Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

8 papers of 11,817Sort Recent · Most cited
  1. 2026
    To Retain or to Adapt? Generalizing Continual LearningGiulia Lanzillotta, Mandana Samiei, D. Precup … Claire VernadearXiv
    PDF ↗
  2. 2026
    When One Adapter Speaks for Many: Discovering Low-Rank Redundancy in Continual Fine-TuningTanguy Dieudonn'e, Giulia Lanzillotta, Enis Simsar … Thomas HofmannarXiv
    PDF ↗
  3. 2026
    Heads collapse, features stay: Why Replay needs big buffersGiulia Lanzillotta, D. Meier, Thomas HofmannICLR
    PDF ↗
  4. 2025
    Barriers for Learning in an Evolving World: Mathematical Understanding of Loss of PlasticityA. Joudaki, Giulia Lanzillotta, Mohammad Samragh Razlighi … Fartash FaghriarXiv
    PDF ↗
  5. 2025
    Reactivation: Empirical NTK Dynamics Under Task ShiftsYu-Zhi Liu, Zixuan Chen, Zirui Zhang … Giulia LanzillottaarXiv
    PDF ↗
  6. 2025
    The Importance of Being Lazy: Scaling Limits of Continual LearningJacopo Graldi, Alessandro Breccia, Giulia Lanzillotta … Lorenzo NociICML
    PDF ↗
  7. 2024
    Local vs Global continual learningGiulia Lanzillotta, Sidak Pal Singh, B. Grewe, Thomas HofmannCoLLAs
    PDF ↗
  8. 2023
    Towards guarantees for parameter isolation in continual learningGiulia Lanzillotta, Sidak Pal Singh, B. Grewe, Thomas HofmannarXiv
    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. 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.