Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

4 papers of 8,653Sort Recent · Most cited
  1. 2026
    MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation ModelsXiaoyun Yu, Li Fan, Xiangfei Qiu … Jilin HuKDD · East China Normal University · Shanghai Innovative Research Center of Traditional Chinese Medicine · +3
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  2. 2026
    Eliciting CLIP's intrinsic attribute knowledge through a dual-cache guided mechanism for class-incremental learningShengcheng Ye, Ziyi Wang, Yaomin Huang … Guixu ZhangKnowledge-Based Systems · East China Normal University
  3. 2026
    Revisiting Prototypes for Open-Domain Continual Learning in Vision-Language ModelsYadong Lu, Shitian Zhao, Boxiang Yun … Yue WangICASSP · East China Normal University
  4. 2026
    Developing Evolving Adaptability in Biological Intelligence: A Novel Biologically-Inspired Continual Learning Model for Video Saliency PredictionDandan Zhu, Kaiwei Zhang, Kun Zhu … Xiaokang YangTPAMI · East China Normal University · Shanghai Artificial Intelligence Laboratory · +3
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. By default it shows the 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. The rest are one click away under “All papers”. 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.