3 papers
cs.LG2026
When Routing Reveals Membership: Privacy Leakage from MoE Router Telemetry
Yixin Tan, Jiayang Liu, Lu Sun +3
Mixture-of-Experts (MoE) language models produce routing information during inference that may be logged or exposed for monitoring, debugging, load analysis, and safety auditing. U…
cs.CR2026
Near-Duplicate Families Break Exact-Record Membership Inference
Yiyong Liu, Jiayang Liu, Yixin Tan +2
Membership inference (MI) asks whether a specific record appeared in a model's training set and is increasingly used as evidence for data provenance and copyright auditing. These a…
cs.CL2026
The Benchmark Illusion: Pruned LLMs Can Pass Multiple Choice but Fail to Answer
Rui Wen, Lu Sun, Jiayang Liu +3
Compressing large language models reduces memory use and inference cost, but it can also create failures that standard benchmarks miss. A pruned model may still perform well on mul…