Scientific Image Synthesis: Benchmarking, Methodologies, and Downstream Utility

While synthetic data has proven effective for improving sci-entific reasoning in the text domain, multimodal reasoning remains con-strained by the difficulty of synthesizing scientifically rigorous images.Existing Text-to-Image (T2I) models often produce outputs that arevisually plausible yet scientifically incorrect, resulting in a persistentvisual–logic divergence that limits their value for downstream reason-ing. Motivated by recent advances in next-generation T2I models, weconduct a systematic study of scientific image synthesis across genera-tion paradigms, evaluation, and downstream use. We examine both di-rect pixel-based generation and programmatic synthesis, and instantiateImgCoder as a structured implementation of the code-driven workflow,following an explicit “understand → plan → code” prompting strategyto encourage clearer structural specification. To rigorously assess scien-tific correctness, we introduce SciGenBench, which evaluates generatedimages based on information utility and logical validity. Our evaluationreveals systematic failure modes in pixel-based models and highlightsa fundamental expressiveness–precision trade-off. Finally, we show thatfine-tuning Large Multimodal Models (LMMs) on rigorously verified syn-thetic scientific images yields consistent reasoning gains, with potentialscaling trends analogous to the text domain, validating high-fidelity sci-entific synthesis as a viable path to unlocking massive multimodal rea-soning capabilities.