-Rank Adaptation for Pre-trained Visual Models
Institute of Intelligent Information Processing, Shanxi University（山西大学智能信息处理研究所）；Alliance Manchester Business School, The University of Manchester（曼彻斯特大学联盟曼彻斯特商学院）
Hierarchical-Task-Aware Multi-modal Mixture of Incremental LoRA Experts for Embodied Continual Learning
Ping An Technology (Shenzhen) Co., Ltd.（平安科技（深圳）有限公司）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Tsinghua University（清华大学）；Huazhong University of Science and Technology（华中科技大学）
Task-Driven Subspace Decomposition for Knowledge Sharing and Isolation in LoRA-based Continual Learning
Department of XXX, University of YYY, Location, Country（XXX部门，YYY大学，地点，国家）；School of ZZZ, Institute of WWW, Location, Country（ZZZ学院，WWW研究所，地点，国家）；State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China（信息服务网络国家重点实验室，电信工程学院，西安电子科技大学，西安，中国）；School of Electronic Engineering, Xidian University, Xi'an, China（电子工程学院，西安电子科技大学，西安，中国）
Practical Continual Forgetting for Pre-trained Vision Models
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（人工智能与机器人中心，香港科学创新研究院，中国科学院）；SenseTime Research（商汤科技研究院）
Energy-Structured Low-Rank Adaptation for Continual Learning
School of Computer Science and Engineering, Southeast University, Nanjing, China（东南大学计算机科学与工程学院，南京，中国）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（新一代人工智能技术及其交叉应用重点实验室（东南大学），教育部，中国）；Huawei Technologies, Shenzhen, China（华为技术有限公司，深圳，中国）
Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning
Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, China（计算机科学与技术系，BNRist，清华大学，北京，中国）；Alibaba Group, Hangzhou, China（阿里巴巴集团，杭州，中国）
Janus-LoRA: A Balanced Low-Rank Adaptation for Continual Learning
School of Computer Science and Technology, Tongji University, Shanghai, China（同济大学计算机科学与技术学院，上海，中国）；School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China（电子科技大学计算机科学与工程学院，成都，中国）；Shanghai Innovation Institute, Shanghai, China（上海创新研究院，上海，中国）；Independent Researcher（独立研究者）
Few-Shot Continual Learning for 3D Brain MRI with Frozen Foundation Models
Independent Researcher, USA（美国独立研究者）；Indiana University at Bloomington, United States（印第安纳大学布卢明顿分校）；Independent Researcher, Taiwan（台湾独立研究者）；National Taiwan University, Taiwan（台湾国立大学）；Boise State University, United States（博伊西州立大学）；National Yang Ming Chiao Tung University, Taiwan（台湾阳明交通大学）
Dynamic Cross-Modal Prompt Generation for Multimodal Continual Instruction Tuning
School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）
PASs-MoE: Mitigating Misaligned Co-drift among Router and Experts via Pathway Activation Subspaces for Continual Learning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；National University of Singapore（新加坡国立大学）；Southeast University, Nanjing, China（南京东南大学）；Wuhan AI Research, Wuhan, China（武汉人工智能研究院）
Latent-LoRA: Compact Latent-Space Adapters with Gradient-Free Routing for Continual Learning
College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）；Department of Electrical and Computer Engineering, The University of Texas at Austin（德克萨斯大学奥斯汀分校电气与计算机工程系）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；The GAME School, Arizona State University（亚利桑那州立大学GAME学院）
GEM-Style Constraints for PEFT with Dual Gradient Projection in LoRA
Department of Computer Science University of North Carolina Greensboro（计算机科学系北卡罗来纳州立大学）；Department of Computer Science North Carolina State University（计算机科学系北卡罗来纳州立大学）
Continual-NExT: A Unified Comprehension And Generation Continual Learning Framework
East China Normal University（华东师范大学）；Shanghai Innovation Institute（上海创新研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Xiamen University（厦门大学）
Is Our Benchmark Enough? An Analysis of Continual Learning for MLLMs
School of Computer Science and Statistics, Trinity College Dublin（都柏林圣三一学院计算机科学与统计学院）；Department of Mathematics and Computer Science, Eindhoven University of Technology（埃因霍温理工大学数学与计算机科学系）
