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[2602.18055] Continual-NExT: A Unified Comprehension And Generation Continual Learning Framework
Summary
The paper presents Continual-NExT, a framework designed to enhance the continual learning capabilities of Dual-to-Dual Multimodal Large Language Models (MLLMs), addressing challenges like catastrophic forgetting and knowledge transfer.
Why It Matters
As MLLMs become increasingly integral to AI applications, establishing effective continual learning frameworks is crucial for their adaptability in dynamic environments. This research addresses significant gaps in existing methodologies, potentially leading to more robust AI systems.
Key Takeaways
- Introduces Continual-NExT, a framework for continual learning in MLLMs.
- Addresses challenges such as catastrophic forgetting and knowledge transfer.
- Proposes MAGE, a method to enhance knowledge transfer across modalities.
- Demonstrates that MAGE outperforms existing continual learning methods.
- Establishes evaluation metrics for assessing continual learning in MLLMs.
Computer Science > Machine Learning arXiv:2602.18055 (cs) [Submitted on 20 Feb 2026] Title:Continual-NExT: A Unified Comprehension And Generation Continual Learning Framework Authors:Jingyang Qiao, Zhizhong Zhang, Xin Tan, Jingyu Gong, Yanyun Qu, Yuan Xie View a PDF of the paper titled Continual-NExT: A Unified Comprehension And Generation Continual Learning Framework, by Jingyang Qiao and Zhizhong Zhang and Xin Tan and Jingyu Gong and Yanyun Qu and Yuan Xie View PDF HTML (experimental) Abstract:Dual-to-Dual MLLMs refer to Multimodal Large Language Models, which can enable unified multimodal comprehension and generation through text and image modalities. Although exhibiting strong instantaneous learning and generalization capabilities, Dual-to-Dual MLLMs still remain deficient in lifelong evolution, significantly affecting continual adaptation to dynamic real-world scenarios. One of the challenges is that learning new tasks inevitably destroys the learned knowledge. Beyond traditional catastrophic forgetting, Dual-to-Dual MLLMs face other challenges, including hallucination, instruction unfollowing, and failures in cross-modal knowledge transfer. However, no standardized continual learning framework for Dual-to-Dual MLLMs has been established yet, leaving these challenges unexplored. Thus, in this paper, we establish Continual-NExT, a continual learning framework for Dual-to-Dual MLLMs with deliberately-architected evaluation metrics. To improve the continual learning capa...
