Continual learning enables AI systems to acquire new knowledge while retaining previously learned information. While traditional unimodal methods have made progress, the rise of Multimodal Large Language Models (MLLMs) brings new challenges in Multimodal Continual Learning (MCL), where models are expected to address both catastrophic forgetting and cross-modal coordination. To advance research in this area, we present MCITlib, a comprehensive library for Multimodal Continual Instruction Tuning. MCITlib currently implements 8 representative algorithms and conducts evaluations on 3 benchmarks under 2 backbone models. The library will be continuously updated to support future developments in MCL. The codebase is released at https://github.com/Ghy0501/MCITlib.

翻译：持续学习使人工智能系统能够在获取新知识的同时保留先前学习的信息。尽管传统的单模态方法已取得进展，但多模态大语言模型（MLLMs）的兴起为多模态持续学习（MCL）带来了新的挑战，该领域要求模型同时应对灾难性遗忘和跨模态协调问题。为推进该领域的研究，我们提出了MCITlib——一个用于多模态持续指令调优的综合性开源库。MCITlib目前实现了8种代表性算法，并在2种骨干模型上对3个基准数据集进行了系统评估。该库将持续更新以支持多模态持续学习的未来发展。代码库发布于 https://github.com/Ghy0501/MCITlib。