My current research focuses on post-training for multimodal foundation models, particularly
on-policy distillation for flow matching models. I aim to build post-training algorithms on
a statistical machine learning framework, and validate them through large-scale experiments
and evals to understand when and why they remain reliable at foundation-model scale.
News ๐ข
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Sep 2026
Starting my Ph.D. at the School of Mathematical Sciences,
Shanghai Jiao Tong University, supervised by Prof. Mingyang Ren.
Jul 2026
HYSET is on arXiv and under review at
AAAI 2026: set-level tool retrieval for LLM agents, cast as query-conditioned hyperedge
prediction. Code, model, and a live demo are released.
Jun 2026
Graduated from East China Normal University with the
Outstanding Undergraduate Thesis Award and the
Outstanding Undergraduate Graduate honor.
May 2026
HYVINT, our intensity-driven hypergraph
generative model, is on arXiv and under review at AAAI 2026.
Apr 2026
Joined the Department of Data Science and Artificial Intelligence, PolyU, as a research
assistant with Prof. Binyan Jiang.
Publications ๐
* corresponding author
Tools Are Not Islands: Set-Level Tool Retrieval for LLM Agents via Query-Conditioned Hyperedge Prediction
LanguagesMandarin (native), English (professional), Cantonese (conversational), Japanese (beginner)
GitHub activity
Contact ๐
I welcome discussions and potential collaborations on post-training for generative
foundation models. Please feel free to contact me at
xinyi.hong@sjtu.edu.cn.