Tree-of-Code: A Hybrid Approach for Robust Complex Task Planning and Execution

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Bibliografiske detaljer
Udgivet i:	arXiv.org (Dec 18, 2024), p. n/a
Hovedforfatter:	Ni, Ziyi
Andre forfattere:	Li, Yifan, Dong, Daxiang
Udgivet:	Cornell University Library, arXiv.org
Fag:	Python Large language models Decision trees Task complexity
Online adgang:	Citation/Abstract Full text outside of ProQuest
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Beskrivelse
Resumen:	The exceptional capabilities of large language models (LLMs) have substantially accelerated the rapid rise and widespread adoption of agents. Recent studies have demonstrated that generating Python code to consolidate LLM-based agents' actions into a unified action space (CodeAct) is a promising approach for developing real-world LLM agents. However, this step-by-step code generation approach often lacks consistency and robustness, leading to instability in agent applications, particularly for complex reasoning and out-of-domain tasks. In this paper, we propose a novel approach called Tree-of-Code (ToC) to tackle the challenges of complex problem planning and execution with an end-to-end mechanism. By integrating key ideas from both Tree-of-Thought and CodeAct, ToC combines their strengths to enhance solution exploration. In our framework, each final code execution result is treated as a node in the decision tree, with a breadth-first search strategy employed to explore potential solutions. The final outcome is determined through a voting mechanism based on the outputs of the nodes.
ISSN:	2331-8422
Fuente:	Engineering Database