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cs.LG2026
No Free Efficiency: Revisiting the Trade-off Between Training Efficiency and Model Vulnerability
Yiyong Liu, Jun Sakuma, Michael Backes +1
Training efficiency has become the central driver of recent progress in foundation models. To overcome the massive computational and data requirements of large-scale training, rese…
cs.LG2026
Pruning Unsafe Tickets: A Resource-Efficient Framework for Safer and More Robust LLMs
Wai Man Si, Mingjie Li, Michael Backes +1
Machine learning models are increasingly deployed in real-world applications, but even aligned models such as Mistral and LLaVA still exhibit unsafe behaviors inherited from pre-tr…
cs.LG2026
Sparse Models, Sparse Safety: Unsafe Routes in Mixture-of-Experts LLMs
Yukun Jiang, Hai Huang, Mingjie Li +3
By introducing routers to selectively activate experts in Transformer layers, the mixture-of-experts (MoE) architecture significantly reduces computational costs in large language…