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.

8 papers of 11,817Sort Recent · Most cited
  1. 2026PDF ↗
  2. 2026
    Tunable MAGMAX: Preference-Aware Model Merging for Continual LearningKeita Hiroshima, Kento Uchida, Shinichi ShirakawaICPR
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  3. 2026PDF ↗
  4. 2026
    STAER: Temporal Aligned Rehearsal for Continual Spiking Neural NetworkMatteo Gianferrari, Omayma Moussadek, Riccardo Salami … Simone CalderaraICPR
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  5. 2026
    Federated Class-Incremental Object DetectionMatthias Pijarowski, Matthias Rapp, Alexander Wolpert, Martin HeckmannICPR
  6. 2026
  7. 2026
    Kernel-Prototype Guided Background Adaptation for Class-Incremental Semantic SegmentationViet Tran Ngoc, Dinh-Nhat Loi, T. Dang, Quynh-Trang Pham ThiICPR
  8. 2026
    Self-adaptive Low-Rank Adaptation for Class-Incremental LearningYi-Ming Song, Qiqi Duan, Li-Jun Sun … Yu-Hui ShiICPR
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.