[Submitted on 15 Oct 2026]
Title:Star Light, Star Bright: Can Multimodal Models Parse an Astronomy Visual Question Answering Dataset Right?
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Abstract:Figures are essential for scientific communication, and the field of Visual Question Answering (VQA) aims to test the figure understanding capabilities of large multimodal models (LMMs). Here, we investigate the accuracy of several low-cost LMMs on domain-specific VQA tasks for astronomical images of the sky. As instrument for this task we build a domain-specific VQA dataset comprising 2001 partially-synthetic figures divided evenly between contour plots, real telescope images drawn from NASA's SkyView, and "fake" sky images which share the axes and angular scales of the real images but draw their colors from a gaussian mixture model, alongside 47357 questions. Each figure is released as pristine and with realistic page-aging degradations mimicking the historical astronomical corpus. Testing three low-cost proprietary LMMs, we find models frequently fail to answer domain-specific questions at high accuracy. An initial comparison with 104 human-verified, astrometrically tagged figures from historical journal publications shows similar median errors in angular extent. All code is available on GitHub: this https URL and dataset is available on HuggingFace: this https URL.
Comments: 5 main pages, 12 pages total, accepted to The 4th Annual Workshop for Artificial Intelligence for Scientific Publications (WASP) 2026, International Joint Conference on Natural Language Processing & Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL)
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Instrumentation and Methods for Astrophysics (astro-ph.IM); Digital Libraries (cs.DL); Information Retrieval (cs.IR)
Cite as: arXiv:2610.22474 [cs.CV]
(or arXiv:2610.22474v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.22474
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jill Naiman [view email] [v1] Thu, 15 Oct 2026 18:00:08 UTC (3,960 KB)
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