About
I am Qi Cao, a 2nd-year Ph.D. student in the Department of Electrical and Computer Engineering at the University of California, San Diego, advised by Prof. Pengtao Xie.
Previously, I received my B.S. in Mathematics and Physics Class from the Yingcai Honors School at the University of Electronic Science and Technology of China, where I was fortunate to be advised by Prof. Liangjian Deng.
I will join Meta as a research scientist intern in Summer 2026. My current research focuses on large language model (LLM) reasoning and building LLM-based reasoning systems.
News
We built a project page for SCOPE.
I will join Meta as a research scientist intern in Summer 2026!
Starting my PhD at UCSD.
Selected Publications
View All →Models Under SCOPE: Scalable and Controllable Routing via Pre-hoc Reasoning
Qi Cao†, Shuhao Zhang†, Ruizhe Zhou, Ruiyi Zhang, Peijia Qin, Pengtao Xie
Arxiv Preprint
SCOPE, a model routing framework that predicts how accurate and how expensive each model will be before running it, allowing users to control cost-accuracy trade-offs and naturally handle new models.
DreamPRM-1.5: Unlocking the Potential of Each Instance for Multimodal Process Reward Model Training
Qi Cao, Pengtao Xie
Arxiv Preprint
An instance-reweighting updated version of DreamPRM, higher accuracy and more robust.
DreamPRM: Domain-Reweighted Process Reward Model for Multimodal Reasoning
Qi Cao, Ruiyi Wang, Ruiyi Zhang, Sai Ashish Somayajula, Pengtao Xie
The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS)
A multimodal Process Reward Model (PRM) trained with domain-reweighting. Top 1 method on MathVista, MMMU & R-Bench-V.
Bidomain Modeling Paradigm for Pansharpening
Junming Hou†, Qi Cao†, Ran Ran, Che Liu, Junling Li, Liang-jian Deng
Proceedings of the 31st ACM international conference on multimedia (ACM MM)
We propose BiPan, a bidomain pansharpening framework that models band-specific local spectral features and global spatial details in the Fourier domain, achieving state-of-the-art performance by better handling spectral diversity and MS image degradation.
Zero-shot Semi-supervised Learning for Pansharpening
Qi Cao, Liang-Jian Deng, Wu Wang, Junming Hou, Gemine Vivone
Information Fusion
Zero-shot pansharpening (ZS-Pan) only requires a single pair of PAN/LRMS images. Any pansharpening network can take the ZS-Pan as a plug-and-play module. A two-phase three-component semi-supervised model is designed for ZS-Pan.