Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

19 papers of 11,817Sort Recent · Most cited
  1. 2026
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  3. 2026
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  8. 2026
    PILA'26: Personal Intelligence in Agentic AI EraXiaoyan Zhao, Yang Zhang, Moxin Li … Yang SongKDD
  9. 2026
    Self-Regulating Prompt Expansion for Continual LearningYiwen Wang, Diana Benavides-Prado, Yun Sing KohKDD
  10. 2026
  11. 2026
    Sparsity Curse: Understanding RLVR Model Parameter Space from Model MergingChenrui Wu, Zexi Li, Jiajun Bu … Haishuai WangKDD
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  12. 2026PDF ↗
  13. 2026PDF ↗
  14. 2026
  15. 2026
    cPNN: Continuous Progressive Neural Networks for Evolving Streaming Time SeriesFederico Giannini, Giacomo Ziffer, Emanuele Della ValleKDD
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  16. 2026PDF ↗
  17. 2026
    TS-Memory: Plug-and-Play Memory for Time Series Foundation ModelsSisuo Lyu, Siru Zhong, Tiegang Chen … Yuxuan LiangKDD
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  18. 2026
    CREAM: Continual Retrieval on Dynamic Streaming Corpora with Adaptive Soft MemoryHuiJeong Son, Hyeongu Kang, Sunho Kim … Susik YoonKDD
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  19. 2026
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.