Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

21 papers of 11,817Sort Recent · Most cited
  1. 2026PDF ↗
  2. 2025
    Towards understanding memory buffer based continual learningGuodong Zheng, Peng Wang, Tao Sun, Li ShenNeural Networks
  3. 2025
  4. 2025
  5. 2025
  6. 2025
  7. 2024
    Continual Task Learning through Adaptive Policy Self-CompositionShengchao Hu, Yu-Hang Zhou, Ziqing Fan … Dacheng TaoarXiv
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  8. 2024PDF ↗
  9. 2024
    Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications, and OpportunitiesEnneng Yang, Li Shen, Gui-Bing Guo … Dacheng TaoACM Computing Surveys
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  10. 2024
    A Unified and General Framework for Continual LearningZhenyi Wang, Yan Li, Li Shen, Heng HuangICLR
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  11. 2023
    Task-Distributionally Robust Data-Free Meta-LearningZixuan Hu, Yongxian Wei, Li Shen … D. TaoTPAMI
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  12. 2023
    Data Augmented Flatness-aware Gradient Projection for Continual LearningEnneng Yang, Li Shen, Zhenyi Wang … Xingwei WangICCV
  13. 2023
  14. 2023
    Continual Learning From a Stream of APIsEnneng Yang, Zhenyi Wang, Li Shen … Dacheng TaoTPAMI
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  15. 2023PDF ↗
  16. 2023
  17. 2023
    An Efficient Dataset Condensation Plugin and Its Application to Continual LearningEnneng Yang, Li Shen, Zhenyi Wang … Gui-Bing GuoNeurIPS
  18. 2022
    Streaming Radiance Fields for 3D Video SynthesisLingzhi Li, Zhen Shen, Zhongshu Wang … Ping TanNeurIPS
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  19. 2022PDF ↗
  20. 2022PDF ↗
  21. 2022
    Learning to Learn and Remember Super Long Multi-Domain Task SequenceZhenyi Wang, Li Shen, Tiehang Duan … Mingchen GaoCVPR · University at Buffalo, State University of New York · Jingdong (China) · +1
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 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.