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.
@article{arxiv.2504.07872,
title = {Dual Engines of Thoughts: A Depth-Breadth Integration Framework for Open-Ended Analysis},
author = {Fei-Hsuan Yu and Yun-Cheng Chou and Teng-Ruei Chen},
journal= {arXiv preprint arXiv:2504.07872},
year = {2025}
}