This research challenges the assumption that LLMs truly understand code structure and control flow in the way humans do, despite impressive benchmark performances.

• Benchmark success ≠ structural understanding: High scores on coding tasks don't translate to human-like comprehension of control flow
• Specific weaknesses: Models struggle with tracing execution paths and grasping core programming concepts like recursion
• Hidden limitations: Current benchmarks may overstate LLMs' true programming abilities
• Engineering implications: Developers should exercise caution when relying on LLMs for complex structural code tasks

For engineering teams, this research highlights the need for improved evaluation methods that better assess structural code understanding in AI systems.

CoCoNUT: Structural Code Understanding does not fall out of a tree