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cs.LG2026
When Muon Meets Task Interference: A Spectral Perspective on Continual Learning and Model Merging
Shangge Liu, Yuehan Yin, Yinghuan Shi +2
Continual learning (CL) and model merging (MM) both aim to obtain a single model that performs well across multiple tasks, challenged respectively by catastrophic forgetting and we…
cs.LG2026
Harness Continual Learning: Continual Adaptation Beyond Model Parameters
Borui Kang, Jinrui Gu, Junhan Lv +3
Continual learning has largely been model-centric, treating model parameters as the state that changes with sequential experience. Modern agents can also adapt through a harness of…
cs.AI2026
Understanding and Enforcing Weight Disentanglement in Task Arithmetic
Shangge Liu, Yuehan Yin, Lei Wang +5
Task arithmetic provides an efficient, training-free way to edit pre-trained models, yet lacks a fundamental theoretical explanation for its success. The existing concept of ``weig…