most citedNuclear Deployed: Analyzing Catastrophic Risks in Decision-making of Autonomous LLM Agents

3 citations · 3 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CL2025

Detecting LLM-Generated Spam Reviews by Integrating Language Model Embeddings and Graph Neural Network

Xin Liu, Rongwu Xu, Xinyi Jia +4

The rise of large language models (LLMs) has enabled the generation of highly persuasive spam reviews that closely mimic human writing. These reviews pose significant challenges fo…

cs.AI2025

The Singapore Consensus on Global AI Safety Research Priorities

Yoshua Bengio, Tegan Maharaj, Luke Ong +84

Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to ensure that AI is safe, i.e., trustworthy,…

cs.AI2025

Does Chain-of-Thought Reasoning Really Reduce Harmfulness from Jailbreaking?

Chengda Lu, Xiaoyu Fan, Yu Huang +3

Jailbreak attacks have been observed to largely fail against recent reasoning models enhanced by Chain-of-Thought (CoT) reasoning. However, the underlying mechanism remains underex…

cs.AI2025

AI Awareness

Xiaojian Li, Haoyuan Shi, Rongwu Xu +1

Recent breakthroughs in artificial intelligence (AI) have brought about increasingly capable systems that demonstrate remarkable abilities in reasoning, language understanding, and…

cs.CL20253 cited

Nuclear Deployed: Analyzing Catastrophic Risks in Decision-making of Autonomous LLM Agents

Rongwu Xu, Xiaojian Li, Shuo Chen +1

Large language models (LLMs) are evolving into autonomous decision-makers, raising concerns about catastrophic risks in high-stakes scenarios, particularly in Chemical, Biological,…

cs.CL2024

LongRAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recall

Zehan Qi, Rongwu Xu, Zhijiang Guo +3

Retrieval-augmented generation (RAG) is a promising approach to address the limitations of fixed knowledge in large language models (LLMs). However, current benchmarks for evaluati…