Runyuan He

runyuanhe@link.cuhk.edu.cn

I am a junior undergraduate in Computer Science at The Chinese University of Hong Kong, Shenzhen, currently a visiting student at UC Berkeley through the Berkeley Global Access Program.

My research focuses on LLM-based database testing systems under the supervision of Prof. Alvin Cheung. I am actively seeking Ph.D. opportunities in database systems and software engineering.

Research Interests

Database Systems
Large Language Models (LLMs)

Selected Achievements

🥈 Silver Medalist - The 38th National Olympiad in Informatics (NOI 2021)
🌍 24th Place - The 48th ICPC World Final 2024
🏆 8th Place - The 48th ICPC World Final Huawei Challenge 2024
🥇 Gold Medalist - ICPC Regional 2023/2024

selected publications

arXiv
Automated Discovery of Test Oracles for Database Management Systems Using LLMs

Qiuyang Mang, Runyuan He^*, Suyang Zhong, and 3 more authors

arXiv preprint arXiv:2510.06663, 2025

Abs arXiv Bib

This paper explores the use of large language models (LLMs) to automate the discovery and instantiation of test oracles, addressing a long-standing bottleneck towards fully automated DBMS testing. We introduce Argus, a novel framework built upon the core concept of the Constrained Abstract Query. Argus uses LLMs to generate pairs of SQL skeletons that are asserted to be semantically equivalent, then formally proven using a SQL equivalence solver to ensure soundness.
@article{mang2025argus, title = {Automated Discovery of Test Oracles for Database Management Systems Using LLMs}, author = {Mang, Qiuyang and He, Runyuan and Zhong, Suyang and Liu, Xiaoxuan and Zhang, Huanchen and Cheung, Alvin}, journal = {arXiv preprint arXiv:2510.06663}, year = {2025}, }
arXiv
Continuum: Efficient and Robust Multi-Turn LLM Agent Scheduling with KV Cache Time-to-Live

Hanchen Li, Qiuyang Mang, Runyuan He^*, and 7 more authors

arXiv preprint arXiv:2511.02230, 2025

Abs arXiv Bib

We present Continuum, a serving system to optimize job completion time for multi-turn agent workloads by introducing time-to-live mechanism for KV cache retaining. For LLM request that generates a tool call, Continuum selectively pins the KV cache in GPU memory with a time-to-live value determined by considering both the reload cost and ordering preserve benefit of retaining KV cache.
@article{li2025continuum, title = {Continuum: Efficient and Robust Multi-Turn LLM Agent Scheduling with KV Cache Time-to-Live}, author = {Li, Hanchen and Mang, Qiuyang and He, Runyuan and Zhang, Qizheng and Mao, Huanzhi and Chen, Xiaokun and Zhou, Hangrui and Cheung, Alvin and Gonzalez, Joseph and Stoica, Ion}, journal = {arXiv preprint arXiv:2511.02230}, year = {2025}, }