Related work

The foundational work on continual learning, 1991 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

46 papers of 4,574Sort Recent · Most cited
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    Spectral Collapse Drives Loss of Plasticity in Deep Continual LearningNaicheng He, Kaicheng Guo, Arjun Prakash … George KonidarisICML
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    Confounder-Free Continual Learning via Recursive Feature NormalizationYash Shah, Camila González, Mohammad Abbasi … E. AdeliICML
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    The Importance of Being Lazy: Scaling Limits of Continual LearningGraldi, Jacopo, Breccia, Alessandro, Lanzillotta, Giulia … Noci, LorenzoICML
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    Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing ChurnHongyao Tang, Johan Obando-Ceron, Pablo Samuel Castro … Glen BersethICML
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    Unlocking the Power of Rehearsal in Continual Learning: A Theoretical PerspectiveJunze Deng, Qiqi Wu, Peizhong Ju … Ness B. ShroffICML
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    LADA: Scalable Label-Specific CLIP Adapter for Continual LearningLuo, Mao-Lin, Zihao Zhou, Wei Tong, Min-Ling ZhangICML
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    Test-Time Learning for Large Language ModelsJinwu Hu, Zhitian Zhang, Guohao Chen … Mingkui TanICML
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    Learning without Isolation: Pathway Protection for Continual LearningZhikang Chen, Abudukelimu Wuerkaixi, Sen Cui … Tianling RenICML
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    GCAL: Adapting Graph Models to Evolving Domain ShiftsZiyue Qiao, Qianyi Cai, Hao Dong … Hui XiongICML
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    Learning Dynamics in Continual Pre-Training for Large Language ModelsXingjin Wang, Howe Tissue, Lu Wang … Daniel Dajun ZengICML
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    Autoencoder-Based Hybrid Replay for Class-Incremental LearningMilad Khademi Nori, Il‐Min Kim, Guanghui WangICML
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    BECAME: BayEsian Continual Learning with Adaptive Model MErgingMei Li, Yuxiang Lu, Qinyan Dai … Hongtao LuICML
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    Understanding the Limits of Lifelong Knowledge Editing in LLMsLukas Thede, Karsten Roth, Matthias Bethge … Hartvigsen, TomICML
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    Knowledge Retention for Continual Model-Based Reinforcement LearningYixiang Sun, Haotian Fu, Michael L. Littman, George KonidarisICML
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    A Selective Learning Method for Temporal Graph Continual LearningHanmo Liu, Di Su, Haoyang Li … Lei ChenICML
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    From RAG to Memory: Non-Parametric Continual Learning for Large Language ModelsBernal Jiménez Gutiérrez, Yiheng Shu, Weijian Qi … Yu SuICML
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    Knowledge Swapping via Learning and UnlearningXing, Mingyu, Lechao Cheng, S Y Tang … Meng WangICML
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    Navigating Semantic Drift in Task-Agnostic Class-Incremental LearningFangwen Wu, Lechao Cheng, Shengeng Tang … Meng WangICML
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    Reinforced Lifelong Editing for Language ModelsZherui Li, Jiang, Houcheng, Hao Chen … Xiang WangICML
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    Demystifying Catastrophic Forgetting in Two-Stage Incremental Object DetectorQirui Wu, Shizhou Zhang, De Cheng … Yanning ZhangICML
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    Upweighting Easy Samples in Fine-Tuning Mitigates ForgettingSunny Sanyal, Hayden Prairie, Rudrajit Das … Sujay SanghaviICML
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    Large Continual Instruction AssistantJingyang Qiao, Zhang, Zhizhong, Xin Tan … Yuan XieICML
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    Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?Guiomar Pescador-Barrios, Sarah Filippi, Mark van der WilkICML
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    Improving Continual Learning Performance and Efficiency with Auxiliary ClassifiersFilip Szatkowski, Zheng, Yaoyue, Fei Yang … Joost van de WeijerICML
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    Where is the Truth? The Risk of Getting Confounded in a Continual WorldFlorian Peter Busch, Roshni Kamath, Rupert Mitchell … Martin MundtICML
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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. It lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times, and only papers with a PDF we can point you at, so every title opens the paper itself. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.