{"ID":5675425,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-07T01:06:03.009715918Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.02290","arxiv_id":"2607.02290","title":"DisciplineGen-1M: A Large-Scale Dataset for Multidisciplinary Visual Generation and Editing","abstract":"Recent image generation and editing models can produce visually appealing natural images, yet they remain unreliable when the target image is a knowledge-intensive diagram whose correctness depends on disciplinary concepts, symbolic structure, and precise spatial relations. We introduce DisciplineGen-1M, a million-scale multidisciplinary dataset that supports text-to-image generation and image editing. It contains 1.2M samples spanning mathematics, physics, chemistry, biology, geography, computer science, economics, history, music, and sports. To construct the dataset, we design a scalable framework that combines vector-graphics rendering, OCR-based editing, curated programmatic synthesis, and large-scale text-to-image filtering. These pipelines produce captions, editing instructions, structured annotations, and paired images with controllable semantic differences. Building on DisciplineGen-1M, we further introduce a discipline-informed reasoning-generation model for both text-to-image generation and image editing. Experiments on discipline-related benchmarks, GenExam and GRADE, show substantial improvements over open-source baselines, while evaluations on general reasoning-informed benchmarks, WISE and RISE, further indicate broader transfer. The results suggest that large-scale structured academic visual data is a key ingredient for moving image generation from aesthetic plausibility toward verifiable knowledge-grounded visual creation. We will publicly release our dataset, model, and source code of the data curation pipeline to ensure reproducibility and benefit future research.","short_abstract":"Recent image generation and editing models can produce visually appealing natural images, yet they remain unreliable when the target image is a knowledge-intensive diagram whose correctness depends on disciplinary concepts, symbolic structure, and precise spatial relations. We introduce DisciplineGen-1M, a million-scal...","url_abs":"https://arxiv.org/abs/2607.02290","url_pdf":"https://arxiv.org/pdf/2607.02290v1","authors":"[\"Zhaokai Wang\",\"Mingxin Liu\",\"Zirun Zhu\",\"Ziqian Fan\",\"Yiguo He\",\"Mohan Zhang\",\"Leyao Gu\",\"Xiangyu Zhao\",\"Ning Liao\",\"Shaofeng Zhang\",\"Xuanhe Zhou\",\"Zhihang Zhong\",\"Junchi Yan\",\"Xue Yang\"]","published":"2026-07-02T15:07:47Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}