@inproceedings{yin-etal-2025-godel,
    title = {G{\"o}del Agent: A Self-Referential Agent Framework for Recursively Self-Improvement},
    author = "Yin, Xunjian  and
      Wang, Xinyi  and
      Pan, Liangming  and
      Lin, Li  and
      Wan, Xiaojun  and
      Wang, William Yang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1354/",
    pages = "27890--27913",
    ISBN = "979-8-89176-251-0",
    abstract = {The rapid advancement of large language models (LLMs) has significantly enhanced the capabilities of agents across various tasks. However, existing agentic systems, whether based on fixed pipeline algorithms or pre-defined meta-learning frameworks, cannot search the whole agent design space due to the restriction of human-designed components, and thus might miss the more optimal agent design. In this paper, we introduce G{\"o}del Agent, a self-evolving framework inspired by the G{\"o}del Machine, enabling agents to recursively improve themselves without relying on predefined routines or fixed optimization algorithms. G{\"o}del Agent leverages LLMs to dynamically modify its own logic and behavior, guided solely by high-level objectives through prompting. Experimental results on multiple domains demonstrate that the implementation of G{\"o}del Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.}
}