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
    Reducing catastrophic forgetting via gradient-based task organization in class-incremental learningJunlong Huang, Lingji Xu, Yining Liu, Zhenglin LiNeurocomputing · Sun Yat-sen University · Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) · +1
  2. 2026
    HiCPS: Hierarchical complementary prompt synergy for few-shot class-incremental learningQiang Huang, Shengli Wu, Shaohua Wan … Hu LuNeurocomputing · Jiangsu University · University of Ulster · +4
  3. 2026
    TriP: A triple-prompt framework aligning pre-training and class-incremental objectives in continual graph learning.Can-Ming Cui, Hui-yu Zhou, Pei-Yuan Lai, Chang‐Dong WangNeural Networks · Sun Yat-sen University · Guangxi Zhuang Autonomous Region Health and Family Planning · +1
  4. 2026
    Continual Learning with Clip Text-Prototype and an Orthogonal Pre-Expanded Classification HeadTong Yu, Kanghao Chen, Jiantao Tan … Ruixuan WangICASSP · Sun Yat-sen University · Guangzhou University · +1
  5. 2026
    CAKGE: Context-Aware Adaptive Learning for Dynamic Knowledge Graph EmbeddingsZongsheng Cao, Qianqian Xu, Zhiyong Yang … Qingming HuangTPAMI · ShangHai JiAi Genetics & IVF Institute · Shanghai Artificial Intelligence Laboratory · +3
  6. 2026
    Learning Prompt Adapters for Forgetting-Free Continual Image Super-ResolutionChaowei Fang, Bolin Fu, De Cheng … Guanbin LiTIP · Xidian University · Sun Yat-sen University
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.