Related work

The foundational work on continual learning, 1989 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5,456 papers · showing 501–550Sort Recent · Most cited
  1. 2026
    Robust Personalized Federated Continual Learning via Explainable Multi-Granularity Prompt.Hao Yu, Xiaomin Yang, Boyang Fan … Qiang YangTPAMI · Artificial Intelligence in Medicine (Canada) · Shenzhen Weiguang Biological Products (China) · +3
  2. 2026
    Same Methods, Different Rankings: Trainable Depth as an Evaluation Variable in Continual LearningPaul-Tiberiu Iordache, Elena Burceanu, Mihai DascăluIEEE Access · University of Science and Technology · Universitatea Națională de Știință și Tehnologie Politehnica București
  3. 2026
    Soft Orthogonal Low-Rank Adaptation for Knowledge Sharing in Large Language Model Continual LearningYitong Wang, Xue Han, Wenchun Gao … Junlan FengACL · Jiuquan Iron & Steel (China) · China Mobile (China) · +1
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  4. 2026
    The Dominance of Text Space: Unveiling the Asymmetric Nature of Cross-Modal Alignment in Large Language ModelsLinqing Chen, Hanmeng Zhong, Wentao Wu, Peng ZhouACL · Suzhou Research Institute
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  5. 2026
    Topology-Evolving Semantic Adaptation for Few-Shot Class-Incremental Action RecognitionXingyu Zhu, Binqian Xu, Jiachao Zhang … Xiangbo ShuIEEE Trans. Multimedia · Nanjing University of Science and Technology · Nanjing Institute of Technology · +1
  6. 2026
    Type-Balanced Contextual Learning for Incremental Named Entity RecognitionDuzhen Zhang, Yahan Yu, Xiuyi Chen … Dong YuIEEE TAI · Mohamed bin Zayed University of Artificial Intelligence · Kyoto University · +2
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  7. 2026
    VFA: Empowering Multilingual MLLMs via Vision-Free AdaptationYixia Li, Yaqing Shi, Zhiwen Ruan … Furu WeiACL · Southern University of Science and Technology · Shanghai University of Finance and Economics · +3
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About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. It lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.