Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement
An agent that reads and rewrites its own logic at runtime, including the logic it uses to rewrite itself, guided only by a high-level objective. It is the first language-model framework to implement the Gödel machine structure directly, with evaluation in place of proof.
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Self-modification happens during execution rather than between training runs.
Published at ACL 2025.
Abstract
The rapid advancement of large language models (LLMs) has significantly enhanced the capabilities of AI-driven 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 globally 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 mathematical reasoning and complex agent tasks demonstrate that implementation of Gödel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.