Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 6,984Sort Recent · Most cited
  1. 2026
    TTMC: Brain-Inspired Test-Time Memory Calibration with Orthogonal Projection for Online Continual LearningYuyang Han, Ziyu Li, Diwei Su … Xia WuKDD · Beijing Normal University · Beijing Institute of Technology · +1
  2. 2026
    Learning forward-compatible and domain-invariant representations for cross-domain few-shot class-incremental learningWeidong Shi, Xudong Yan, Jiazheng Yuan … Songhe FengNeural Networks · Beijing Jiaotong University · Beijing Normal University · +3
  3. 2026
    The Stability-Plasticity Dilemma Revisited: A Brain-Inspired Continual Learning Method with Representation-Function SeparationYuyang Han, Li Z, Zhiying Long, Xia WuICASSP · Beijing Normal University · Beijing Institute of Technology · +2
  4. 2026
    Boosting Few-Shot Continual Learning via Self-Adaptive EvolutionZiqi Gu, Chunyan Xu, Yue Wang … Zhen CuiTIP · Nanjing University of Science and Technology · Nanyang Technological University · +2
  5. 2026
    Rethinking softmax in incremental learningZheng Zhai, Jiali Zhang, Haiyu Wang … Qiang SunNeural Networks · Beijing Normal-Hong Kong Baptist University · Beijing Normal University · +4
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. 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.