@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://aclanthology.org/2025.acl-long.1354/",
doi = "10.18653/v1/2025.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.}
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%0 Conference Proceedings
%T Gödel Agent: A Self-Referential Agent Framework for Recursively Self-Improvement
%A Yin, Xunjian
%A Wang, Xinyi
%A Pan, Liangming
%A Lin, Li
%A Wan, Xiaojun
%A Wang, William Yang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F yin-etal-2025-godel
%X 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 Gödel Agent, a self-evolving framework inspired by the Gödel Machine, enabling agents to recursively improve themselves without relying on predefined routines or fixed optimization algorithms. Gödel 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 Gödel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.
%R 10.18653/v1/2025.acl-long.1354
%U https://aclanthology.org/2025.acl-long.1354/
%U https://doi.org/10.18653/v1/2025.acl-long.1354
%P 27890-27913
Markdown (Informal)
[Gödel Agent: A Self-Referential Agent Framework for Recursively Self-Improvement](https://aclanthology.org/2025.acl-long.1354/) (Yin et al., ACL 2025)
ACL