A Pathway to General-Purpose Scientific AI: Multimodal Comprehension of Scientific Images
Abstract
The ALD/E-ImageMiner benchmark and ICDAR 2026 competition advance machine interpretation of scientific figures through tasks spanning visual reading, domain reasoning, and evidential justification, proposing long-term goals for verifiable multimodal scientific AI.
Scientific figures and tables encode essential experimental evidence, yet remain difficult for digital libraries and multimodal AI systems to retrieve and interpret. The ALD/E-ImageMiner benchmark and ICDAR 2026 Competition on Information Extraction from Atomic Layer Deposition/Etching Scientific Figures provide 1,951 figures from 205 publications, expert-annotated for classification, data table extraction, summarization, and visual question answering. In these companion proceedings, we present a forward-looking perspective on how the benchmark can guide future scientific-image challenges. We examine how its tasks probe capabilities from visual and quantitative reading to domain-grounded reasoning and evidential justification, and how Bloom-informed question design can support deeper scientific understanding. We propose "scientific conceptual understanding from images" as a long-term benchmark objective, with future directions including broader domains and figure types, contextual and cross-document synthesis, hypothesis evaluation, provenance, uncertainty, counterfactual grounding, and open-ended multimodal research. This perspective connects the ICDAR 2026 challenge to a broader agenda for machine-actionable scientific visual knowledge and verifiable multimodal scientific AI.
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Scientific figures are more than illustrations—they encode the evidence behind scientific claims. This perspective asks what it would take for multimodal AI to move beyond extracting information from figures toward scientific conceptual understanding from images, with reasoning that is grounded, traceable, and verifiable.
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