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.

7 papers of 8,653Sort Recent · Most cited
  1. 2025
    CEM: A Data-Efficient Method for Large Language Models to Continue Evolving From MistakesHaokun Zhao, Jinyi Han, Jie Shi … Fei YuCIKM · Fudan University · East China Normal University · +1
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  2. 2025
    Towards Robust Continual Test-Time Adaptation via Neighbor FiltrationTaki Hasan Rafi, Amit Agarwal, Hitesh Laxmichand Patel, Dong‐Kyu ChaeCIKM · Hanyang University · Liverpool John Moores University · +1
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  3. 2025
    DebiasedKGE: Towards Mitigating Spurious Forgetting in Continual Knowledge Graph EmbeddingJunlin Zhu, Bo Fu, Guiduo DuanCIKM · University of Electronic Science and Technology of China
  4. 2025
    Hyperbolic Prompt Learning for Incremental Event Detection with LLMsXiujin Zhang, Wenxin Jin, Haotian Hong … Li SunCIKM · North China Electric Power University · Anhui University of Science and Technology · +2
  5. 2025
    Contextual Attention Modulation: Towards Efficient Multi-Task Adaptation in Large Language ModelsDawei Pan, Zhaoyang Fu, Jingyuan Wang … Xiangyu ZhaoCIKM · City University of Hong Kong · Beihang University · +2
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  6. 2025
    Exploring the Tradeoff Between Diversity and Discrimination for Continuous Category DiscoveryRuobing Jiang, Yang Liu, Haobing Liu … Chunyang WangCIKM · Ocean University of China · East China Normal University
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  7. 2025
    Federated Continual RecommendationJaehyung Lim, Wonbin Kweon, Woojoo Kim … Hwanjo YuCIKM
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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 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.