I am a sophomore at University of Liverpool, majoring in Applied Mathematics, and I am expected to complete my BSc degree in 2029.
Concurrently, I serve as a Research Assistant at PremiLab, supervised by Prof. Qiufeng Wang, where I work on formal mathematics, agent systems, and reinforcement learning.
If you share similar research interests and would like to chat about research or collaboration, please feel free to contact me.
Understanding how (multimodal) large language models perform on physics problems requires benchmarks that reflect the difficulty and breadth of expert-level physical reasoning. Existing physics benchmarks remain limited in the following two important ways: (1) short of high-difficulty datasets, and (2) lack of comprehensive coverage of visual forms, knowledge points, and step-by-step solution processes. As a result, model performance on current datasets may not be fully representative of their ability to solve complex physics problems. To address these issues, we present PhysElite, a large-scale bilingual multimodal benchmark for Olympiad-level physics reasoning. PhysElite contains 11,586 Olympiad-tier problems. For each problem, we provide corresponding visual diagrams, step-by-step bilingual Chinese-English solution derivations, and the final answer. We benchmark 18 open-source and closed-source MLLMs, and find that even the strongest model reaches only 33.7% answer accuracy. We additionally conduct step-level process evaluation to diagnose where models fail in the reasoning chain. Our datasets are released at https://huggingface.co/datasets/physelite/PhysElite.
@inproceedings{anonymous2026physelite,title={PhysElite: How Far Are {LLM}s from Solving Olympiad-Level Physics Problems?},author={Anonymous},booktitle={The Fortieth Annual Conference on Neural Information Processing Systems Evaluations and Datasets Track},year={2026},url={https://openreview.net/forum?id=f3ceI8ijyu}}
MATH-AI
Dense Neuro-Symbolic Reasoning in a Unified Geometry State
Ruoran Xu, Wending Gao, Haoyu Cheng, Xiaoqiang Kang, and Qiufeng Wang
In The Sixth Workshop on Mathematical Reasoning and AI, 2026
