Bowen Zuo

Riverside, CA

Bowen Zuo is a third-year Ph.D. student in Electrical and Computer Engineering at the University of California, Riverside, advised by professor Yinglun Zhu. His research focuses on large language models, with a particular emphasis on test-time scaling. He is currently working on diffusion large language models.

Prior to his Ph.D., he received his M.S. in Computer Science from the Georgia Institute of Technology, working with professor Larry Heck on digital humans, and his B.S. in Electrical and Computer Engineering from the University of Washington, where he worked at ACME Lab with professor Scott Hauck and professor Shih-Chieh Hsu on high-level synthesis and machine learning acceleration for Physics discovery.

He is always happy to discuss potential collaborations, whether on test-time scaling, diffusion language models, or related topics in large language models. Feel free to reach out by email if you’d like to connect.

selected publications

ACL Findings
Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations

Bowen Zuo, D. Zhou, and Yinglun Zhu

Findings of the Association for Computational Linguistics (ACL), 2026

Abs arXiv Bib HTML

Introduces a test-time compute allocation framework that jointly adapts where computation is spent and how generation is performed, conditioning generation on successful responses from semantically related queries.
@article{zuo2026adaptive, title = {Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations}, author = {Zuo, Bowen and Zhou, D. and Zhu, Yinglun}, journal = {Findings of the Association for Computational Linguistics (ACL)}, year = {2026}, }
ICLR
Strategic Scaling of Test-Time Compute: A Bandit Learning Approach

Bowen Zuo and Yinglun Zhu

In International Conference on Learning Representations (ICLR), 2026

Abs arXiv Bib HTML

Formulates test-time compute allocation as a bandit learning problem, proposing adaptive algorithms that estimate query difficulty on the fly and allocate more compute to harder queries.
@inproceedings{zuo2026strategic, title = {Strategic Scaling of Test-Time Compute: A Bandit Learning Approach}, author = {Zuo, Bowen and Zhu, Yinglun}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2026}, }