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cs.CR2025
Improving Sustainability of Adversarial Examples in Class-Incremental Learning
Taifeng Liu, Xinjing Liu, Liangqiu Dong +3
Current adversarial examples (AEs) are typically designed for static models. However, with the wide application of Class-Incremental Learning (CIL), models are no longer static and…
cs.CR2025
Auditing Data Membership in Reinforcement Learning With Verifiable Rewards
Yule Liu, Heyi Zhang, Jinyi Zheng +6
Reinforcement Learning with Verifiable Rewards (RLVR) has become a core training stage in recent large language models (LLMs). Its reliance on non-public, high-value prompt sets ra…