Hi! I am a fourth-year undergrad in the school of EECS, Peking University.
I worked on the intersection of cognitive science and language models. I hope to transform AI from a tool of interaction into a partner in symbiosis with humans.
I am fortunate to have worked with Professors Heng Ji, Tianmin Shu and Yizhou Wang. I am also deeply greatful to my amazing mentors, Wentao Zhu and Chi Han, for their invaluable guidance and support along the way.
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Peking UniversityB.S. in Computer ScienceSep. 2022 - Present -
Johns Hopkins UniversityVisiting StudentJune. 2024 - Sep. 2024
Honors & Awards
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Zhi-Class Scholarship2024
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Peking University Scholarship2023
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Award for Scientific Research, Peking University2023
News
‘AutoToM: Scaling Model-based Mental Inference via Automated Agent Modeling’ will be presented at NeurIPS 2025 as Spotlight!
‘Language Models Represent Belief of Self and Others’ is presented at ICML 2024
Publications - Not able to set up "Selected Pubs" yet :-) (view all )
AutoToM: Scaling Model-based Mental Inference via Automated Agent Modeling
Zhining Zhang*, Chuanyang Jin*, Mung Yao Jia*, Shunchi Zhang*, Tianmin Shu (* equal contribution)
Spotlight, Annual Conference on Neural Information Processing Systems (NeurIPS), 2025
We introduce AutoToM, an automated agent modeling method for scalable, robust, and interpretable mental inference. Leveraging an LLM as the backend, AutoToM combines the robustness of Bayesian models and the open-endedness of Language models, offering a scalable and interpretable approach to machine ToM.
AutoToM: Scaling Model-based Mental Inference via Automated Agent Modeling
Zhining Zhang*, Chuanyang Jin*, Mung Yao Jia*, Shunchi Zhang*, Tianmin Shu (* equal contribution)
Spotlight, Annual Conference on Neural Information Processing Systems (NeurIPS), 2025
We introduce AutoToM, an automated agent modeling method for scalable, robust, and interpretable mental inference. Leveraging an LLM as the backend, AutoToM combines the robustness of Bayesian models and the open-endedness of Language models, offering a scalable and interpretable approach to machine ToM.
Language models represent beliefs of self and others
Wentao Zhu, Zhining Zhang, Yizhou Wang
International Conference on Machine Learning (ICML) , 2024
We investigate belief representations in LMs: we discover that the belief status of characters in a story is linearly decodable from LM activations. We further propose a way to manipulate LMs through the activations to enhance their Theory of Mind performance.
Language models represent beliefs of self and others
Wentao Zhu, Zhining Zhang, Yizhou Wang
International Conference on Machine Learning (ICML) , 2024
We investigate belief representations in LMs: we discover that the belief status of characters in a story is linearly decodable from LM activations. We further propose a way to manipulate LMs through the activations to enhance their Theory of Mind performance.