Tree-of-Code: A Hybrid Approach for Robust Complex Task Planning and Execution
में बचाया:
| में प्रकाशित: | arXiv.org (Dec 18, 2024), p. n/a |
|---|---|
| मुख्य लेखक: | |
| अन्य लेखक: | , |
| प्रकाशित: |
Cornell University Library, arXiv.org
|
| विषय: | |
| ऑनलाइन पहुंच: | Citation/Abstract Full text outside of ProQuest |
| टैग: |
कोई टैग नहीं, इस रिकॉर्ड को टैग करने वाले पहले व्यक्ति बनें!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3147568797 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3147568797 | ||
| 045 | 0 | |b d20241218 | |
| 100 | 1 | |a Ni, Ziyi | |
| 245 | 1 | |a Tree-of-Code: A Hybrid Approach for Robust Complex Task Planning and Execution | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 18, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a 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. | |
| 653 | |a Python | ||
| 653 | |a Large language models | ||
| 653 | |a Decision trees | ||
| 653 | |a Task complexity | ||
| 700 | 1 | |a Li, Yifan | |
| 700 | 1 | |a Dong, Daxiang | |
| 773 | 0 | |t arXiv.org |g (Dec 18, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3147568797/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.14212 |