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Scientific figures give research ideas a visual form through choices of abstraction, composition, and emphasis. Generative AI offers new ways to realize these choices, raising questions about explanat...
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.
arXiv:2607. 18514v1 Announce Type: cross Abstract: Visual diagrams, figures, and tables are central to scientific papers, and convey information beyond what is captured in text.
ReFigBench is a benchmark that evaluates how well multimodal coding agents can transform scientific overview figures into editable PowerPoint slides, preserving text, layout, and document structure. The study uses 1,000 real figures from arXiv, testing agents from four model families across two workflows—direct code generation and a specialized PPTX workflow—within ten different harness configurations. Evaluation combines deterministic artifact checks, automated scoring by judges, and blinded human comparisons, revealing that workflow and harness choices significantly affect reconstruction quality and that even the best agents fall short of the ideal rubric.
arXiv:2607. 03006v1 Announce Type: cross Abstract: Text-rich image models can now design poster-scale layouts, but we lack ways to measure whether they honor scientific communication contracts: legible labels, prescribed aspect ratios, and -- above all -- abstaining from fabricated scientific figures.
arXiv:2602. 02465v2 Announce Type: replace Abstract: Frontier models are transitioning from multimodal large language models (MLLMs) that merely ingest visual information to unified multimodal models (UMMs) capable of native interleaved generation.