analiza aiqbench
The paper releases DiagramBank, a retrieval resource of 57,100 scientific diagrams from accepted AI/ML papers, and reports that swapping in its references raises a PaperBanana benchmark score from 4.62 to 7.88. The gain comes from one benchmark and one generation pipeline, so the magnitude may not transfer to other systems. For readers, the useful takeaway is that curated reference data, not only model changes, can drive gains in automated scientific figure generation.
DiagramBank: Draw from the Great, Draw Your Best
arXiv:2604.20857v3 Announce Type: replace-cross Abstract: Scientific diagrams help researchers communicate complex methods and relationships, but creating them requires substantial effort in content organization, layout design, and visual refinement. Automated diagram generation can reduce this workload. Reference examples can guide this process by showing how scientific ideas are organized and presented visually. Diagrams from accepted papers provide a rich source of such examples, but their utility as a large-scale reference resource remains underexplored. We introduce DiagramBank, a diagram-text reference resource containing 57,100 scientific diagrams from 26,751 accepted AI/ML papers. Each diagram is paired with its caption, paper title, and abstract, with citing paragraphs included when available. We combine multi-stage filtering, automated checks, and human audits, with an estimated diagram-selection precision of 93.67%. Retrieval experiments show that citing paragraphs complement captions with information specific to individual diagrams. Under a fixed generation pipeline, replacing PaperBanana's curated reference pool with DiagramBank increases the overall PaperBanana score from 4.62 to 7.88 on PaperBananaBench. Retrieved references also outperform random references on BizGenEval scientific figures. Additional evaluations with different judges and renderers, together with a separate blind human evaluation, demonstrate the utility of DiagramBank for automated diagram generation. Code: https://github.com/csml-rpi/DiagramBank.
Abstract:Scientific diagrams help researchers communicate complex methods and relationships, but creating them requires substantial effort in content organization, layout design, and visual refinement. Automated diagram generation can reduce this workload. Reference examples can guide this process by showing how scientific ideas are organized and presented visually. Diagrams from accepted papers provide a rich source of such examples, but their utility as a large-scale reference resource remains underexplored. We introduce DiagramBank, a diagram-text reference resource containing 57,100 scientific diagrams from 26,751 accepted AI/ML papers. Each diagram is paired with its caption, paper title, and abstract, with citing paragraphs included when available. We combine multi-stage filtering, automated checks, and human audits, with an estimated diagram-selection precision of 93.67%. Retrieval experiments show that citing paragraphs complement captions with information specific to individual diagrams. Under a fixed generation pipeline, replacing PaperBanana's curated reference pool with DiagramBank increases the overall PaperBanana score from 4.62 to 7.88 on PaperBananaBench. Retrieved references also outperform random references on BizGenEval scientific figures. Additional evaluations with different judges and renderers, together with a separate blind human evaluation, demonstrate the utility of DiagramBank for automated diagram generation. Code: this https URL.
Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
arXiv:2604.20857 [cs.IR]
(or arXiv:2604.20857v3 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2604.20857 Focus to learn more arXiv-issued DOI via DataCite
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