Related work

The foundational work on continual learning, 1991 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 4,574Sort Recent · Most cited
  1. 2026
    Continual test-time adaptation for object detection with adaptive monitoring and randomized restorationShilei Cao, Juepeng Zheng, Yan Liu … Haohuan FuExpert Systems with Applications · Sun Yat-sen University · National Supercomputing Center in Shenzhen · +4
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  2. 2026
    TAAM:Inductive Graph-Class Incremental Learning with Task-Aware Adaptive ModulationJingtao Liu, Xi ZhangInternational Conference on Autonomous Agents and Multiag… · University of Science and Technology of China
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  3. 2026
    Mitigating Catastrophic Forgetting With Adaptive Transformer Block Expansion in Federated Fine-TuningYujia Huo, Jianchun Liu, Hongli Xu … Liusheng HuangIEEE Transactions · University of Science and Technology of China · China University of Mining and Technology
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  4. 2026
    Enhancing Federated Class-Incremental Learning via Spatial-Temporal Statistics AggregationZenghao Guan, Guojun Zhu, Zhou Yucan … Xiaoyan GuWWW · Institute of Information Engineering · University of Chinese Academy of Sciences · +3
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  5. 2026
    We Need a More Robust Classifier: Dual Causal Learning Empowers Domain-Incremental Time Series ClassificationZhipeng Liu, Peibo Duan, Xuan Tang … Binwu WangACM Web Conference 2026 · Northeastern University · Xi'an Jiaotong University · +2
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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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times, and only papers with a PDF we can point you at, so every title opens the paper itself. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.