This research introduces Layered Chain-of-Thought (Layered-CoT), a framework that systematically segments reasoning into multiple verification layers, improving transparency and reliability of LLM decisions.

• Segmented Reasoning Process: Divides complex reasoning into distinct layers with external verification
• Multi-Agent Architecture: Leverages specialized LLM agents for different reasoning tasks
• Enhanced Explainability: Provides clearer justifications for decisions through structured reasoning chains
• Domain Adaptability: Successfully applied to medical triage scenarios

For medical applications, this approach enables more transparent diagnostic reasoning, potentially reducing errors and improving patient outcomes through verifiable decision paths. Healthcare professionals can trace, understand, and trust AI-assisted medical decisions with greater confidence.

Layered Chain-of-Thought Prompting for Multi-Agent LLM Systems: A Comprehensive Approach to Explainable Large Language Models