I am a 2nd-year CS Ph.D. student at Rutgers University, advised by Prof. Ruixiang (Ryan) Tang. Before Rutgers, I completed my master's and bachelor's degrees at the SIGMA Lab of Wuhan University, advised by Prof. Zengmao Wang and Prof. Bo Du.
My research focuses on trustworthy agentic AI and LLM post-training and reasoning. I study how to audit autonomous agents as they act, predict failures early, and make an LLM's confidence better reflect the correctness of its reasoning. I also work on reliable visual perception, including out-of-distribution detection and distinguishing AI-generated images from real ones, and explore ML applications in healthcare such as immunology and cell morphology.
🔥 If you share these research interests or have feedback on my previous work, feel free to drop me an email or add me on WeChat. I am always delighted to discuss potential collaborations!
💼 I am currently seeking research internship opportunities for Summer 2027. Feel free to reach out and check out my CV.
Selected work — click through to explore each project.
Boxuan Zhang*, Jianing Zhu*, Zeru Shi, Dongfang Liu, Ruixiang Tang
We reframe agentic failure analysis from post-hoc attribution on completed trajectories to online auditing on unfolding prefixes, where an auditor commits a continue-or-alarm verdict at every step.
Boxuan Zhang, Jianing Zhu, Qifan Wang, Jiang Liu, Ruixiang Tang
We propose Micro-Defects expose Macro-Fakes (MDMF), a local distribution-aware detection framework that amplifies micro-scale statistical irregularities into macro-level distributional discrepancies for AI-generated image detection.
Boxuan Zhang*, Jianing Zhu*, Zengmao Wang, Tongliang Liu, Bo Du, Bo Han
Propose a novel perspective to employ different common corruptions on the input space to expand the representation dimension for out-of-distribution detection.
