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.

8 papers of 8,653Sort Recent · Most cited
  1. 2026
    A scalable complexity-regularized modular network for continual learningZiye Fang, Bo Wan, Shangqi Guo, Jian K. LiuNeurocomputing · Xidian University · Human Computer Interaction (Switzerland) · +2
  2. 2026
    Continual-GraphLLM: Dynamic Graph Large Language Model with Invariance Regularized Adaptive Multi-Scale ExpertsTianhang Wan, Xin Wang, Haibo Chen … Wenwu ZhuKDD · Tsinghua University · Alibaba Group (China)
  3. 2026
    Generalized few-shot intent detection by prompt learning without forgettingChaiyut Luoyiching, Yangning Li, Rongsheng Li … Hong‐Gee KimNeural Computing and Applications · University Town of Shenzhen · Tsinghua University · +3
  4. 2026
    Pooling-based Gate Networks for Dynamic Modular Continual LearningZiye Fang, Bo Wan, Shangqi Guo, Jian K. LiuJournal of Information and Intelligence · Xidian University · Human Computer Interaction (Switzerland) · +2
  5. 2026
    Experimental demonstration of quantum continual learning with superconducting qubitsChuanyu Zhang, Zhide Lu, Liangtian Zhao … Chao Songnpj Quantum Information · Zhejiang University · ShangHai JiAi Genetics & IVF Institute · +7
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  6. 2026
    Multi-Stage Knowledge Integration of Vision-Language Models for Continual LearningHongsheng Zhang, Zhong Ji, Jingren Liu … Jungong HanTIP · Tianjin University · Tsinghua University
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  7. 2026
    NExplore: Exploration with Neural Fields for Autonomous Scene Reconstruction.Zike Yan, Zijia Kuang, Yuetao Li … Hongbin ZhaTPAMI · Tsinghua University · Beijing Academy of Artificial Intelligence
  8. 2026
    Continual learning: A systematic literature reviewQinwen Yang, Liyuan Wang, Joerg Wicker, Gillian DobbieNeural Networks · University of Auckland · Tsinghua University · +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. 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.