Predicting program behavior and reasoning about code execution remain significant challenges in software engineering, particularly for large language models (LLMs) designed for code analysis. While these models excel at understanding static syntax, they often struggle with dynamic reasoning tasks. We introduce VisualCoder, a simple yet effective approach that enhances code reasoning by integrating multimodal Chain-of-Thought (CoT) reasoning with a visual Control Flow Graph (CFG). By aligning code snippets with their corresponding CFGs, VisualCoder provides deeper insights into execution flows. We address challenges in multimodal CoT integration through a reference mechanism, ensuring consistency between code and its execution path, thereby improving performance in program behavior prediction, error detection, and output generation.
@article{arxiv.2410.23402,
title = {VisualCoder: Guiding Large Language Models in Code Execution with Fine-grained Multimodal Chain-of-Thought Reasoning},
author = {Cuong Chi Le and Hoang-Chau Truong-Vinh and Huy Nhat Phan and Dung Duy Le and Tien N. Nguyen and Nghi D. Q. Bui},
journal= {arXiv preprint arXiv:2410.23402},
year = {2025}
}