Seeing is Coding: On the Effectiveness of Vision Language Models in Code Understanding

Understand

Large Language Models (LLMs) have achieved remarkable success in source code understanding, yet as software systems grow in scale, computational efficiency has become a critical bottleneck.

• Currently, these models rely on a text-based paradigm that treats source code as a linear sequence of tokens, which leads to a linear increase in context length and associated computational costs.
• The rapid advancement of Multimodal LLMs (MLLMs) introduces an opportunity to optimize efficiency by representing source code as rendered images.
• Unlike text, which is difficult to compress without losing semantic meaning, the image modality is inherently suitable for compression.