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.

8 papers of 11,817Sort Recent · Most cited
  1. 2026
    A dual-stage exemplar-free continual learning approach for physical field reconstructionChenying Tang, Ning Wang, Weien Zhou, W. YaoEng. Applications of AI
  2. 2025
    TableGPT-R1: Advancing Tabular Reasoning Through Reinforcement LearningSaisai Yang, Qingyi Huang, Jing Yuan … Junbo ZhaoarXiv
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  3. 2025
  4. 2025
    Updating Physical Field Reconstruction Model Based on Continual LearningChenying Tang, Ning Wang, Wenzhe Zhang … Wen YaoIEEE 6th International Seminar on Artificial Intelligence…
  5. 2025
  6. 2024
    Toward Practical Operation of Deep Reinforcement Learning Agents in Real-World Network Management at Open RAN EdgesHaiyuan Li, Hari Madhukumar, Peizheng Li … D. SimeonidouIEEE Communications Magazine
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  7. 2024
    Experimental demonstration of quantum continual learning with superconducting qubitsChuan-Yu Zhang, Zhide Lu, Liangtian Zhao … Chaolong Songnpj Quantum Information
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  8. 2022
    Disk anomaly detection for remote maintenance control system of natural gas pipeline based on multi-source domain transfer and incremental learningJing Xiao, Lei Wang, Wei Shi, Ning WangJournal of Physics Conference Series · Beijing University of Posts and Telecommunications · China National Petroleum Corporation (China)
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.