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. 2026
    ASMem: Anchor sparse memory for multi-domain knowledge editing of large language modelsGuanyu Zheng, Zhenyu Wang, Yang Zhao … Chengqing ZongNeural Networks · South China University of Technology · Shandong Institute of Automation · +3
  2. 2026
    GAIN: Global-Atomic INteraction Graph for Few-Shot Class-Incremental LearningFan Lyu, Linglan Zhao, Changli Liu … Liang WangIEEE TCSVT · Chinese Academy of Sciences · Institute of Automation · +6
  3. 2026
    Continual learning of multiple cognitive functions with a brain-inspired temporal development mechanismBing Han, feifei Zhao, Yinqian Sun … Yi ZengNational Science Review · Center for Excellence in Brain Science and Intelligence Technology · Institute of Automation · +2
    PDF ↗
  4. 2026
    Constructing Enhanced Mutual Information for Online Class-Incremental LearningHuan Zhang, Fan Lyu, Shenghua Fan … Dingwen WangIEEE Trans. Multimedia · Wuhan University · Institute of Automation
    PDF ↗
  5. 2026
    Spectral Disentanglement: Rank-Aware Task Adaptation for Rehearsal-free Continual Learning in LLMsHuanxuan Liao, Shizhu He, Yupu Hao … Kang LiuACL · Shandong Institute of Automation · Beijing Academy of Artificial Intelligence · +3
    PDF ↗
  6. 2026
    Symmetric Image-Text Tuning With Entropy-Guided Fusion for Online Continual Learning in Non-Stationary Visual StreamsLeyuan Wang, Liuyu Xiang, Yujie Wei … Zhaofeng HeTIP · Beijing University of Posts and Telecommunications · Shandong Institute of Automation · +1
  7. 2026
    Harmonizing the Past, Present, and Future: A Null-Space Constrained Region-Specific Method for Continual Learning in LLMsJinhui Chen, Shizhu He, Xingchang Yang … Jun ZhaoACL · Shandong Institute of Automation · Beijing Academy of Artificial Intelligence · +3
    PDF ↗
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.