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.

6 papers of 8,653Sort Recent · Most cited
  1. 2026
    Leveraging Textual Semantic Guidance for Few-Shot Class-Incremental LearningYuqiao Xu, Hantao Yao, Lu Yu, Changsheng XuACM Transactions · Tianjin University of Technology · University of Science and Technology of China · +1
  2. 2026
    Sleep as a system-level resilience mechanism in complex dynamic networks: Insights from biological and artificial systemsL Yang, C T Lin, Haohong Li, Xiaohui WangBrain medicine : · University of Science and Technology of China · Changchun Institute of Applied Chemistry · +1
  3. 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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  4. 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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  5. 2026
    GCL: Group-shared continual learning fine-tuning for sparse LLMsYanzhe Wang, Baoqun YinNeurocomputing · University of Science and Technology of China
  6. 2026
    Emotion-augmented continual learning for empathic robot behaviorYuxuan Zhao, Tongwei Zhang, Siqi Liu … Wei WuExpert Systems with Applications · Chinese Academy of Sciences · Shandong Institute of Automation · +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. 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.