**摘要**
Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to catastrophic forgetting of previous knowledge. Existing methods typically treat parameters uniformly, failing to distinguish between specific task knowledge and shared capabilities. We introduce Mixture of Sparse Experts for Task Agnostic Continual Lea
👤 作者: Fatema Siddika, Md Anwar Hossen, Tanwi Mallick, Ali Jannesari

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🔗 **[Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning](https://arxiv.org/abs/2606.07500v1)**

> Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning
🏷️ 来源: ArXiv cs.AI
⏱️ 2026-06-08 14:00