Can AI Draw Science? A Benchmark for Evaluating Scientific Figure Generation by Text-to-Image and Multimodal Models
arXiv:2606.28406v1 Announce Type: new Abstract: Text-to-image and multimodal generative models are increasingly used to produce scientific figures such as mechanism diagrams, experimental-design schematics, conceptual frameworks, and graphical abstracts. Yet existing image-generation benchmarks (e.g., GenEval, T2I-CompBench, DPG-Bench) evaluate natural images and measure compositionality, object counting, or photorealism. None of them measure what makes a generated scientific figure usable: correct and legible text labels, faithful depiction of entities and their relations, coherent diagrammat
The proliferation of text-to-image models necessitates specific benchmarks to evaluate their utility for scientific applications, moving beyond general image generation metrics.
This benchmark addresses a critical gap in assessing AI's capability to generate reliable scientific figures, crucial for research, education, and professional communication.
The focus for generative AI in scientific domains will shift from raw image generation to accuracy, legibility, and faithful representation of complex scientific concepts and data.
- · AI model developers specializing in scientific applications
- · Scientific researchers and publishers
- · AI ethics and safety organizations
- · General-purpose T2I models without scientific finetuning
- · Manual scientific illustration services (eventually)
- · Researchers relying on inaccurate AI-generated figures
Improved scientific figure generation leads to clearer communication of complex research findings.
Accelerated scientific discovery due to more efficient conceptualization and data visualization.
Enhanced accessibility of scientific knowledge through AI-powered visual aids, potentially lowering barriers to entry in complex fields.
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Read at arXiv cs.LG