In a world awash with data and decisions, the tools we use to think are just as important as the thoughts themselves. That’s why the Dual Engines of Thoughts (DEoT) framework, recently introduced by NeuroWatt, is such a game-changer.

It’s not just another spin on reasoning chains—it’s a whole new architecture of thought.

🧠 The Problem with Single-Track Thinking

Most reasoning systems rely on either a single engine (a one-track logic flow like Chain-of-Thought) or a multi-agent setup (such as AutoGen) where agents collaborate on subtasks. However, both have trade-offs:

  • Single-engine models struggle to cover diverse dimensions and often sacrifice complexity for coherence.
  • Multi-agent systems offer specialization but lack cohesion, often producing fragmented or redundant insights.

DEoT is different: it’s not just an ensemble of agents or a recursive chain—it’s a coordinated cognitive structure that mirrors how humans map problems spatially and then zoom in tactically.

Most current AI frameworks—like Chain-of-Thought or Tree-of-Thought—aim to find the best answer through a linear or structured reasoning path. But business problems rarely arrive that cleanly. They are messy, multi-dimensional, and open-ended.

DEoT’s insight? Don’t choose between depth or breadth. Do both—intelligently.

🔀 Meet the Dual Engines

📌 [Insert Figure 1: Dual Engines of Thoughts Analytical Framework here]

The core inspiration behind DEoT is Mind Mapping—how humans visually deconstruct a problem into branches, explore outward, and then dive inward on key points. The Breadth Engine mirrors this outward exploration, while the Depth Engine handles vertical drilling into high-impact nodes.

  • Breadth Engine Example: Imagine a firm assessing whether to enter the electric vehicle (EV) market. The Breadth Engine considers environmental regulation, battery supply chains, consumer trends, and charging infrastructure.

  • Depth Engine Example: Once ‘battery supply chain risk’ is flagged as crucial, the Depth Engine formulates sub-queries like: “How dependent are current suppliers on rare-earth metals?” or “What are the geopolitical risks tied to lithium exporters?”

These engines are coordinated by an Engine Controller, which dynamically manages when to go wide, when to go deep, and when to stop. That orchestration is what sets DEoT apart.

DEoT breaks reasoning into two parallel engines:

  • Breadth Engine: Think of it like a brainstorming map, rapidly scanning economic, social, technical, and regulatory aspects of a problem.
  • Depth Engine: Like a microscope, it zooms into the most critical branches, uncovering deeper causal structures and implications.

These engines are coordinated by an Engine Controller, which dynamically decides where to explore next, when to go deeper, and when to consolidate. This mirrors how top analysts think—starting broad, then narrowing in.

⚙️ What Powers It

DEoT is not a pre-trained model—it’s a reasoning framework. You don’t need a single monolithic model to run DEoT. You can combine multiple LLMs—like GPT-4o for reasoning and o1 for retrieval—as long as their roles are organized through DEoT’s architecture.

Using GPT-4o and GPT-o1 together? That’s only DEoT if you also include:

  • A Base Prompter to clarify and standardize the query.
  • A Solver Agent to plan and orchestrate task decomposition.
  • A Toolbox to gather news, extract events, search historical context, and reason logically.
  • A Dual-Engine system that consciously manages analytical directions.

Without this architecture, you’re just switching between models—not truly reasoning in tandem.

Behind the scenes, DEoT is modular:

  • A Base Prompter that optimizes vague or ambiguous user queries.
  • A Solver Agent that decomposes tasks, executes them via a toolbox (news search, event extraction, reasoning, etc.), and validates results.
  • Iterative layers of analysis that adapt to content complexity.

The system isn’t just smarter—it’s self-aware of its own reasoning process.

📊 Why It Matters for Cognaptus

Let’s ground this with two real Cognaptus use cases:

Case 1: Automating Policy Impact Reports for Logistics Firms

Scenario: A shipping company wants to understand the effects of new carbon pricing rules on operations in Southeast Asia.

DEoT Workflow:

  • Base Prompter refines the vague question into: “What are the operational and financial impacts of regional carbon pricing policies in 2025 for cross-border logistics?”
  • Solver Agent plans three subtasks: (1) Policy scanning, (2) historical emission-cost trends, (3) fleet adaptation options.
  • Toolbox pulls real-time regulations, past responses of logistics firms, and expert commentary.
  • Breadth Engine explores carbon pricing across Singapore, Malaysia, and the Philippines.
  • Depth Engine dives into the long-term ROI of fleet electrification.
  • Final Response Agent consolidates into a 3-part risk-reduction strategy tailored for the client.

Case 2: Market Entry Strategy for FinTech SaaS

Scenario: A client is exploring B2B SaaS expansion into Latin America.

DEoT Workflow:

  • Base Prompter transforms the vague prompt: “Can we succeed in Latin America?” into a structured query.
  • Solver Agent breaks it into: (1) local regulatory landscape, (2) payment tech infrastructure, (3) competitive positioning.
  • Toolbox retrieves country-level FinTech news, historical failures/successes, and industry benchmarks.
  • Breadth Engine evaluates Brazil, Mexico, and Chile on five market dimensions.
  • Depth Engine zooms into the cost and success factors of API integration partnerships in Brazil.
  • Final Response Agent delivers an evidence-based go/no-go memo with a phased market plan.

These aren’t just faster outputs—they’re smarter decisions powered by structure.

At Cognaptus, we believe automation should make business thinking more powerful, not just faster. DEoT fits directly into our vision:

  • We can use DEoT to automate analyst-grade reports—from policy impact briefs to market entry scenarios.
  • It could serve as the backbone of a Cognaptus Advisor, an AI-powered agent that provides structured recommendations based on broad + deep analysis.
  • For clients in finance, logistics, or government, DEoT can structure chaos into clarity—automating the mind-mapping process behind tough decisions.

🧪 A Smarter Brain for Complex Times

DEoT isn’t perfect—it still needs stronger retrieval grounding and real-world calibration. But it’s a leap toward how humans truly reason under uncertainty. And for Cognaptus clients, it offers a glimpse of how AI can reason with you, not just for you.

Because in a world of open-ended complexity, the best insights don’t come from either/or—they come from both/and.

Want to explore how DEoT logic can power your team’s thinking? Reach out to Cognaptus.

Reference:
Yu, F.-H., Chou, Y.-C., & Chen, T.-R. (2025). Dual Engines of Thoughts: A Depth-Breadth Integration Framework for Open-Ended Analysis. arXiv:2504.07872 [cs.AI]. [Submitted on 10 Apr 2025]