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.

2 papers of 7,070Sort Recent · Most cited
  1. 2025
    Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series ForecastingNouha Karaouli, Denis Coquenet, Élisa Fromont … Marina ReybozarXiv · Institut de Recherche en Informatique et Systèmes Aléatoires · Université de Rennes · +7
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  2. 2025
    Exploring continual learning strategies in artificial neural networks through graph-based analysis of connectivity: Insights from a brain-inspired perspectiveLucrezia Carboni, Dwight Nwaigwe, Marion Mainsant … Sophie AchardNeural Networks · Centre National de la Recherche Scientifique · Inserm · +10
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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. It lists only 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. 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.