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.

8 papers of 6,984Sort Recent · Most cited
  1. 2026
    Model Editing for New Document Integration in Generative Information RetrievalZ C Zhang, Zihan Wang, Xinyu Ma … Zhaochun RenACM Web Conference 2026 · Shandong University · Qingdao University · +4
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  2. 2026
    Graph Cross-Domain Continual Fine-Tuning via Orthogonal LoRA Routing with Contrastive Expert SpecializationQianyi Cai, Ziyue Qiao, Minghao Yang … Hui XiongACM Web Conference 2026 · Great Bay University · University of Wisconsin–Madison
  3. 2026
    Self-Evolving LLMs via Continual Instruction TuningJiazheng Kang, Le Huang, Cheng Hou … Ting BaiACM Web Conference 2026 · Beijing University of Posts and Telecommunications
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  4. 2026
    Class-Domain Incremental Learning on Graphs via Disentangled Knowledge DistillationQin Tian, Chen Zhao, Xintao Wu … Wenjun WangACM Web Conference 2026 · Tianjin University · Baylor University · +2
  5. 2026
    We Need a More Robust Classifier: Dual Causal Learning Empowers Domain-Incremental Time Series ClassificationZhipeng Liu, Peibo Duan, Xuan Tang … Binwu WangACM Web Conference 2026 · Northeastern University · Xi'an Jiaotong University · +2
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  6. 2026
    E2PL: Effective and Efficient Prompt Learning for Incomplete Multi-view Multi-Label Class Incremental LearningJiajun Chen, Yue Wu, Kai Huang … Guanjie ChengACM Web Conference 2026 · Zhejiang University · Ningbo University
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  7. 2026
    Smaller but Better: Plasticity-Preserving Continual Learning for Embedded AIChenxin Mao, Haibo Liu, Zhenzhe Zheng … Guihai ChenACM Web Conference 2026 · Shanghai Jiao Tong University
  8. 2026
    Space-based Parameter Evolving with Lightweight Optimization for Graph Adaptation to Evolving ShiftsJunyu Luo, Zixuan Ouyang, Xiao Luo … Ming ZhangACM Web Conference 2026 · Peking University · University of Wisconsin–Madison · +2
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.