Tree-of-Code: A Hybrid Approach for Robust Complex Task Planning and Execution
I tiakina i:
| I whakaputaina i: | arXiv.org (Dec 18, 2024), p. n/a |
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| Kaituhi matua: | |
| Ētahi atu kaituhi: | , |
| I whakaputaina: |
Cornell University Library, arXiv.org
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| Ngā marau: | |
| Urunga tuihono: | Citation/Abstract Full text outside of ProQuest |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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| Whakarāpopotonga: | 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. |
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| ISSN: | 2331-8422 |
| Puna: | Engineering Database |