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. Liang-jian 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 harness for reasoning model system.
News
AIBuildAI ranks No.1 on MLE-bench!
UCSD Today News covers our work DreamPRM!
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 →AIBuildAI: An AI agent that automatically builds AI models
Ruiyi Zhang†, Peijia Qin†, Qi Cao†, Li Zhang†, Pengtao Xie
Arxiv Preprint
We present AIBuildAI, an AI agent that automatically builds AI models, with the goal of solving general AI tasks in an end-to-end manner.
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.