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I am currently in the MSCS program at UIUC, advised by Prof. Jiaxuan You. I am also a researcher in the MIT Brain and Cognitive Sciences department, working with Prof. Evelina Fedorenko and Dr. Andrea de Varda in the EvLab. I received my B.A. in Mathematics and Computer Science from Carleton College, a leading liberal arts college in the US. During my undergrad, I was fortunate to work with Prof. Anima Anandkumar and Dr. Rafał Kocielnik in the Anima AI+Science Lab at Caltech. I also previously interned at NVIDIA. 韩芃睿 / Email / Google Scholar / GitHub / LinkedIn / Twitter |
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[Apr 2026] 🔥 Our paper The Personality Illusion is accepted to ICML. See you in Seoul! |
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My research aims to advance scientific understanding of AI (especially neural models like LLMs), and more broadly, the general principles of intelligence and intelligent behavior. I approach this goal across three interconnected levels:
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Pengrui Han*, Rafal D. Kocielnik*, Peiyang Song, Ramit Debnath, Dean Mobbs, Anima Anandkumar, and R. Michael Alvarez (* Equal Contribution) International Conference on Machine Learning (ICML), 2026 NeurIPS LAW Workshop: Bridging Language, Agent, and World Models, 2025, Best Paper Honorable Mention arXiv / project / code / media LLMs say they have personalities, but they don’t act like it. Alignment today shapes language, not behavior. This linguistic–behavioral dissociation cautions against equating coherent self-reports with cognitive depth. |
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Peiyang Song*, Pengrui Han*, and Noah Goodman (* Equal Contribution) Transactions on Machine Learning Research (TMLR), 2026, Survey Certificate arXiv / code / proceeding / media We present the first comprehensive survey dedicated to reasoning failures in LLMs. By unifying fragmented research efforts, our survey provides a structured perspective on systemic weaknesses in LLM reasoning, offering valuable insights and guiding future research towards building stronger, more reliable, and robust reasoning capabilities. |
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Pengrui Han*, Peiyang Song*, Haofei Yu, and Jiaxuan You (* Equal Contribution) Findings of Empirical Methods in Natural Language Processing (EMNLP), 2024 arXiv / code / proceeding Motivated by the crucial cognitive phenomenon of A-not-B errors, we present the first systematic evaluation on the surprisingly vulnerable inhibitory control abilities of LLMs. We reveal that this weakness undermines LLMs' trustworthy reasoning capabilities across diverse domains, and introduce various mitigations. |
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Pengrui Han*, Rafal Kocielnik*, Adhithya Saravanan,Roy Jiang, Or Sharir,and Anima Anandkumar (* Equal Contribution) Conference On Language Modeling (COLM), 2024 arXiv / code / proceeding We propose a light and efficient pipeline that enables both domain and non-domain experts to quickly generate synthetic debiasing data to mitigate specific or general bias in their models with parameter-efficient fine-tuning. |
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Guanyu Lin*, Tao Feng*, Pengrui Han*, Ge Liu, Jiaxuan You (* Equal Contribution) System Demonstration Track of Empirical Methods in Natural Language Processing (EMNLP), 2024 Huggingface Live Demo: Link We propose a light and efficient pipeline that enables both domain and non-domain experts to quickly generate synthetic debiasing data to mitigate specific or general bias in their models with parameter-efficient fine-tuning. |
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