Published in arXiv preprint, 2025
We develop a novel Bandit algorithm for rapidly identifying user preferences to improve LLM responses.
Recommended citation: Xiangxiang Dai, Yuejin Xie, Maoli Liu, Xuchuang Wang, Zhuohua Li, Huanyu Wang, John C.S. Lui. (2025). "Multi-Agent Conversational Online Learning for Adaptive LLM Response Identification." arXiv preprint arXiv:2501.01849. https://arxiv.org/abs/2501.01849
Published in NeurIPS 2025 D&B Track, 2025
We propose a benchmark for evaluating proactive risk awareness in multimodal language models.
Recommended citation: Youliang Yuan, Wenxiang Jiao, Yuejin Xie, Chihao Shen, Menghan Tian, Wenxuan Wang, Jen-tse Huang, Pinjia He. (2025). "Towards Evaluating Proactive Risk Awareness of Multimodal Language Models." NeurIPS 2025 Datasets and Benchmarks Track. https://arxiv.org/abs/2505.17455
Published in EMNLP 2025, 2025
We introduce ToolSafety, a safety fine-tuning dataset containing 5,668 direct harm samples, 4,311 indirect harm samples, and 4,311 multi-step samples to address safety vulnerabilities in tool-using AI systems.
Recommended citation: Yuejin Xie, Youliang Yuan, Wenxuan Wang, Fan Mo, Jianmin Guo, Pinjia He. (2025). "ToolSafety: A Comprehensive Dataset for Enhancing Safety in LLM-Based Agent Tool Invocations." Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://aclanthology.org/2025.emnlp-main.714/
Published in arXiv preprint, 2026
We propose AgentDoG, a diagnostic guardrail framework that provides fine-grained and contextual monitoring across agent trajectories, diagnosing root causes of unsafe actions.
Recommended citation: Dongrui Liu, ..., Yuejin Xie, et al. (2026). "AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security." arXiv preprint arXiv:2601.18491. https://arxiv.org/abs/2601.18491
Published in arXiv preprint, 2026
We propose a multi-agent framework that leverages code agents to autonomously evolve existing math problems into more complex variants while validating solvability and increased difficulty.
Recommended citation: Dadi Guo*, Yuejin Xie*, Qingyu Liu, Jiayu Liu, Zhiyuan Fan, Qihan Ren, Shuai Shao, Tianyi Zhou, Dongrui Liu, Yi R. Fung. (2026). "Code2Math: Can Your Code Agent Effectively Evolve Math Problems Through Exploration?" arXiv preprint arXiv:2603.03202. https://arxiv.org/abs/2603.03202