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.

22 papers of 8,653Sort Recent · Most cited
  1. 2026
    Building Intelligent Agents with Neuro-Symbolic ConceptsJiayuan Mao, Josh Tenenbaum, Jiajun WuCommunications of the ACM · Massachusetts Institute of Technology · Institute of Cognitive and Brain Sciences · +1
  2. 2025
    Hippocampal indexing alters the stability landscape of synaptic weight space allowing life-long learningOscar C. González, Ryan Golden, Erik Delanois … Maxim BazhenovbioRxiv · University of Colorado Boulder · University of California San Diego · +3
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  3. 2025
    Interleaved Replay of Novel and Familiar Memory Traces During Slow-Wave Sleep Prevents Catastrophic ForgettingRyan Golden, Rajat Saxena, Oscar C. González … Maxim BazhenovbioRxiv · University of California San Diego · University of California, Irvine · +2
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  4. 2025
    Jointly exploring client drift and catastrophic forgetting in dynamic learningNiklas Babendererde, Moritz Fuchs, Camila González … Anirban MukhopadhyayScientific Reports · Technische Universität Darmstadt · Stanford University · +1
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  5. 2025
    Gradient-Guided Epsilon Constraint Method for Online Continual LearningSong Lai, Changyi Ma, Fei Zhu … Qingfu ZhangNeurIPS · City University of Hong Kong · Chinese University of Hong Kong · +4
  6. 2023
    Rethinking Few-Shot Class-Incremental Learning With Open-Set Hypothesis in Hyperbolic GeometryYawen Cui, Zitong Yu, Wei Peng … Li LiuIEEE Trans. Multimedia · University of Oulu · Great Bay University · +3
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  7. 2023
    Instant Continual Learning of Neural Radiance FieldsRyan Po, Zhengyang Dong, Alexander W. Bergman, Gordon WetzsteinICCV · Stanford University
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  8. 2022
    Representational drift: Emerging theories for continual learning and experimental future directions.Laura Driscoll, Lea Duncker, Christopher D. HarveyCurrent Opinion in Neurobiology · Stanford University · Howard Hughes Medical Institute · +1
  9. 2022
    Rethinking Architecture Design for Tackling Data Heterogeneity in Federated LearningLiangqiong Qu, Yuyin Zhou, Paul Pu Liang … Daniel L. RubinCVPR · Stanford University · University of California, Santa Cruz · +2
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  10. 2022
    Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic EnvironmentsAbhiram Iyer, Karan Grewal, Akash Velu … Subutai AhmadFrontiers · Carnegie Mellon University · Stanford University · +1
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  11. 2021
    Reset-Free Lifelong Learning with Skill-Space PlanningKevin Lü, Aditya Grover, Pieter Abbeel, Igor MordatchICLR · University of California, Berkeley · Stanford University · +1
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  12. 2020
    Generative Continual Concept LearningMohammad Rostami, Soheil Kolouri, Praveen K. Pilly, James L. McClellandAAAI · California University of Pennsylvania · HRL Laboratories (United States) · +1
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  13. 2020
    Model primitives for hierarchical lifelong reinforcement learningBohan Wu, Jayesh K. Gupta, Mykel J. KochenderferAutonomous Agents and Multi-Agent Systems · Columbia University · Stanford University
  14. 2020
    Continual Adaptation for Efficient Machine CommunicationRobert D. Hawkins, Minae Kwon, Dorsa Sadigh, Noah D. GoodmanCoNLL · Princeton University · Department of Physics, Mathematics and Informatics · +1
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  15. 2020
    Continuous Meta-Learning without TasksJ. Michael Harrison, Apoorva Sharma, Chelsea Finn, Marco PavoneNeurIPS · Stanford University · University of California, Berkeley
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  16. 2020
    Generative Memory for Lifelong LearningXin Su, Shangqi Guo, Tian Tan, Feng ChenTNNLS · Beijing Advanced Sciences and Innovation Center · Tsinghua University · +1
  17. 2019
    Model Primitive Hierarchical Lifelong Reinforcement LearningBohan Wu, Jayesh K. Gupta, Mykel J. KochenderferInternational Joint Conference on Autonomous Agents and M… · Columbia University · Stanford University
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  18. 2019
    Online Meta-LearningChelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey LevineICML · Stanford University · University of Washington · +3
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  19. 2019
    Task representations in neural networks trained to perform many cognitive tasksGuangyu Robert Yang, Madhura R. Joglekar, Hui Song … Xiao‐Jing WangNature Neuroscience · New York University · Columbia University · +5
  20. 2019
    Learning to Learn without Forgetting By Maximizing Transfer and Minimizing InterferenceMatthew Riemer, Ignacio Cases, Robert Ajemian … Gerald TesauroICLR · IBM (United States) · Stanford University · +2
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  21. 2018
    Modular Continual Learning in a Unified Visual EnvironmentKevin Feigelis, Blue Sheffer, Daniel YaminsICLR · Rutgers, The State University of New Jersey · Stanford University
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  22. 2017
    Improved multitask learning through synaptic intelligenceFriedemann Zenke, Ben Poole, Surya GanguliarXiv · Stanford University
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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.