DEoT框架让AI能深度广度并行分析复杂开放问题。
Dual Engines of Thoughts: A Depth-Breadth Integration Framework for Open-Ended Analysis
- 双引擎设计：广度引擎探索多元影响因素，深度引擎深入剖析关键点。
- 在复杂问题上胜率77%-86%，显著优于现有模型。
- 适合需要多维度、多层次分析的决策类任务，如战略规划。
我们提出双思维引擎（DEoT）框架，用于全面的开放式推理。传统推理框架侧重于单答案问题的“最佳”或“正确”答案，而DEoT专为“开放性问题”设计，支持广泛且深入的分析。框架包含三个核心组件：基础提示器用于优化用户查询，求解代理负责任务分解、执行与验证，以及由广度引擎（探索多样影响因素）和深度引擎（进行深入探究）组成的双引擎系统。该集成设计实现了覆盖广度与分析深度的平衡，且高度可配置，支持根据需求调整分析参数和工具设置。实验表明，DEoT在应对复杂多面问题时表现优异，总胜率达77%-86%，凸显其在实际应用中的有效性。
原文摘要 · Abstract (English)
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.
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