I am a second-year PhD student studying computer science at UC Santa Cruz, advised by Chenguang Wang. Previously, I completed my Bachelor’s degree in Computer Science and Mathematics, as well as a Master’s degree in Computer Science, both at Washington University in St. Louis. You can find my CV here.
My current research focuses on LLM post-training, agentic AI, and scaling test-time compute for hard-to-verify tasks. When not engaging in research, I’m frequently rock climbing 🧗.
selected publications
See the full list of publications. (*) denotes equal contribution.
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rLLM: A Framework for Post-Training Language Agents
Sijun Tan, Michael Luo, Colin Cai, Tarun Venkat, Kyle Montgomery, Aaron Hao, Tianhao Wu, Arnav Balyan, and 5 more authors
2025
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VMDT: Decoding the Trustworthiness of Video Foundation Models
Y Potter*, Z Wang*, N Crispino*, A Xiong*, K Montgomery*, F Pinto, E Chang, Y Chen, and 6 more authors
In Advances in Neural Information Processing Systems, 2025
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Humanity’s Last Exam
Long Phan, Alice Gatti, Ziwen Han, Nathaniel Li, Josephina Hu, Hugh Zhang, Chen Bo Calvin Zhang, Mohamed Shaaban, and 1101 more authors
2025
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JudgeBench: A Benchmark for Evaluating LLM-Based Judges
Sijun Tan*, Siyuan Zhuang*, Kyle Montgomery*, Willian Y. Tang, Alejandro Cuadron, Chenguang Wang, Raluca Ada Popa, and Ion Stoica
In Proceedings of the Thirteenth International Conference on Learning Representations, 2025
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Re-Tuning: Overcoming the Compositionality Limits of Large Language Models with Recursive Tuning
Eric Pasewark*, Kyle Montgomery*, Kefei Duan, Dawn Song, and Chenguang Wang
In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, 2024
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Agent Instructs Large Language Models to be General Zero-Shot Reasoners
Nicholas Crispino, Kyle Montgomery, Fankun Zeng, Dawn Song, and Chenguang Wang
In Proceedings of the Forty-first International Conference on Machine Learning, 2024
