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.

18 papers of 11,817Sort Recent · Most cited
  1. 2026PDF ↗
  2. 2026PDF ↗
  3. 2026
    Adaptive Momentum Mixture-of-Experts for Continual Visual Question AnsweringTianyu Huai, Jie Zhou, Qin Chen … Liang HeIEEE TCSVT
  4. 2026
    AutoSkill: Experience-Driven Lifelong Learning via Skill Self-EvolutionYu-Tao Yang, Junsong Li, Qianjun Pan … Liang HearXiv
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  5. 2025
  6. 2025PDF ↗
  7. 2025PDF ↗
  8. 2025PDF ↗
  9. 2025
    Wav2DF-TSL: Two-stage Learning with Efficient Pre-training and Hierarchical Experts Fusion for Robust Audio Deepfake DetectionYunqi Hao, Yihao Chen, Minqiang Xu … Lin LiuIEEE International Joint Conference on Neural Network
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  10. 2026
    Reinforced Interactive Continual Learning via Real-time Noisy Human FeedbackYu-Tao Yang, Jie Zhou, Junsong Li … Liang HeExpert Systems with Applications
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  11. 2025PDF ↗
  12. 2025PDF ↗
  13. 2024
    Semi-Process Noise Distillation for Continual Mixture-of-Experts Diffusion ModelsJiaxiang Cheng, Yiming Liu, Bo Long … Tian WangACM Cloud and Autonomic Computing Conference
  14. 2024
    Recent Advances of Foundation Language Models-based Continual Learning: A SurveyYu-Tao Yang, Jie Zhou, Xuanwen Ding … Yuan XieACM Computing Surveys
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  15. 2024
    A safety realignment framework via subspace-oriented model fusion for large language modelsXin Yi, Shunfan Zheng, Linlin Wang … Liang HeKnowledge-Based Systems
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  16. 2024PDF ↗
  17. 2023
    Uncertainty-Aware Few-Shot Class-Incremental LearningJian-Cai Zhu, Jiabao Zhao, Jiayi Zhou … Zhi ZhangICASSP
  18. 2023
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.