Layered Chain-of-Thought Prompting for Multi-Agent LLM Systems: A Comprehensive Approach to Explainable Large Language Models
Manish Sanwal
TL;DR
Problem: vanilla CoT yields unverified intermediate reasoning and limited user interaction in high-stakes contexts. Method: Layered-CoT partitions reasoning into verifiable layers with external checks and optional user feedback, extended by multi-agent coordination for verification and data retrieval. Contributions: framework, workflow examples, and domain demonstrations (medical triage, financial risk, agile engineering) showing improved transparency, correctness, and engagement. Impact: promotes safer, more grounded explanations for LLMs in critical domains and motivates future work on adaptive layering and agent orchestration.
Abstract
Large Language Models (LLMs) leverage chain-of-thought (CoT) prompting to provide step-by-step rationales, improving performance on complex tasks. Despite its benefits, vanilla CoT often fails to fully verify intermediate inferences and can produce misleading explanations. In this work, we propose Layered Chain-of-Thought (Layered-CoT) Prompting, a novel framework that systematically segments the reasoning process into multiple layers, each subjected to external checks and optional user feedback. We expand on the key concepts, present three scenarios -- medical triage, financial risk assessment, and agile engineering -- and demonstrate how Layered-CoT surpasses vanilla CoT in terms of transparency, correctness, and user engagement. By integrating references from recent arXiv papers on interactive explainability, multi-agent frameworks, and agent-based collaboration, we illustrate how Layered-CoT paves the way for more reliable and grounded explanations in high-stakes domains.