Scientific Image Synthesis: Benchmarking, Methodologies, and Downstream Utility
While synthetic data has proven effective for improving scientific reasoning in the text domain, multimodal reasoning remains constrained by the difficulty of synthesizing scientifically rigorous images. Existing Text-to-Image (T2I) models often produce outputs that are visually plausible yet scientifically incorrect, resulting in a persistent visual-logic divergence that limits their value for downstream reasoning. Motivated by recent advances in next-generation T2I models, we conduct a systematic study of scientific image synthesis across generation paradigms, evaluation, and downstream use. We analyze both direct pixel-based generation and programmatic synthesis, and propose ImgCoder, a logic-driven framework that follows an explicit "understand - plan - code" workflow to improve structural precision. To rigorously assess scientific correctness, we introduce SciGenBench, which evaluates generated images based on information utility and logical validity. Our evaluation reveals systematic failure modes in pixel-based models and highlights a fundamental expressiveness-precision trade-off. Finally, we show that fine-tuning Large Multimodal Models (LMMs) on rigorously verified synthetic scientific images yields consistent reasoning gains, with potential scaling trends analogous to the text domain, validating high-fidelity scientific synthesis as a viable path to unlocking massive multimodal reasoning capabilities.
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Honglin Lin
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2026-01-27T13:56:57.457Z
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Honglin Lin
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Chonghan Qin
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2026-01-27T09:42:44.133Z
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Chonghan, Qin
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Zheng Liu
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2026-01-27T09:42:39.432Z
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Zheng Liu
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Qizhi Pei
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2026-04-14T08:18:27.856Z
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QizhiPei
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Yu Li
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Zhanping Zhong
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2026-01-27T09:42:41.958Z
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Zhanping Zhong
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Xin Gao
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Yanfeng Wang
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Conghui He
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Lijun Wu
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2026-02-02T17:00:58.746Z
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Lijun Wu
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Scientific Image Synthesis: Benchmarking, Methodologies, and Downstream Utility
While synthetic data has proven effective for improving scientific reasoning in the text domain, multimodal reasoning remains constrained by the difficulty of synthesizing scientifically rigorous images. Existing Text-to-Image (T2I) models often produce outputs that are visually plausible yet scientifically incorrect, resulting in a persistent visual-logic divergence that limits their value for downstream reasoning. Motivated by recent advances in next-generation T2I models, we conduct a systematic study of scientific image synthesis across generation paradigms, evaluation, and downstream use. We analyze both direct pixel-based generation and programmatic synthesis, and propose ImgCoder, a logic-driven framework that follows an explicit "understand - plan - code" workflow to improve structural precision. To rigorously assess scientific correctness, we introduce SciGenBench, which evaluates generated images based on information utility and logical validity. Our evaluation reveals systematic failure modes in pixel-based models and highlights a fundamental expressiveness-precision trade-off. Finally, we show that fine-tuning Large Multimodal Models (LMMs) on rigorously verified synthetic scientific images yields consistent reasoning gains, with potential scaling trends analogous to the text domain, validating high-fidelity scientific synthesis as a viable path to unlocking massive multimodal reasoning capabilities.
Scientific image synthesis using logic-driven frameworks like ImgCoder improves multimodal reasoning by addressing visual-logic divergence through structured generation and evaluation benchmarks.
Text-to-Image models Large Multimodal Models scientific correctness visual-logic divergence ImgCoder SciGenBench pixel-based generation programmatic synthesis logical validity information utility