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
    Bi-Compatible Task-Agnostic Feature Augmentation for Expansion-Based Class-Incremental LearningBowen Zheng, Zijun Shen, Da-Wei Zhou … De‐Chuan ZhanInternational Journal of Computer Vision · Nanjing University
  2. 2026
    Amorphous LiNbO3 memristors for nonvolatile memory and neuromorphic computing with metaplasticity-enabled learningMengting Dong, Ruirui Hu, Yi-Xiang Wang … Xiaodong PiNeuromorphic Computing and Engineering · Zhejiang University of Science and Technology · Hangzhou Dianzi University · +1
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  3. 2026
    Decomposing the Neurons: Activation Sparsity via Mixture of Experts for Continual Test Time AdaptationRongyu Zhang, Aosong Cheng, Y. Luo … Yuan DuAAAI · Hong Kong Polytechnic University · Peking University · +3
    PDF ↗
  4. 2026
    Online Cross-Modal Hashing with Expanding Label SpaceWentao Fan, Chao Zhang, Chunlin Chen, Huaxiong LiAAAI · Nanjing University
  5. 2026
    A continual learning framework with long-term and multiple short-term memory networksShangge Liu, Lei Wang, Rui Yan … Yang GaoNeural Networks · Nanjing Tech University · University of Wollongong · +2
  6. 2026
    Class semantics guided knowledge distillation for few-shot class incremental learningPing Li, Jiajun Chen, Shaoqi Tian, Ran WangInformation Sciences · Hangzhou Dianzi University · Nanjing 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.