# About

I'm a PhD student at Purdue University, advised by Prof. Jing Gao and Prof. Xiaoqian Wang. My research focuses on the post-training and alignment of large language models (LLMs), with a recent emphasis on multi-agent systems for collaborative reasoning and decision-making. I develop methods that improve capability, trustworthiness, and structural safety. My work has appeared in top-tier venues (e.g., ICLR, COLM, and ACL) and garnered .

# News

• [Mar 2026] I will be joining Microsoft Research - Deep Learning Group this upcoming summer as a Research Intern, see you again in Seattle!
• [Feb 2026] We introduce The Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems, a text-free communication framework that uses a Universal Visual Codec to enable efficient latent-space transfer across heterogeneous VLM agents. Code is available here.
• [Jan 2026] We release tokenforge, exposing tokenizer transplant as a critical supply-chain attack surface in modular AI. We demonstrate how the standard merging pipeline can be exploited to inject "sleeper" backdoors that remain mathematically inert in donor models but activate downstream.
• [Jul 2025] SUV, a scalable copyright compliance framework for large language models (LLMs) with regularized selective unlearning, is accepted by COLM 2025.
• [May 2025] I joined AWS AI Fundamental Research Team as an Applied Scientist Intern for Summer 2025.
• [May 2025] I will present a portion of the tutorial, LLMs and Copyright Risks: Benchmarks and Mitigation Approaches, at NAACL 2025.
• [Jan 2025] FedBiscuit accepted by ICLR 2025.
• [Jan 2025] CausalEval accepted by NAACL 2025.
• [Jan 2025] Survey on Factuality in Large Language Models accepted by ACM Computing Surveys.
• [Dec 2024] GraphEval accepted by IEEE Data Engineering Bulletin.
• [Nov 2024] Listed as one of the Top Reviewers for NeurIPS 2024.
• [Sep 2024] Two Papers Accepted by EMNLP 2024 Main Conference: SHIELD and SaySelf. See you in Miami, Florida!

# Recent Publications

1. The Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems. Xiaoze Liu, Ruowang Zhang, Weichen Yu, Siheng Xiong, Liu He, Feijie Wu, Hoin Jung, Matt Fredrikson, Xiaoqian Wang, Jing Gao. Code

2. The Trojan in the Vocabulary: Stealthy Sabotage of LLM Composition. Xiaoze Liu, Weichen Yu, Matt Fredrikson, Xiaoqian Wang, Jing Gao

3. SUV: Scalable Large Language Model Copyright Compliance with Regularized Selective Unlearning. Tianyang Xu#, Xiaoze Liu#, Feijie Wu, Xiaoqian Wang, Jing Gao, The 2025 Conference on Language Modeling (COLM), 2025.

4. SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generation. Xiaoze Liu#, Ting Sun#, Tianyang Xu, Feijie Wu, Cunxiang Wang, Xiaoqian Wang, Jing Gao, The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024.

5. Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity. Cunxiang Wang#, Xiaoze Liu#, Yuanhao Yue#, Qipeng Guo, Xiangkun Hu, Xiangru Tang, Tianhang Zhang, Jiayang Cheng, Yunzhi Yao, Wenyang Gao, Xuming Hu, Zehan Qi, Yidong Wang, Linyi Yang, Jindong Wang, Xing Xie, Zheng Zhang, Yue Zhang, ACM Computing Surveys (CSUR), 2025

6. Towards Federated RLHF with Aggregated Client Preference for LLMs. Feijie Wu, Xiaoze Liu, Haoyu Wang, Xingchen Wang, Lu Su, Jing Gao, Thirteenth International Conference on Learning Representations (ICLR), 2025

7. Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs. Xiaoze Liu, Feijie Wu, Tianyang Xu, Zhuo Chen, Yichi Zhang, Xiaoqian Wang, Jing Gao IEEE Data Engineering Bulletin, 2024

8. SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales. EMNLP 2024 Tianyang Xu, Shujin Wu, Shizhe Diao, Xiaoze Liu, Xingyao Wang, Yangyi Chen, Jing Gao, The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024.

9. CausalEval: Towards Better Causal Reasoning in Language Models. Longxuan Yu, Delin Chen, Siheng Xiong, Qingyang Wu, Qingzhen Liu, Dawei Li, Zhikai Chen, Xiaoze Liu, Liangming Pan, The 2025 Annual Conference of the Nations of the Americas Chapter of the ACL (NAACL), 2025

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(Google Scholar)

# Education and Experience

• PhD Student, Purdue University, 2023 - Present
  • Advised by Prof. Jing Gao and Prof. Xiaoqian Wang
  • Applied Scientist Intern, AWS AI Fundamental Research Team, Summer 2025
• Master's Degree, Zhejiang University, 2020 - 2023,
  • Awarded Outstanding Master’s Thesis of Zhejiang Province (122 out of 35,149, Top 0.3%), and Excellent Postgraduate student's Award of Zhejiang University.
  • Research Intern, AliCloud & Alibaba DAMO Academy, 2021-2023
• Bachelor's Degree, Northeastern University, 2016 - 2020
  • Awarded the National Scholarship of China (Top 0.2%) twice during undergraduate studies, and the Outstanding Graduate of Northeastern University in 2020.
  • Intern, ByteDance, 2019 - 2020
  • Exchange Student, Kanazawa University, 2019, Supported by JASSO Scholarship

# Academic Services

Serve as a reviewer/PC for

• 2026: ICML, ACL Rolling Review, KDD
• 2025: ICLR, ICML, NeurIPS, ACL Rolling Review, KDD
• 2024: NeurIPS, ACM MM, SDM, CIKM, ISWC, ACL Rolling Review, KDD
• 2023: NeurIPS, EMNLP, KDD

Served as a Journal reviewer for Transactions on Machine Learning Research, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Neural Networks and Learning Systems, Pattern Recognition, Information Sciences, IEEE Transactions on Big Data