activity
20242026
most citedMultiBreak: A Scalable and Diverse Multi-turn Jailbreak Benchmark for Evaluating LLM Safety

1 citations · 1 across the 2 of their papers we have counts for

collaborators

9 papers

cs.AI2026

Agentic-imodels: Evolving agentic interpretability tools via autoresearch

Chandan Singh, Yan Shuo Tan, Weijia Xu +4

Agentic data science (ADS) systems are rapidly improving their capability to autonomously analyze, fit, and interpret data, potentially moving towards a future where agents conduct…

cs.CL20261 cited

MultiBreak: A Scalable and Diverse Multi-turn Jailbreak Benchmark for Evaluating LLM Safety

Jialin Song, Xiaodong Liu, Weiwei Yang +4

We present MultiBreak, a scalable and diverse multi-turn jailbreak benchmark to evaluate large language model (LLM) safety. Multi-turn jailbreaks mimic natural conversational setti…

cs.AI2026

Statistical Estimation of Adversarial Risk in Large Language Models under Best-of-N Sampling

Mingqian Feng, Xiaodong Liu, Weiwei Yang +3

Large Language Models (LLMs) are typically evaluated for safety under single-shot or low-budget adversarial prompting, which underestimates real-world risk. In practice, attackers…

cs.CL2026

SEMA: Simple yet Effective Learning for Multi-Turn Jailbreak Attacks

Mingqian Feng, Xiaodong Liu, Weiwei Yang +4

Multi-turn jailbreaks capture the real threat model for safety-aligned chatbots, where single-turn attacks are merely a special case. Yet existing approaches break under exploratio…

cs.AI2025

The Illusion of Readiness in Health AI

Yu Gu, Jingjing Fu, Xiaodong Liu +29

Large language models have demonstrated remarkable performance in a wide range of medical benchmarks. Yet underneath the seemingly promising results lie salient growth areas, espec…

cs.CL2025

SAS: Simulated Attention Score

Chuanyang Zheng, Jiankai Sun, Yihang Gao +12

The attention mechanism is a core component of the Transformer architecture. Various methods have been developed to compute attention scores, including multi-head attention (MHA),…