Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

28 papers of 6,984Sort Recent · Most cited
  1. 2025PDF ↗
  2. 2025
    Continual Gradient Low-Rank Projection Fine-Tuning for LLMsChenxu Wang, Yilin Lyu, Zicheng Sun, Liping JingACL
    PDF ↗
  3. 2025PDF ↗
  4. 2025PDF ↗
  5. 2025
    Enhancing Multimodal Continual Instruction Tuning with BranchLoRADuzhen Zhang, Yongcheng Ren, Zhongzhi Li … Bai, JinfengACL
    PDF ↗
  6. 2025PDF ↗
  7. 2025PDF ↗
  8. 2025
    Model Editing with Graph-Based External MemoryYash Kumar Atri, Ahmed Alaa, Thomas HartvigsenACL
    PDF ↗
  9. 2025PDF ↗
  10. 2025PDF ↗
  11. 2025
    SEE: Continual Fine-tuning with Sequential Ensemble of ExpertsZhilin Wang, Yafu Li, Xiaoye Qu, Yu ChengACL
    PDF ↗
  12. 2025
    TiC-LM: A Web-Scale Benchmark for Time-Continual LLM PretrainingJeffrey Li, Mohammadreza Armandpour, Iman Mirzadeh … Fartash FaghriACL
    PDF ↗
  13. 2025PDF ↗
  14. 2025PDF ↗
  15. 2025PDF ↗
  16. 2025
    Unveiling and Addressing Pseudo Forgetting in Large Language ModelsHuashan Sun, Yizhe Yang, Yinghao Li … Yang GaoACL · Beijing Institute of Technology
    PDF ↗
  17. 2025
  18. 2025
  19. 2025
  20. 2025
  21. 2025
  22. 2025
  23. 2025
    Exploring Forgetting in Large Language Model Pre-TrainingLiao, Chonghua, Ruobing Xie, Sun, Xingwu … Zhanhui KangACL
    PDF ↗
  24. 2025PDF ↗
  25. 2025PDF ↗
  26. 2025
    HFT: Half Fine-Tuning for Large Language ModelsTingfeng Hui, Zhenyu Zhang, Shuohuan Wang … Hua WuACL
    PDF ↗
  27. 2025
    Don't Half-listen: Capturing Key-part Information in Continual Instruction TuningYongquan He, Wenyuan Zhang, Xuancheng Huang … Cai, XunliangACL
    PDF ↗
  28. 2025PDF ↗
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. 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.