WEREWOLF: Reputation-Aware Red-Teaming for Self-Organizing LLM Multi-Agent Systems
In Findings of the Association for Computational Linguistics: EMNLP 2026
EMNLP'2026
About
Profile
My background.
I am a Ph.D. candidate in Computer Science at City University of Hong Kong, advised by Prof. Jinhang Zuo. Previously, I worked as a research assistant with Prof. Sibo Wang at the Chinese University of Hong Kong from July to October 2024.
I received my bachelor’s degree in Computer Science and Technology from the School of the Gifted Young at the University of Science and Technology of China, where I was advised by Prof. Xue Chen and Prof. Jinhang Zuo.
- Citations
- 6
- h-index
- 1
- i10-index
- 0
- Conference papers
- 3
- Preprints
- 2
News
Recent research updates.
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Our paper WEREWOLF: Reputation-Aware Red-Teaming for Self-Organizing LLM Multi-Agent Systems was accepted to Findings of EMNLP 2026.
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Our paper One Rounding Fits All: Memory-Efficient Approximation Algorithms for Partition-Constrained Influence Maximization was accepted to KDD 2026.
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Our paper Fusing Reward and Dueling Feedback in Stochastic Bandits was accepted to ICML 2025.
Research Focus
I study sequential decision-making under limited, noisy, or hybrid feedback. My current work focuses on bandit learning and influence maximization.
- Learning Theory
- Influence Maximization
- Game Theory
- Multi-Agent Systems
Selected Publications
Selected current and recent work.
Service
Academic service and mentoring.
2026
Reviewer
ICLR and NeurIPS.
2025
Reviewer
ICML and NeurIPS.
2025 — 2026
Student Mentor
MSc Artificial Intelligence Program, CityUHK.
Teaching
I have served as a teaching assistant for programming, data structures, algorithms, and mathematical analysis courses.