collaborators

5 papers

cs.AI2026

League of LLMs: A Benchmark-Free Paradigm for Mutual Evaluation of Large Language Models

Qianhong Guo, Wei Xie, Xiaofang Cai +7

Although large language models (LLMs) have shown exceptional capabilities across a wide range of tasks, reliable evaluation remains a critical challenge due to data contamination,…

cs.CL2026

Structured Semantic Cloaking for Jailbreak Attacks on Large Language Models

Xiaobing Sun, Perry Lam, Shaohua Li +4

Modern LLMs employ safety mechanisms that extend beyond surface-level input filtering to latent semantic representations and generation-time reasoning, enabling them to recover obf…

cs.CL2025

AIPsychoBench: Understanding the Psychometric Differences between LLMs and Humans

Wei Xie, Shuoyoucheng Ma, Zhenhua Wang +4

Large Language Models (LLMs) with hundreds of billions of parameters have exhibited human-like intelligence by learning from vast amounts of internet-scale data. However, the unint…

cs.AI2025

Do Large Language Models Truly Grasp Mathematics? An Empirical Exploration From Cognitive Psychology

Wei Xie, Shuoyoucheng Ma, Zhenhua Wang +4

The cognitive mechanism by which Large Language Models (LLMs) solve mathematical problems remains a widely debated and unresolved issue. Currently, there is little interpretable ex…

cs.CL2025

Cross-Modal Obfuscation for Jailbreak Attacks on Large Vision-Language Models

Lei Jiang, Zixun Zhang, Zizhou Wang +4

Large Vision-Language Models (LVLMs) demonstrate exceptional performance across multimodal tasks, yet remain vulnerable to jailbreak attacks that bypass built-in safety mechanisms…