Rui Wang (王瑞)

Computer Science & Artificial Intelligence

I am a third-year undergraduate student at The Hong Kong University of Science and Technology (HKUST), pursuing a Bachelor of Engineering in Computer Science with an Extended Major in Artificial Intelligence. I completed a semester exchange at The University of Texas at Austin (UT Austin) in May 2026. I am currently working at AQUMON (Shenzhen) as an AI Large Language Model Research Intern through the HKUST CSE Co-op Program. I am also an incoming PhD student of Professor Yangqiu Song at CSE, HKUST.

My research interests broadly lie in Large Language Models (LLMs) and their reasoning capabilities. Specifically, I focus on:

  • Decisions under risk and uncertainty
  • Confidence calibration
  • Robustness and adaptability

Feel free to reach out.

I always feel lucky to meet great mentors along my journey, including and not limited to, Mr. Jiayu Liu, Ms. Qing Zong.

🔥 News

Aug 2026
🎉 3 Papers Accepted to EMNLP 2026. Huge thanks to all collaborators!
Jun 2026
💼 Currently interning at AQUMON (Shenzhen) as an AI Large Language Model Research Intern through the HKUST CSE Co-op Program, under the supervision of Prof. Yangqiu Song.
Apr 2026
🚀 Planning to start HKUST UG AI Lab with collaborator Jiayu Liu.
Jan 2026
✈️ Started semester exchange at The University of Texas at Austin.

📚 Publications

PlanBench-XL: Evaluating Long-Horizon Planning of LLM Tool-Use Agents in Large-Scale Tool Ecosystems

EMNLP 2026 (Main) May 2026

Jiayu Liu, Qihan Lin, Cheng Qian, Rui Wang, Emre Can Acikgoz, Xiaocheng Yang, Jiateng Liu, Zhenhailong Wang, Xiusi Chen, Heng Ji, Dilek Hakkani-Tur

TL;DR: PlanBench-XL is an interactive benchmark with 327 retail tasks and 1,665 tools, showing that even leading LLM agents struggle with long-horizon planning in large, imperfect tool ecosystems.

Rethinking Prospect Theory for LLMs: Revealing the Instability of Decision-Making under Epistemic Uncertainty

EMNLP 2026 (Main) Apr 2026

Rui Wang, Qihan Lin, Jiayu Liu, Qing Zong, Tianshi Zheng, Dadi Guo, Haochen Shi, Weiqi Wang, Yangqiu Song

TL;DR: A three-stage evaluation grounded in Prospect Theory shows that LLM decision-making is not reliably described by the framework and becomes especially unstable under epistemic uncertainty.

NOVA: NOise-aware Verbal Confidence CAlibration for Robust Large Language Models in RAG Systems

EMNLP 2026 (Findings) Jan 2026

Jiayu Liu, Rui Wang, Qing Zong, Yumeng Wang, Cheng Qian, Qingcheng Zeng, Tianshi Zheng, Haochen Shi, Dadi Guo, Baixuan Xu, Chunyang Li, Yangqiu Song

TL;DR: NOVA is a noise-aware verbal confidence calibration framework that substantially improves the reliability of RAG systems when retrieved evidence is contradictory or irrelevant.

📝 Under Review

Co-Evolution in Agentic Systems: Toward Self-Directed Evolution Beyond Human Design

Under Review Aug 2026

Qing Zong, Jiayu Liu, Junhao Shen, Zecong Tang, Linsi Wu, Yuxuan Liu, Rui Wang, Zhaowei Wang, Weiqi Wang, Cheng Qian, Xiusi Chen, Yangqiu Song

TL;DR: This survey proposes a progressive three-stage taxonomy of co-evolution in agentic systems — agent–agent, agent–environment, and meta co-evolution — laying a unified foundation for open-ended systems that improve beyond fixed human-designed paths.

🏫 Education

The Hong Kong University of Science and Technology

Sep 2023 - May 2027

Bachelor of Engineering in Computer Science - Extended Major in AI

Cumulative GPA: 3.983 / 4.30
Major CGPA: 4.073 / 4.30
Honors: Dean's List (All Semesters)
Scholarship: University Continuing Scholarship (Total 60,000 HKD)

The University of Texas at Austin

Jan 2026 - May 2026

Semester Exchange, Electronic and Computer Engineering

Coursework: Software Engineering, Machine Learning & Data Analytics for Edge AI

University College London

Jul 2024

Summer School

Programme: Short-term summer programme

💼 Internships

AQUMON

Jun 2026 - Present

AI Large Language Model Research Intern, Shenzhen

Program: HKUST CSE Co-op Program
Supervisor: Prof. Yangqiu Song
Focus: Large language models, reasoning, and applied AI research

🤝 Services

Teaching Assistant

HKUST

COMP1021: Introduction to Computer Science

Designed coding assignments and conducted tutorials for 50+ first-year undergraduates
Received positive feedback for clarifying complex CS concepts

Peer Mentor

HKUST

CSE Peer Mentor Program

Provided academic guidance and career planning advice to junior students
Fostered a collaborative learning environment

🔗 Connect

Research Figure