Diagnosing CFG Interpretation in LLMs

This paper investigates the capabilities and limitations of Large Language Models (LLMs) in accurately interpreting Control Flow Graphs (CFGs). It proposes novel diagnostic methodologies to identify common errors and biases in how LLMs process and understand program structures, crucial for enhancing their performance in tasks like code generation, debugging, and vulnerability detection. The research aims to improve the reliability of LLM-powered software development tools, making them more effective and trustworthy for real-world applications in engineering and cybersecurity.