Zhimeng Guo

Brief Bio

I am a graduate student at Penn State, specializing in LLM for code generation and LLM agents for long-horizon problem solving. I am interested in solving practical real-world problems using LLMs. I consider two main approaches for leveraging LLM capabilities: (1) agentic approaches to decompose complex problems into smaller ones and solve them systematically; (2) designing environments and using RL and SFT post-training to enhance LLM capabilities. My research focuses on RL and agentic system design.

2021 - Present: Graduate student at Penn State, advised by Prof. Minhao Cheng.
2017 - 2021: B.Sc. at UESTC, advised by Prof. Lianli Gao and Prof. Linyuan Lv.

Code Repos

ContextAgent: An lightweight multi-agent framework for context-driven system design.
Sleepless Agent: An agentOS for long-horizon problem solving.

Selected Publications

Optimizing Token Choice for Code Watermarking: An RL Approach
Zhimeng Guo, Huaisheng Zhu, Siyuan Xu, Hangfan Zhang, Teng Xiao, Minhao Cheng. arXiv, 2025.
[bib] [paper]

      @misc{guo2025optimizing,
        title={Optimizing Token Choice for Code Watermarking: An RL Approach}, 
        author={Zhimeng Guo and Huaisheng Zhu and Siyuan Xu and Hangfan Zhang and Teng Xiao and Minhao Cheng},
        year={2025},
        eprint={2508.11925},
        archivePrefix={arXiv},
        primaryClass={cs.CR},
        url={https://arxiv.org/abs/2508.11925}, 
    }

Practical and Effective Code Watermarking for Large Language Models
Zhimeng Guo, Minhao Cheng. NeurIPS, 2025.
[bib] [paper] [code]

      @inproceedings{
        guo2025practical,
        title={Practical and Effective Code Watermarking for Large Language Models},
        author={Zhimeng Guo and Minhao Cheng},
        booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
        year={2025},
        url={https://openreview.net/forum?id=RpE4HeuX69}
        }

Jailbreak Open-Sourced Large Language Models via Enforced Decoding
Hangfan Zhang, Zhimeng Guo, Huaisheng Zhu, Bochuan Cao, Lu Lin, Jinyuan Jia, Jinghui Chen, Dinghao Wu. ACL, 2024.
[bib] [paper]

    @inproceedings{zhang2024jailbreak,
      title={Jailbreak open-sourced large language models via enforced decoding},
      author={Zhang, Hangfan and Guo, Zhimeng and Zhu, Huaisheng and Cao, Bochuan and Lin, Lu and Jia, Jinyuan and Chen, Jinghui and Wu, Dinghao},
      booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
      pages={5475--5493},
      year={2024}
    }

Addressing shortcomings in fair graph learning datasets: Towards a new benchmark
Xiaowei Qian*, Zhimeng Guo*, Jialiang Li, Haitao Mao, Bingheng Li, Suhang Wang, Yao Ma. KDD, 2024.
[bib] [paper]

      @inproceedings{qian2024addressing,
        title={Addressing shortcomings in fair graph learning datasets: Towards a new benchmark},
        author={Qian, Xiaowei and Guo, Zhimeng and Li, Jialiang and Mao, Haitao and Li, Bingheng and Wang, Suhang and Ma, Yao},
        booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
        pages={5602--5612},
        year={2024}
      }

Towards Fair Graph Neural Networks via Graph Counterfactual.
Zhimeng Guo, Jialiang Li, Teng Xiao, Yao Ma, and Suhang Wang. CIKM, 2023.
[bib] [paper] [code]

@article{guo2023towards,
  title={Towards Fair Graph Neural Networks via Graph Counterfactual},
  author={Guo, Zhimeng and Li, Jialiang and Xiao, Teng and Ma, Yao and Wang, Suhang},
  journal={arXiv preprint arXiv:2307.04937},
  year={2023}
}

Efficient Contrastive Learning for Fast and Accurate Inference on Graphs.
Teng Xiao, Huaisheng Zhu, Zhiwei Zhang, Zhimeng Guo, Charu C Aggarwal, Suhang Wang, Vasant G Honavar. ICML, 2024.
[bib] [paper]

    @inproceedings{xiao2024efficient,
      title={Efficient contrastive learning for fast and accurate inference on graphs},
      author={Xiao, Teng and Zhu, Huaisheng and Zhang, Zhiwei and Guo, Zhimeng and Aggarwal, Charu C and Wang, Suhang and Honavar, Vasant G},
      booktitle={Forty-first International Conference on Machine Learning},
      year={2024}
    }

Counterfactual Learning on Graphs: A Survey.
Zhimeng Guo, Teng Xiao, Charu Aggarwal, Hui Liu, and Suhang Wang. arXiv, 2023.
[bib] [paper] [code]

@misc{guo2023counterfactual,
  title={Counterfactual Learning on Graphs: A Survey}, 
  author={Zhimeng Guo and Teng Xiao and Charu Aggarwal and Hui Liu and Suhang Wang},
  year={2023},
  eprint={2304.01391},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}

Fairness-aware Message Passing for Graph Neural Networks.
Huaisheng Zhu, Guoji Fu, Zhimeng Guo, Zhiwei Zhang, Teng Xiao, and Suhang Wang. arXiv, 2023.
[bib] [paper]

@article{zhu2023fairnessaware,
  author = {Zhu, Huaisheng and Fu, Guoji and Guo, Zhimeng and Zhang, Zhiwei and Xiao, Teng and Wang, Suhang},
  journal = {arXiv},
  year = {2023},
  title = {Fairness-aware Message Passing for Graph Neural Networks},
  volume = {abs/2306.11132},
}

Decoupled Self-supervised Learning for Graphs.
Teng Xiao, Zhengyu Chen, Zhimeng Guo, Zeyang Zhuang, and Suhang Wang. NeurIPS, 2022.
[bib] [paper]

@inproceedings{xiao2022decoupled,
  author = {Xiao, Teng and Chen, Zhengyu and Guo, Zhimeng and Zhuang, Zeyang and Wang, Suhang},
  booktitle = {Conference on Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  pages = {620--634},
  title = {Decoupled Self-supervised Learning for Graphs},
  volume = {35},
}

Teaching

IST 815 Foundations of Information Security and Assurance, Fall 2025.
DS 560 Principles of Causal Inference, Spring 2025.
CYBER 362 Security Analytics Studio, Fall 2024.
DS 330 Visual Analytics, Spring 2024.
IST 597 Data Science for Researchers and Scholars, Fall 2023.

Awards / Acknowledgements / Services

Reviewer and subreviewer for AISTATS, NeurIPS, ICLR, ICML.
Outstanding Undergraduate Thesis Award.
Ranked 46/4103 in IEEEXtreme 24-Hour Programming Competition.
Meritorious Winner in 2019 MCM/ICM Mathematical Contest in Modeling.

Last update on Sep 25th, 2025.