SciForma: Structure-Faithful Generation of Scientific Diagrams
Structural fidelity is essential to scientific methodology diagrams. To communicate research logic, these diagrams must faithfully render components, directional relations, and textual annotations. Since a single error, such as a reversed arrow or an unreadable equation, can invalidate the entire figure, structural fidelity is inherently conjunctive: correctness on one axis cannot compensate for failure on another. Current open-source models fail to satisfy this criterion. Supervised fine-tuning (SFT) learns plausible layouts but cannot reliably ensure structural correctness, while scalar reward-based post-training obscures which structural dimension has failed. To address this, we introd...
| Authors | Yuxuan Luo, Peng Zhang, Xinjie Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
Connected in the knowledge graph
- microsoft/SciFormaBenchDataset · cited by
- microsoft/SciFormaData-700KDataset · cited by
Models and datasets referencing this paper
- microsoft/SciFormaData-700KDataset
- microsoft/SciFormaBenchDataset
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.