About

I am a first-year Ph.D. student in Computer Science at Johns Hopkins University, advised by Tianmin Shu. I’m also a research intern in the Fundamental AI Research (FAIR) team at Meta, mentored by Jason Weston.

I received my B.A. in Honors Computer Science and B.A. in Mathematics from New York University in 2024, where I was awarded the Computer Science Prize for the Most Promising Student. I have also spent time as a research intern at MIT. I received an Outstanding Paper Award at ACL 2024 for my work on evaluation and methods for multimodal Theory of Mind..

I’m interested in developing AI systems with advanced social intelligence (ASI). My current areas of focus include:

Continual Reasoning about Humans: Developing AI systems that can continuously perceive, understand, reason about, and respond to the behavior and cognition of humans or other agents.

• In particular, my works automate agent modeling with LLM backends, advancing Theory of Mind reasoning by grounding it in cognitive theories, learning from multi-modal, multi-agent and multi-perspective observations, and scaling to long-term and complex settings.

Continual Learning from Humans: Building AI that continuously learns from human interactions—either through demonstrations, communications, feedback, or collaboration.

• I'm co-organizing RSS Workshop on Continual Robot Learning from Humans.

Besides academics, I enjoy playing Go, piano, badminton, hiking, and so much more.

News

• [June, 2025] Co-organized the Workshop on Continual Robot Learning from Humans at RSS 2025.
• [June, 2025] I was selected as a Notable Reviwer for ICLR 2025.
• [Jan, 2025] MuMA-ToM was accepted at AAAI 2025 as an Oral Presentation!
• [Sep, 2024] Invited talk @ University of Washinton on “Towards Multimodal Theory of Mind”.
• [Aug, 2024] MMToM-QA won the Outstanding Paper Award at ACL 2024! It was also covered by Futurity, Synced, and JHU News.

Recent Publications

(* denotes equal contribution; † denotes project lead)
For a more complete list, please see my publication page or Google Scholar page.

AutoToM: Scaling Model-based Mental Inference via Automated Agent Modeling Zhining Zhang*, Chuanyang Jin*†, Mung Yao Jia*, Tianmin Shu
Preprint
paper / project / code AutoToM is an automated agent modeling method for scalable, robust, and interpretable mental inference. It achieves SOTA on five benchmarks, produces human-like confidence estimates, and supports embodied decision-making.

OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis Qiushi Sun*, Kanzhi Cheng*, Zichen Ding*, Chuanyang Jin*, Yian Wang, Fangzhi Xu, Zhenyu Wu, Chengyou Jia, Liheng Chen, Zhoumianze Liu, Ben Kao, Guohao Li, Junxian He, Yu Qiao, Zhiyong Wu
ACL 2025 (Huggingface Daily Paper Top-1)
paper / project / code / model / data / slides OS-Genesis enables agents to actively explore web and mobile environments, facilitating the self-collection and self-annotation of high-quality training data.

MuMA-ToM: Multi-modal Multi-Agent Theory of Mind Haojun Shi*, Suyu Ye*, Xinyu Fang, Chuanyang Jin, Leyla Isik, Yen-Ling Kuo, Tianmin Shu
AAAI 2025 (Oral)
paper / project / code / benchmark / leaderboard / slides MuMA-ToM introduces a benchmark and method that extend MMToM-QA to multi-agent interaction scenarios.

MMToM-QA: Multimodal Theory of Mind Question Answering Chuanyang Jin, Yutong Wu, Jing Cao, Jiannan Xiang, Yen-Ling Kuo, Zhiting Hu, Tomer Ullman, Antonio Torralba, Joshua Tenenbaum, Tianmin Shu
ACL 2024 (Outstanding Paper Award)
paper / project / code / benchmark / leaderboard / slides / Futurity news / JHU news
Can machines understand people's minds from multimodal inputs? MMToM-QA introduces the first benchmark and a promising method that combines the flexibility of LLMs with the robustness of Bayesian inverse planning.

You can also take a look at my undergrad projects. A mountain of gratitude to those who have kindly mentored me and inspired me with their vision and passion!

Selected Honors & Awards

• Notable Reviewer, ICLR 2025
• Outstanding Paper Award, ACL 2024
• Presidential Honors Scholar and Summa cum Laude, New York University (Top 5%), 2024
• Computer Science Prize for the Most Promising Student, New York University (1 person/year), 2023
• Dean’s Undergraduate Research Fund, New York University, 2023
• COMAP International Scholarship Award (Top 0.1%), 2022
• MAA Award in Mathematical Contest in Modeling (Top 0.1%), 2022
• Bronze Medal of Shing-Tung Yau Computer Science Award (Top 1%), 2019
• Finalist of FIRST Robotics Competition World Championship (Top 0.2%), 2019
• NFLS Outstanding Student Leader Award and Zhou Enlai Scholarship (Top 1%), 2018
• First Prize of Chinese Mathematical Olympiad (Top 0.1%), 2018