9 papers · 1 filter
When Muon Optimizer Meets Adversarial Training: A Theoretical and Empirical Study
Jun Yan, Weiquan Huang, Jiankai Zuo +4
Adversarial training (AT) remains one of the most reliable empirical defenses against adversarial attacks. Its robustness critically depends on how the underlying min-max objective…
Secure LLM Fine-Tuning via Safety-Aware Probing
Chengcan Wu, Zhixin Zhang, Zeming Wei +3
Large language models (LLMs) have achieved remarkable success across many applications, but their ability to generate harmful content raises serious safety concerns. Although safet…
Absorber LLM: Harnessing Causal Synchronization for Test-Time Training
Zhixin Zhang, Shabo Zhang, Chengcan Wu +2
Transformers suffer from a high computational cost that grows with sequence length for self-attention, making inference in long streams prohibited by memory consumption. Constant-m…
Stabilizing Multi-Attack Adversarial Training via Bandit Optimization
Rui Wang, Zeming Wei, Xiyue Zhang +1
Deep Neural Networks (DNNs) remain vulnerable to diverse adversarial perturbations, motivating multi-attack adversarial training (AT) for improved robustness. However, existing met…
Dynamic Orthogonal Continual Fine-tuning for Mitigating Catastrophic Forgettings
Zhixin Zhang, Zeming Wei, Meng Sun
Catastrophic forgetting remains a critical challenge in continual learning for large language models (LLMs), where models struggle to retain performance on historical tasks when fi…
Boosting Jailbreak Attack with Momentum
Yihao Zhang, Zeming Wei
Large Language Models (LLMs) have achieved remarkable success across diverse tasks, yet they remain vulnerable to adversarial attacks, notably the well-known jailbreak attack. In p…