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.

7 papers of 8,653Sort Recent · Most cited
  1. 2026
    CLAIM: Mitigating Catastrophic Forgetting in Continual Instruction Fine-tuning Large Language ModelsXihe Qiu, Shaojie Shi, Teqi Hao, Xiaoyu TanIEEE TCDS · Shanghai University of Engineering Science · Fudan University · +1
  2. 2025
    ArMA: Mitigating Catastrophic Forgetting using Attention-Regularized Model Averaging in Continual Fine-tuning Large Language ModelsXihe Qiu, Leijun Cheng, Teqi Hao, Xiaoyu TanIEEE TAI · Shanghai University of Engineering Science · Tencent (China)
  3. 2024
    Enhancing Task Performance in Continual Instruction Fine-tuning Through Format UniformityXiaoyu Tan, Leijun Cheng, Xihe Qiu … Qi YuanSIGIR · Shanghai University of Engineering Science · Fudan University
  4. 2024
    Self-Evolution Policy Learning: Leveraging Basic Tasks to Complex OnesQizhen Chen, Wenwen Xiao, Yang Li, Xiangfeng LuoInternational Seminar on Artificial Intelligence, Network… · Shanghai University of Engineering Science
  5. 2024
    Adaptive tree-like neural network: Overcoming catastrophic forgetting to classify streaming data with concept driftsYimin Wen, Xiang Liu, Hang YuKnowledge-Based Systems · Guilin University of Electronic Technology · Shanghai University · +1
  6. 2021
    Prioritized Experience Replay for Continual LearningGuannan Hu, Wu Zhang, Wenhao ZhuInternational Conference on Computational Intelligence an… · Shanghai University of Engineering Science
  7. 2020
    SEM: Adaptive Staged Experience Access Mechanism for Reinforcement LearningJianshu Wang, Xinzhi Wang, Xiangfeng Luo … Yang LiIEEE 32nd International Conference on Tools with Artifici… · Shanghai University of Engineering Science
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.