publications

2025

1. 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},
   }

2. 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},
   }

3. arXiv

   FrontierCS: Evolving Challenges for Evolving Intelligence

   Qiuyang Mang, Wenhao Chai, Zhifei Li, and 21 more authors

   arXiv preprint arXiv:2512.15699, 2025

   Abs arXiv Bib

   We introduce FrontierCS, a challenging benchmark to test the CS research knowledge of frontier LLMs. FrontierCS includes 1,092 problems hand-written by domain experts, with 71.2% contributed by Turing Award and Best Paper Award winners.

   @article{mang2025frontiercs,
     title = {FrontierCS: Evolving Challenges for Evolving Intelligence},
     author = {Mang, Qiuyang and Chai, Wenhao and Li, Zhifei and Mao, Huanzhi and Zhou, Shang and Du, Alexander and Li, Hanchen and Liu, Shu and Chen, Edwin and Wang, Yichuan and Chu, Xieting and Cheng, Zerui and Xu, Yuan and Xia, Tian and Wang, Zirui and Shi, Tianneng and Yao, Jianzhu and Zhao, Yilong and Zhang, Qizheng and Ruan, Charlie and Shen, Zeyu and Liu, Kaiyuan and He, Runyuan and others},
     journal = {arXiv preprint arXiv:2512.15699},
     year = {2025},
   }

4. arXiv

   In-depth Analysis of Graph-based RAG in a Unified Framework

   Yingli Zhou, Yaodong Su, Youran Sun, and 8 more authors

   arXiv preprint arXiv:2503.04338, 2025

   Abs arXiv Bib

   This paper presents a comprehensive analysis of Graph-based Retrieval-Augmented Generation (RAG) systems within a unified framework.

   @article{zhou2025depth,
     title = {In-depth Analysis of Graph-based RAG in a Unified Framework},
     author = {Zhou, Yingli and Su, Yaodong and Sun, Youran and Wang, Shu and Wang, Taotao and He, Runyuan and Zhang, Yongwei and Liang, Sicong and Liu, Xilin and Ma, Yuchi and Fang, Yixiang},
     journal = {arXiv preprint arXiv:2503.04338},
     year = {2025},
   }