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cs.CR2026
PathMark: Protecting Intellectual Property of Mixture-of-Expert LLMs via Path Watermarks
Yudong Gao, Qingyue Wang, Yuanyuan Yuan +4
Mixture-of-Experts (MoE) large language models represent high-value intellectual property, yet existing watermarking schemes designed for dense models fail on MoE architectures due…
cs.CR2025
RepetitionCurse: Measuring and Understanding Router Imbalance in Mixture-of-Experts LLMs under DoS Stress
Ruixuan Huang, Qingyue Wang, Hantao Huang +4
Mixture-of-Experts architectures have become the standard for scaling large language models due to their superior parameter efficiency. To accommodate the growing number of experts…
cs.CR2025
BadMoE: Backdooring Mixture-of-Experts LLMs via Optimizing Routing Triggers and Infecting Dormant Experts
Qingyue Wang, Qi Pang, Xixun Lin +2
Mixture-of-Experts (MoE) have emerged as a powerful architecture for large language models (LLMs), enabling efficient scaling of model capacity while maintaining manageable computa…