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.

3 papers of 8,653Sort Recent · Most cited
  1. 2026
    Deep Continual Learning in the Foundation Model Era (Dagstuhl Seminar 25432)Christopher Kanan, Martin Mundt, Tinne Tuytelaars … Timm Felix HessDagstuhl reports · University of Rochester · University of Bremen · +2
    PDF ↗
  2. 2023
    How Efficient Are Today’s Continual Learning Algorithms?Md Yousuf Harun, Jhair Gallardo, Tyler L. Hayes, Christopher KananCVPR · Rochester Institute of Technology · University of Rochester
    PDF ↗
  3. 2022
    System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy GamesIndranil Sur, Zachary Daniels, Abrar Rahman … Aswin RaghavanInternational Conference on AI-ML-Systems · SRI International · American University · +3
    PDF ↗
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.