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Dual Engines of Thoughts: A Depth-Breadth Integration Framework for Open-Ended Analysis

Fei-Hsuan Yu, Yun-Cheng Chou, Teng-Ruei Chen

TL;DR

DEoT presents a depth-breadth integration framework for open-ended analysis, pairing a Breadth Engine with a Depth Engine under an Engine Controller to balance broad coverage and deep investigation. The architecture is built around a Base Prompter, a Solver Agent, and a versatile toolbox that includes news search, event extraction, and historical analysis, enabling coordinated multi-agent reasoning with tool support. The authors introduce the News-to-Question (N2Q) dataset and a multi-criteria evaluation scheme using GPT-4o as the judge, reporting strong analytical depth and innovation relative to GPT-4o and Perplexity AI across five domains. While DEoT excels at open-ended, multi-faceted reasoning, it shows room for improvement in practicality and the generation of domain-specific, actionable arguments, guiding future work on knowledge integration and adaptive reasoning. Overall, DEoT demonstrates a robust framework for scalable, multi-dimensional analysis with potential applications in finance, policy, technology forecasting, and beyond.

Abstract

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.

Dual Engines of Thoughts: A Depth-Breadth Integration Framework for Open-Ended Analysis

TL;DR

DEoT presents a depth-breadth integration framework for open-ended analysis, pairing a Breadth Engine with a Depth Engine under an Engine Controller to balance broad coverage and deep investigation. The architecture is built around a Base Prompter, a Solver Agent, and a versatile toolbox that includes news search, event extraction, and historical analysis, enabling coordinated multi-agent reasoning with tool support. The authors introduce the News-to-Question (N2Q) dataset and a multi-criteria evaluation scheme using GPT-4o as the judge, reporting strong analytical depth and innovation relative to GPT-4o and Perplexity AI across five domains. While DEoT excels at open-ended, multi-faceted reasoning, it shows room for improvement in practicality and the generation of domain-specific, actionable arguments, guiding future work on knowledge integration and adaptive reasoning. Overall, DEoT demonstrates a robust framework for scalable, multi-dimensional analysis with potential applications in finance, policy, technology forecasting, and beyond.

Abstract

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.

Paper Structure

This paper contains 70 sections, 30 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Comparison with Traditional Frameworks
  3. Key Contributions
  4. Framework Application
  5. Related Works
  6. Chain-of-Thought and Multi-Step Reasoning
  7. Multi-Agent Systems
  8. Tool-Enhanced Reasoning Frameworks
  9. Methodology
  10. DEoT Framework Overview
  11. Inspiration from Mind Mapping
  12. Framework Architecture
  13. Core Components
  14. Base Prompter.
  15. Solver Agent.
  16. ...and 55 more sections

Figures (30)

  • Figure 1: Dual Engines of Thoughts Analytical Framework
  • Figure 2: Dual Engines of Thoughts Tree Graph
  • Figure 3: Dual Engines of Thoughts Analysis Flow
  • Figure 4: Two examples of open-ended question generation using N2Q on the news dataset. Colored texts indicate the structural composition of the questions: orange, green, and blue correspond to the Event Overview content derived from the news information, while red represents follow-up questions related to the event.
  • Figure 5: Comparison of DEoT and GPT-4o Based on Five Core Domains
  • ...and 25 more figures