Tu Trinh

According to our database, Tu Trinh authored at least 12 papers between 2022 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

HiL-Bench (Human-in-Loop Benchmark): Do Agents Know When to Ask for Help?

Mohamed Elfeki, Tu Trinh, Kelvin Luu, Guangze Luo, Nathan Hunt, Ernesto Montoya, Nandan Marwaha, Yannis He, Charles Wang, Fernando Crabedo, Alessa Castilo, Bing Liu

CoRR, April, 2026

2025

Learning to Coordinate with Experts.

Mohamad H. Danesh, Tu Trinh, Benjamin Plaut, Nguyen X. Khanh

CoRR, February, 2025

Probabilities of Chat LLMs Are Miscalibrated but Still Predict Correctness on Multiple-Choice Q&A.

Benjamin Plaut, Nguyen X. Khanh, Tu Trinh

Trans. Mach. Learn. Res., 2025

YRC-Bench: A Benchmark for Learning to Coordinate with Experts.

Mohamad H. Danesh, Khanh Xuan Nguyen, Tu Trinh, Benjamin Plaut

Trans. Mach. Learn. Res., 2025

Aligned LLMs Are Not Aligned Browser Agents.

Priyanshu Kumar, Elaine Lau, Saranya Vijayakumar, Tu Trinh, Elaine T. Chang, Vaughn Robinson, Shuyan Zhou, Matt Fredrikson, Sean M. Hendryx, Summer Yue, Zifan Wang

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Getting By Goal Misgeneralization With a Little Help From a Mentor.

Tu Trinh, Mohamad H. Danesh, Nguyen X. Khanh, Benjamin Plaut

CoRR, 2024

Refusal-Trained LLMs Are Easily Jailbroken As Browser Agents.

Priyanshu Kumar, Elaine Lau, Saranya Vijayakumar, Tu Trinh, Scale Red Team, Elaine T. Chang, Vaughn Robinson, Sean Hendryx, Shuyan Zhou, Matt Fredrikson, Summer Yue, Zifan Wang

CoRR, 2024

Softmax Probabilities (Mostly) Predict Large Language Model Correctness on Multiple-Choice Q&A.

Benjamin Plaut, Khanh Nguyen, Tu Trinh

CoRR, 2024

A StrongREJECT for Empty Jailbreaks.

Alexandra Souly, Qingyuan Lu, Dillon Bowen, Tu Trinh, Elvis Hsieh, Sana Pandey, Pieter Abbeel, Justin Svegliato, Scott Emmons, Olivia Watkins, Sam Toyer

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning.

Tu Trinh, Haoyu Chen, Daniel S. Brown

Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, 2024

2022

Efficient Game-Theoretic Planning With Prediction Heuristic for Socially-Compliant Autonomous Driving.

Chenran Li, Tu Trinh, Letian Wang, Changliu Liu, Masayoshi Tomizuka, Wei Zhan

IEEE Robotics Autom. Lett., 2022

Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning.

Tu Trinh, Daniel S. Brown

CoRR, 2022