SourceScImage: How good are multimodal large language models at scientific text-to-image generation?
Comparing the two output modes on the scientific style criterion, images generated via intermediate code (Python or TikZ) receive scores above 2.5, while direct image generation from Stable Diffusion (1.96) and DALL-E (1.55) scores below 2. The paper attributes this to code models producing structured, convention-following output, whereas visual models trained on real-life images lack scientific drawing conventions. Python code also outperforms TikZ across all three criteria (correctness 2.81 vs 2.64, relevance 2.97 vs 2.81, scientific style 3.56 vs 3.18) with a lower compile error rate (0.17 vs 0.19).