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.

8 papers of 8,653Sort Recent · Most cited
  1. 2026
    A complementary learning system for continual episodic memory in large language modelsXu Pan, Ely Hahami, Roy Siegelmann, Haim SompolinskybioRxiv · Harvard University Press · Hebrew University of Jerusalem
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  2. 2026
    Hierarchical learning creates invariant schema within plastic neural networks.James T. Elder, Jie Zheng, Lydia B. Shimelis … Milo M. LinJournal of Computational Neuroscience · Center for Systems Biology · The University of Texas Southwestern Medical Center · +6
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  3. 2026
    Order parameters and phase transitions of continual learning in deep neural networksHaozhe Shan, Qianyi Li, Haim SompolinskyPNAS · Harvard University · Harvard University Press · +4
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  4. 2025
    Few-Shot Incremental Learning via Foreground Aggregation and Knowledge Transfer for Audio-Visual Semantic SegmentationJingqiao Xiu, Mengze Li, Zongxin Yang … Roger ZimmermannAAAI · National University of Singapore · Hong Kong University of Science and Technology · +4
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  5. 2021
    Tuned Compositional Feature Replays for Efficient Stream LearningMorgan B. Talbot, Rushikesh Zawar, Rohil Badkundri … Gabriel KreimanTNNLS · Boston Children's Hospital · Harvard–MIT Division of Health Sciences and Technology · +6
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  6. 2023
    Toward a More Neurally Plausible Neural Network Model of Latent Cause InferenceQihong Lu, Tan Tien Nguyen, Uri Hasson … Kenneth A. NormanConference on Cognitive Computational Neuroscience · Princeton University · Washington University in St. Louis · +1
  7. 2022
    Stochastic consolidation of lifelong memoryNimrod Shaham, Jay Chandra, Gabriel Kreiman, Haim SompolinskyScientific Reports · Harvard University · Hebrew University of Jerusalem · +1
  8. 2019
    Variational Prototype Replays for Continual LearningMengmi Zhang, Tao Wang, Joo‐Hwee Lim … Jiashi FengarXiv · Harvard University Press
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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. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. 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.