Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement

2024-10-06

arXiv.org

AI Summary

Existing AI agents are constrained by manually engineered workflows or meta-learning frameworks, limiting exploration of the agent design space and often leading to suboptimal local solutions. Method: This paper introduces a self-referential, recursively self-improving agent framework inspired by Gödel machines, centered on a large language model (LLM). It employs self-modifying prompts and dynamic logic reloading to enable end-to-end self-evolution and behavioral logic rewriting—given only high-level goal prompts—without fixed optimization algorithms or predefined architectural constraints. The framework supports goal-driven recursive execution cycles. Contribution/Results: Experiments demonstrate sustained performance gains on mathematical reasoning and complex multi-step tasks, significantly outperforming handcrafted agents. The approach achieves breakthroughs in accuracy, reasoning efficiency, and cross-task generalization, establishing a foundation for autonomous, objective-driven agent evolution.

Technology Category

• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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 G""odel Agent, a self-evolving framework inspired by the G""odel machine, enabling agents to recursively improve themselves without relying on predefined routines or fixed optimization algorithms. G""odel 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 G""odel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.

Problem

• Overcome limitations of fixed AI agent designs
• Enable recursive self-improvement in AI agents
• Dynamically modify agent logic using LLMs

Innovation

• Self-evolving framework
• Dynamic logic modification
• High-level objective guidance