Xiaonan Luo

Computer Science and Engineering

University of Notre Dame

Notre Dame, IN 46556

I am a second-year PhD student at the Computer Science and Engineering (CSE) of the University of Notre Dame, where I am fortunate to be advised by Prof. Xiangliang Zhang. I received my Bachelor’s degree from the Hong Kong University of Science and Technology (HKUST).

Research Interests

My research interests lie in large language models (LLM) with a focus on:

LLM Post-Training & Evaluation: explores how to improve LLMs reasoning and the ability to orchestrate external tools and domain knowledge; emphasizes data-centric methods for improving the reliability, robustness, and evaluability of LLMs.
Multi-Agent Systems: investigates how multiple AI agents can collaborate, communicate, and coordinate to solve complex tasks that require diverse expertise and perspectives.
Scientific AI: studies how AI systems can accelerate scientific discovery in domains such as chemistry and the biomedical sciences.

selected publications

AAAI
Better datasets start from refinelab: Automatic optimization for high-quality dataset refinement

Xiaonan Luo^*, Yue Huang^*, Ping He, and 1 more author

In AAAI Conference on Artificial Intelligence, 2026

Abs arXiv Bib HTML

High-quality datasets are crucial for training effective machine learning models. This paper presents RefineLab, an automated framework for optimizing dataset refinement processes to improve data quality and model performance.
@inproceedings{luo2026refinelab, title = {Better datasets start from refinelab: Automatic optimization for high-quality dataset refinement}, author = {Luo, Xiaonan and Huang, Yue and He, Ping and Zhang, Xiangliang}, booktitle = {AAAI Conference on Artificial Intelligence}, year = {2026}, }
NeurIPS
AdaReasoner: Adaptive Reasoning Enables More Flexible Thinking

Xiangqi Wang, Yue Huang, Yanbo Wang, and 4 more authors

In Advances in Neural Information Processing Systems, 2025

Spotlight

Abs arXiv Bib HTML

AdaReasoner introduces an adaptive reasoning framework that enables large language models to dynamically adjust their reasoning strategies based on problem complexity and context.
@inproceedings{wang2025adareasoner, title = {AdaReasoner: Adaptive Reasoning Enables More Flexible Thinking}, author = {Wang, Xiangqi and Huang, Yue and Wang, Yanbo and Luo, Xiaonan and Guo, Kehan and Zhou, Yujun and Zhang, Xiangliang}, booktitle = {Advances in Neural Information Processing Systems}, year = {2025}, note = {Spotlight}, }

NeurIPS

ChemOrch: Empowering LLMs with Chemical Intelligence via Synthetic Instructions

Yue Huang, Zhengzhe Jiang, Xiaonan Luo, and 12 more authors

In Advances in Neural Information Processing Systems, 2025

Abs arXiv Bib HTML

@inproceedings{huang2025chemorch,
  title = {ChemOrch: Empowering LLMs with Chemical Intelligence via Synthetic Instructions},
  author = {Huang, Yue and Jiang, Zhengzhe and Luo, Xiaonan and Guo, Kehan and Zhuang, Haomin and Zhou, Yujun and Yuan, Zhengqing and Sun, Xiaoqi and Schleinitz, Jules and Wang, Yanbo and Zhang, Shuhao and Surve, Mihir and Chawla, Nitesh V and Wiest, Olaf and Zhang, Xiangliang},
  booktitle = {Advances in Neural Information Processing Systems},
  year = {2025},
}

ATC

Torpor: GPU-Enabled Serverless Computing for Low-Latency, Resource-Efficient Inference

Mingyu Yu, Ao Wang, Dong Chen, and 11 more authors

In USENIX Annual Technical Conference, 2025

Abs arXiv Bib HTML

@inproceedings{yu2025torpor,
  title = {Torpor: GPU-Enabled Serverless Computing for Low-Latency, Resource-Efficient Inference},
  author = {Yu, Mingyu and Wang, Ao and Chen, Dong and Yu, Haoxuan and Luo, Xiaonan and Li, Zhuzhong and Wang, Wei and Chen, Ruichuan and Nie, Dapeng and Yang, Kaiyuan and Chen, Xiaobing and Liu, Mingyang and Zhang, Yijia and Yang, Mao},
  booktitle = {USENIX Annual Technical Conference},
  year = {2025},
}