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cs.AI2026
MePo++: Unifying Representation Refinement and Reconciliation for General Continual Learning
Guanglong Sun, Kanglei Zhou, Liyuan Wang +6
General continual learning (GCL) aims to learn from evolving data streams without task identities, explicit boundaries, or repeated access to previous data, making it a realistic y…
cs.AI2026
ComMem: Complementary Memory Systems for Test-Time Adaptation of Vision-Language Models
Guanglong Sun, Shuang Cui, Bo Lei +6
Test-time adaptation (TTA) of vision-language models (VLMs) is essential for their robust deployment in dynamic, real-world environments. However, existing TTA methods often adapt…
cs.AI2026
MePo: Meta Post-Refinement for Rehearsal-Free General Continual Learning
Guanglong Sun, Hongwei Yan, Liyuan Wang +5
To cope with uncertain changes of the external world, intelligent systems must continually learn from complex, evolving environments and respond in real time. This ability, collect…