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.

28 papers of 8,653Sort Recent · Most cited
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    Continual Gradient Low-Rank Projection Fine-Tuning for LLMsChenxu Wang, Yilin Lyu, Zicheng Sun, Liping JingACL
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    Enhancing Multimodal Continual Instruction Tuning with BranchLoRADuzhen Zhang, Yongcheng Ren, Zhongzhi Li … Bai, JinfengACL
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    Model Editing with Graph-Based External MemoryYash Kumar Atri, Ahmed Alaa, Thomas HartvigsenACL
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    SEE: Continual Fine-tuning with Sequential Ensemble of ExpertsZhilin Wang, Yafu Li, Xiaoye Qu, Yu ChengACL
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    TiC-LM: A Web-Scale Benchmark for Time-Continual LLM PretrainingJeffrey Li, Mohammadreza Armandpour, Iman Mirzadeh … Fartash FaghriACL
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    Unveiling and Addressing Pseudo Forgetting in Large Language ModelsHuashan Sun, Yizhe Yang, Yinghao Li … Yang GaoACL · Beijing Institute of Technology
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    Exploring Forgetting in Large Language Model Pre-TrainingLiao, Chonghua, Ruobing Xie, Sun, Xingwu … Zhanhui KangACL
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    HFT: Half Fine-Tuning for Large Language ModelsTingfeng Hui, Zhenyu Zhang, Shuohuan Wang … Hua WuACL
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    Don't Half-listen: Capturing Key-part Information in Continual Instruction TuningYongquan He, Wenyuan Zhang, Xuancheng Huang … Cai, XunliangACL
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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.