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.

67 papers of 8,653 · showing 1–50Sort Recent · Most cited
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    Heads collapse, features stay: Why Replay needs big buffersLanzillotta, Giulia, Meier, Damiano, Hofmann, ThomasICLR
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    A theory of initialisation’s impact on specialisationDevon Jarvis, Sebastian Lee, Clémentine Carla Juliette Dominé … Stefano Sarao MannelliICLR · University of the Witwatersrand · Simons Foundation · +6
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    Optimal protocols for continual learning via statistical physics and control theoryFrancesco Mori, Stefano Sarao Mannelli, Francesca MignaccoICLR · University of Oxford · University of the Witwatersrand · +4
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    Rethinking Continual Learning with Progressive Neural CollapseZheng Wang, Wenhua Yu, Yang Li, Sen LinICLR
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    Privacy-Aware Lifelong LearningOzan Özdenizci, Elmar Rueckert, Robert LegensteinICLR
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    Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual LearningN. Nayak, Krishnateja Killamsetty, Ligong Han … Akash SrivastavaICLR
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    Meta-Continual Learning of Neural FieldsSeongyoun Woo, Yun, Junhyeog, Gunhee KimICLR
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    Enhanced Continual Learning of Vision-Language Models with Model FusionHaoyuan Gao, Zicong Zhang, Wei, Yuqi … Weiran HuangICLR
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    CLDyB: Towards Dynamic Benchmarking for Continual Learning with Pre-trained ModelsShengzhuang Chen, Liao, Yikai, Xiaoxiao Sun … Ying WeiICLR
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    STAR: Stability-Inducing Weight Perturbation for Continual LearningMasih Eskandar, Tooba Imtiaz, Hill, Davin … Jennifer DyICLR
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    Advancing Prompt-Based Methods for Replay-Independent General Continual LearningZhiqi Kang, Liyuan Wang, Xingxing Zhang, Karteek AlahariICLR
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    Spurious Forgetting in Continual Learning of Language ModelsJunhao Zheng, Cai, Xidi, Shengjie Qiu, Qianli MaICLR
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    SD-LoRA: Scalable Decoupled Low-Rank Adaptation for Class Incremental LearningYichen Wu, Hongming Piao, Long-Kai Huang … Ying WeiICLR
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    C-CLIP: Multimodal Continual Learning for Vision-Language ModelWenzhuo Liu, Fei Zhu, Longhui Wei, Qi TianICLR
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    Coreset Selection via Reducible Loss in Continual LearningRuilin Tong, Yuhang Liu, J. Shi, Dong GongICLR
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    Boosting Multiple Views for pretrained-based Continual LearningQ. Tran, T. Tran, Khanh Doan … Trung LeICLR
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    Semantic Aware Representation Learning for Lifelong LearningFahad Sarfraz, Elahe Arani, Bahram ZonoozICLR
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    Federated Few-Shot Class-Incremental LearningM. A. Ma'sum, Mahardhika Pratama, Lin Liu … Ryszard KowalczykICLR
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    Vision and Language Synergy for Rehearsal Free Continual LearningM. A. Ma'sum, Mahardhika Pratama, Savitha Ramasamy … Ryszard KowalczykICLR
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    Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline DataZhiyuan Zhou, Peng, Andy, Qiyang Li … Aviral KumarICLR
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    Plastic Learning with Deep Fourier FeaturesAlex Lewandowski, Dale Schuurmans, Marlos C. MachadoICLR
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    Closed-form merging of parameter-efficient modules for Federated Continual LearningRiccardo Salami, Pietro Buzzega, Matteo Mosconi … Simone CalderaraICLR
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    LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model MergingKe Wang, Nikolaos Dimitriadis, Alessandro Favero … Pascal FrossardICLR
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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.