Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

18 papers of 6,984Sort Recent · Most cited
  1. 2025
    Learning diverse and adaptive representations for continual learningMingyan Liu, Fei YeNeurocomputing · Shenzhen Institute of Information Technology · University Town of Shenzhen · +2
  2. 2025
    Overcoming catastrophic forgetting in robotic manipulation via knowledge-compositional reinforcement learningWenzhang Liu, Wanyi Yao, Ke‐Ke Yang … Changyin SunNeurocomputing · Anhui University
  3. 2025
    A rate-dependent coreset selector for continual learning on time-varying data distributionsZilin Luo, Zichen Tian, Yaoyao Liu, Qianru SunNeurocomputing · Singapore Management University · University of Illinois Urbana-Champaign
  4. 2025
    Enhancing long-term memory in federated class continual learning with lightweight adaptersPan Wang, Wang Ji, Zhengyi Zhong … Jianguo ChenNeurocomputing · National University of Defense Technology · Sun Yat-sen University
  5. 2025
    RehearMixup: Improving rehearsal-based continual learningYan Zhang, Kaiyuan Qi, Dong Wu … Yilong YinNeurocomputing · Shandong University · Inspur (China)
  6. 2025
    Latent attribute augmented network for few-shot class-incremental learningYongli Hu, J Zhang, Huajie Jiang, Baocai YinNeurocomputing · Beijing University of Technology
  7. 2025
    Continually Learn to Map Visual Concepts to Large Language Models in Resource-constrained EnvironmentsClea Rebillard, Julio Hurtado, Andrii Krutsylo … Vincenzo LomonacoNeurocomputing · Institut Polytechnique de Bordeaux · University of Warwick · +3
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  8. 2025
    Federated Continual Learning: Concepts, Challenges, and SolutionsParisa Hamedi, Roozbeh Razavi‐Far, Ehsan HallajiNeurocomputing · University of New Brunswick · University of Windsor
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  9. 2025
    Adapt & Align: Continual Learning with Generative Models Latent Space AlignmentKamil Rafał Deja, Bartosz Cywiński, Jan Rybarczyk, T. P. TrzcinskiNeurocomputing · Warsaw University of Technology · IDEA of Development Foundation · +1
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  10. 2025
    Continual-MEGA: A Large-scale Benchmark for Generalizable Continual Anomaly DetectionGeonu Lee, Yujeong Oh, Geonhui Jang … Y. YooNeurocomputing
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  11. 2025
    Class Incremental Learning with probability dampening and cascaded gated classifierJary Pomponi, Alessio Devoto, Simone ScardapaneNeurocomputing · Sapienza University of Rome
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  12. 2025
    Enhance the old representations' adaptability dynamically for exemplar-free continual learningKunchi Li, Chaoyue Ding, Jun Wan, Shan YuNeurocomputing · Shandong Institute of Automation · Institute of Automation · +1
  13. 2025
    DCFT: Dependency-aware continual learning fine-tuning for sparse LLMsYanzhe Wang, Yizhen Wang, Baoqun YinNeurocomputing · University of Science and Technology of China
  14. 2025
    Minion Gated Recurrent Unit for Continual LearningAbdullah M. Zyarah, D. KudithipudiNeurocomputing
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  15. 2025
    Graph-based interactive knowledge distillation for social relation continual learningWang Tang, Linbo Qing, Pingyu Wang … Yonghong PengNeurocomputing · Sichuan University · Anglia Ruskin University
  16. 2025
    IDEAL: Interpretable-by-Design ALgorithms for learning from foundation feature spacesPlamen Angelov, Dmitry Kangin, Ziyang ZhangNeurocomputing · Lancaster University
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  17. 2025
    Adaptively forget with crossmodal and textual distillation for class-incremental video captioningHuiyu Xiong, Lanxiao Wang, Heqian Qiu … Hongliang LiNeurocomputing · University of Electronic Science and Technology of China
  18. 2025
    Evolving Ensemble Model based on Hilbert Schmidt Independence Criterion for task-free continual learningFei Ye, Adrian G. BorşNeurocomputing · University of Electronic Science and Technology of China · University of York
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 led 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.