Dual Engines of Thoughts: A Depth-Breadth Integration Framework for Open-Ended Analysis
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910908467380224 |
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| author | Yu, Fei-Hsuan Chou, Yun-Cheng Chen, Teng-Ruei |
| author_facet | Yu, Fei-Hsuan Chou, Yun-Cheng Chen, Teng-Ruei |
| contents | We propose the Dual Engines of Thoughts (DEoT), an analytical framework for comprehensive open-ended reasoning. While traditional reasoning frameworks primarily focus on finding "the best answer" or "the correct answer" for single-answer problems, DEoT is specifically designed for "open-ended questions," enabling both broader and deeper analytical exploration. The framework centers on three key components: a Base Prompter for refining user queries, a Solver Agent that orchestrates task decomposition, execution, and validation, and a Dual-Engine System consisting of a Breadth Engine (to explore diverse impact factors) and a Depth Engine (to perform deep investigations). This integrated design allows DEoT to balance wide-ranging coverage with in-depth analysis, and it is highly customizable, enabling users to adjust analytical parameters and tool configurations based on specific requirements. Experimental results show that DEoT excels in addressing complex, multi-faceted questions, achieving a total win rate of 77-86% compared to existing reasoning models, thus highlighting its effectiveness in real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07872 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dual Engines of Thoughts: A Depth-Breadth Integration Framework for Open-Ended Analysis Yu, Fei-Hsuan Chou, Yun-Cheng Chen, Teng-Ruei Artificial Intelligence Computational Engineering, Finance, and Science Computation and Language Multiagent Systems We propose the Dual Engines of Thoughts (DEoT), an analytical framework for comprehensive open-ended reasoning. While traditional reasoning frameworks primarily focus on finding "the best answer" or "the correct answer" for single-answer problems, DEoT is specifically designed for "open-ended questions," enabling both broader and deeper analytical exploration. The framework centers on three key components: a Base Prompter for refining user queries, a Solver Agent that orchestrates task decomposition, execution, and validation, and a Dual-Engine System consisting of a Breadth Engine (to explore diverse impact factors) and a Depth Engine (to perform deep investigations). This integrated design allows DEoT to balance wide-ranging coverage with in-depth analysis, and it is highly customizable, enabling users to adjust analytical parameters and tool configurations based on specific requirements. Experimental results show that DEoT excels in addressing complex, multi-faceted questions, achieving a total win rate of 77-86% compared to existing reasoning models, thus highlighting its effectiveness in real-world applications. |
| title | Dual Engines of Thoughts: A Depth-Breadth Integration Framework for Open-Ended Analysis |
| topic | Artificial Intelligence Computational Engineering, Finance, and Science Computation and Language Multiagent Systems |
| url | https://arxiv.org/abs/2504.07872 |