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

AgentZ: An multi-agent framework for open-world problem solving.
Code Evaluator: A repo for evaluating code generation models.

Publications

Jailbreak Open-Sourced Large Language Models via Enforced Decoding
Hangfan Zhang, Zhimeng Guo, Huaisheng Zhu, Bochuan Cao, Lu Lin, Jinyuan Jia, Jinghui Chen, and 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] [code]

    @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},
}

Label-Wise Graph Convolutional Network for Heterophilic Graphs.
Enyan Dai, Shijie Zhou, Zhimeng Guo, and Suhang Wang. LoG, 2022.
[bib] [paper] [code]

@article{dai2022comprehensive,
  title={A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability},
  author={Dai, Enyan and Zhao, Tianxiang and Zhu, Huaisheng and Xu, Junjie and Guo, Zhimeng and Liu, Hui and Tang, Jiliang and Wang, Suhang},
  journal={arXiv preprint arXiv:2204.08570},
  year={2022}
}

Link Prediction on Heterophilic Graphs via Disentangled Representation Learning.
Shijie Zhou, Zhimeng Guo, Charu Aggarwal, Xiang Zhang, and Suhang Wang. arXiv, 2022.
[bib] [paper] [code]

@article{zhou2022link,
  author = {Zhou, Shijie and Guo, Zhimeng and Aggarwal, C. and Zhang, Xiang and Wang, Suhang},
  journal = {arXiv},
  year = {2022},
  title = {Link Prediction on Heterophilic Graphs via Disentangled Representation Learning},
  volume = {abs/2208.01820},
}

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability.
Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, and Suhang Wang. arXiv, 2022.
[bib] [paper] [code]

@article{dai2022a,
  author = {Dai, Enyan and Zhao, Tianxiang and Zhu, Huaisheng and Xu, Junjie and Guo, Zhimeng and Liu, Hui and Tang, Jiliang and Wang, Suhang},
  journal = {arXiv},
  year = {2022},
  title = {A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability},
  volume = {abs/2204.08570},
}

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.