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cs.LG2026

Hyperball May Not Be a Free Lunch

Yihao Xiao, Jialong Sun, Zitian Gao +5

For scale-invariant deep networks, Hyperball-style optimizers have shown strong performance in large-scale training by fixing the norms of matrix-valued parameters and normalizing…

cs.LG2026

SMI: Statistical Membership Inference for Reliable Unlearned Model Auditing

Jialong Sun, Zeming Wei, Jiaxuan Zou +6

Machine unlearning (MU) is essential for enforcing the right to be forgotten in machine learning systems. A key challenge of MU is how to reliably audit whether a model has truly f…

cs.LG2026

Unveiling the Basin-Like Loss Landscape in Large Language Models

Huanran Chen, Yinpeng Dong, Zeming Wei +4

We discover the emergence of \textit{basins} in the loss landscape of large language models. As model scale increases, LLMs become progressively more resilient to random perturbati…

cs.LG2026

RAPO: Risk-Aware Preference Optimization for Generalizable Safe Reasoning

Zeming Wei, Qiaosheng Zhang, Xia Hu +1

Large Reasoning Models (LRMs) have achieved tremendous success with their chain-of-thought (CoT) reasoning, yet also face safety issues similar to those of basic language models. I…

cs.LG2025

Towards the Worst-case Robustness of Large Language Models

Huanran Chen, Yinpeng Dong, Zeming Wei +2

Recent studies have revealed the vulnerability of large language models to adversarial attacks, where adversaries craft specific input sequences to induce harmful, violent, private…

cs.LG2025

Reliable Unlearning Harmful Information in LLMs with Metamorphosis Representation Projection

Chengcan Wu, Zeming Wei, Huanran Chen +2

While Large Language Models (LLMs) have demonstrated impressive performance in various domains and tasks, concerns about their safety are becoming increasingly severe. In particula…