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.

13 papers of 11,817Sort Recent · Most cited
  1. 2026
    Modular Memory is the Key to Continual Learning AgentsVaggelis Dorovatas, M. Schwerin, Andrew D. Bagdanov … Rahaf AljundiarXiv
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  2. 2026
    Revisiting Weight Regularization for Low-Rank Continual LearningYaoyue Zheng, Yin Zhang, J. Weijer … Zhiqiang TianICLR
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  3. 2025
    Continual Learning for VLMs: A Survey and Taxonomy Beyond ForgettingYuyang Liu, Qiuhe Hong, Linlan Huang … Yonghong TianarXiv
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  4. 2025PDF ↗
  5. 2025
    EFC++: Elastic Feature Consolidation with Prototype Re-balancing for Cold Start Exemplar-free Incremental LearningSimone Magistri, Tomaso Trinci, Albin Soutif-Cormerais … Andrew D. BagdanovarXiv
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  6. 2025
    Replay-free Online Continual Learning with Self-Supervised MultiPatchesGiacomo Cignoni, Andrea Cossu, Alex Gomez-Villa … Antonio CartaThe European Symposium on Artificial Neural Networks
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  7. 2024
    Exemplar-free Continual Representation Learning via Learnable Drift CompensationAlex Gomez-Villa, Dipam Goswami, Kai Wang … J. WeijerECCV
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  8. 2024
    Resurrecting Old Classes with New Data for Exemplar-Free Continual LearningDipam Goswami, Albin Soutif-Cormerais, Yuyang Liu … J. WeijerCVPR
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  9. 2024
    An Empirical Analysis of Forgetting in Pre-trained Models with Incremental Low-Rank UpdatesAlbin Soutif-Cormerais, Simone Magistri, J. Weijer, Andew D. BagdanovCoLLAs
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  10. 2024PDF ↗
  11. 2024
    Elastic Feature Consolidation for Cold Start Exemplar-free Incremental LearningSimone Magistri, Tomaso Trinci, Albin Soutif-Cormerais … Andrew D. BagdanovICLR
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  12. 2023
    Continual Learning: Applications and the Road ForwardEli Verwimp, S. Ben-David, Matthias Bethge … Gido M. van de VenTrans. Mach. Learn. Res.
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  13. 2023PDF ↗
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.