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.

5 papers of 8,653Sort Recent · Most cited
  1. 2026
    Federated Class Incremental Learning Method With High Accuracy and Extremely Low Communication Cost Based on Broad Learning SystemJie Du, Wenbing Chen, Peng Liu … C L Philip ChenIEEE Transactions · Shenzhen University · University of Electronic Science and Technology of China · +2
  2. 2026
    Retrieval-Augmented Pseudo-Image Guided Alignment and Text Domain-Aware Memory Recall for Continual Zero-Shot CaptioningBing Liu, Wenjie Yang, Mingming Liu … Yong ZhouIEEE TCSVT · China University of Mining and Technology · Jiangsu Vocational Institute of Architectural Technology · +1
  3. 2025
    MemOS: A Memory OS for AI SystemZhiyu Li, Chunyan Xi, Chunyu Li … Feiyu XiongarXiv
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  4. 2023
    IOB: integrating optimization transfer and behavior transfer for multi-policy reuseSiyuan Li, Hao Li, Jin Zhang … Chongjie ZhangAutonomous Agents and Multi-Agent Systems · Harbin Institute of Technology · Northwestern Polytechnical University · +2
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  5. 2024
    Class-Incremental Learning Method With Fast Update and High Retainability Based on Broad Learning SystemJie Du, Peng Liu, Chi‐Man Vong … C. L. Philip ChenTNNLS · Shenzhen University Health Science Center · University of Macau · +2
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.