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
    Hyperspherical semantic compensation of vision-language models for continual learningHongsheng Zhang, Zhong Ji, Chen Tang, Yanwei PangKnowledge-Based Systems · Tianjin University · Beijing Academy of Artificial Intelligence · +1
  2. 2026PDF ↗
  3. 2026
    SD2-SNN: Self-distillation and structural decomposition framework for SNNs in continual learningZhenhao Xie, Xia Xiao, Hongsheng Zhang … Zhong JiNeural Networks · Tianjin University
  4. 2026
    Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel PerspectiveJingren Liu, Zhong Ji, Yunlong Yu … Xuelong LiTPAMI · Tianjin University · Zhejiang University · +3
    PDF ↗
  5. 2026
    Multi-Stage Knowledge Integration of Vision-Language Models for Continual LearningHongsheng Zhang, Zhong Ji, Jingren Liu … Jungong HanTIP · Tianjin University · Tsinghua University
    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. 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.