Yuxuan Li 李宇轩

@article{li2025makes,
  title={What Makes LLM Agent Simulations Useful for Policy? Insights From an Iterative Design Engagement in Emergency Preparedness},
  author={Li, Yuxuan and Das, Sauvik and Shirado, Hirokazu},
  journal={arXiv preprint arXiv:2509.21868},
  year={2025}
}

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@inproceedings{li-shirado-2025-spontaneous,
    title = "Spontaneous Giving and Calculated Greed in Language Models",
    author = "Li, Yuxuan  and
      Shirado, Hirokazu",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.emnlp-main.267/",
    doi = "10.18653/v1/2025.emnlp-main.267",
    pages = "5271--5286",
    ISBN = "979-8-89176-332-6",
    abstract = "Large language models demonstrate strong problem-solving abilities through reasoning techniques such as chain-of-thought prompting and reflection. However, it remains unclear whether these reasoning capabilities extend to a form of social intelligence: making effective decisions in cooperative contexts. We examine this question using economic games that simulate social dilemmas. First, we apply chain-of-thought and reflection prompting to GPT-4o in a Public Goods Game. We then evaluate multiple off-the-shelf models across six cooperation and punishment games, comparing those with and without explicit reasoning mechanisms. We find that reasoning models consistently reduce cooperation and norm enforcement, favoring individual rationality. In repeated interactions, groups with more reasoning agents exhibit lower collective gains. These behaviors mirror human patterns of ``spontaneous giving and calculated greed.'' Our findings underscore the need for LLM architectures that incorporate social intelligence alongside reasoning, to help address{---}rather than reinforce{---}the challenges of collective action."
}

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@inproceedings{li2025actions,
  title={Actions speak louder than words: Agent decisions reveal implicit biases in language models},
  author={Li, Yuxuan and Shirado, Hirokazu and Das, Sauvik},
  booktitle={Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency},
  pages={3303--3325},
  year={2025}
}

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@article{li2025assessing,
  title={Assessing Collective Reasoning in Multi-Agent LLMs via Hidden Profile Tasks},
  author={Li, Yuxuan and Naito, Aoi and Shirado, Hirokazu},
  journal={arXiv preprint arXiv:2505.11556},
  year={2025}
}

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@article{pan2024human,
  title={A human-computer collaborative tool for training a single large language model agent into a network through few examples},
  author={Pan, Lihang and Li, Yuxuan and Yu, Chun and Shi, Yuanchun},
  journal={arXiv preprint arXiv:2404.15974},
  year={2024}
}

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@article{li2024say,
  title={Say Your Reason: Extract Contextual Rules In Situ for Context-aware Service Recommendation},
  author={Li, Yuxuan and Li, Jiahui and Pan, Lihang and Yu, Chun and Shi, Yuanchun},
  journal={arXiv preprint arXiv:2408.13977},
  year={2024}
}

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