This research introduces a structured approach to evaluate how reliable and consistent explanations from LLMs are by visualizing reasoning as a graph topology.

Key findings:

• Maps LLM reasoning into graph structures to measure explanation uncertainty
• Uses a specially designed structural elicitation strategy to guide LLMs in framing consistent explanations
• Provides a quantifiable method to assess when LLM explanations can be trusted
• Offers critical insights for security applications where verification of AI reasoning is essential

For security professionals, this framework represents a significant advancement in determining when to trust LLM outputs in sensitive contexts, helping to identify potential vulnerabilities in AI-based security systems.

Understanding the Uncertainty of LLM Explanations: A Perspective Based on Reasoning Topology