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        <title>GRAIL: Compact Multimodal Similarities for Astronomy Citation Linking</title>
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            <namePart type="family">Accomazzi</namePart>
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    <abstract>We present GRAIL, a system for classifying relations between astronomy figures and captions in the AstroCLIMB shared task. GRAIL combines frozen CLIP and SciNCL representations with optical character recognition (OCR), lexical overlap, and figure layout. A factorized gradient-boosted classifier predicts four relation classes from 190 symmetric pair features. The fixed system achieves 0.4802 macro-F1 under cross-validation that separates exact objects across folds and 0.48335 on Kaggle. A variant with nested classifier selection and decision offsets obtains 0.4769 and 0.48264, respectively. Removing OCR or corpus-weighted lexical features lowers local macro-F1 by 0.0081 and 0.0128. These development comparisons support the use of explicit textual evidence alongside semantic representations. Additional model selection gives no observed aggregate gain, and direct citation relations remain the hardest class for both variants.</abstract>
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%0 Conference Proceedings
%T GRAIL: Compact Multimodal Similarities for Astronomy Citation Linking
%A Fedosov, Egor
%Y Accomazzi, Alberto
%Y Ghosal, Tirthankar
%Y Grezes, Felix
%Y Lockhart, Kelly
%Y Alkan, Atilla Kaan
%S Proceedings of the 4th Workshop on Artificial Intelligence for Scientific Publications
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, Zhuhai, China
%@ 979-8-89176-456-9
%F fedosov-2026-grail
%X We present GRAIL, a system for classifying relations between astronomy figures and captions in the AstroCLIMB shared task. GRAIL combines frozen CLIP and SciNCL representations with optical character recognition (OCR), lexical overlap, and figure layout. A factorized gradient-boosted classifier predicts four relation classes from 190 symmetric pair features. The fixed system achieves 0.4802 macro-F1 under cross-validation that separates exact objects across folds and 0.48335 on Kaggle. A variant with nested classifier selection and decision offsets obtains 0.4769 and 0.48264, respectively. Removing OCR or corpus-weighted lexical features lowers local macro-F1 by 0.0081 and 0.0128. These development comparisons support the use of explicit textual evidence alongside semantic representations. Additional model selection gives no observed aggregate gain, and direct citation relations remain the hardest class for both variants.
%U https://aclanthology.org/2026.wasp-1.11/
%P 99-103

Markdown (Informal)

[GRAIL: Compact Multimodal Similarities for Astronomy Citation Linking](https://aclanthology.org/2026.wasp-1.11/) (Fedosov, WASP 2026)

• GRAIL: Compact Multimodal Similarities for Astronomy Citation Linking (Fedosov, WASP 2026)

ACL

• Egor Fedosov. 2026. GRAIL: Compact Multimodal Similarities for Astronomy Citation Linking. In Proceedings of the 4th Workshop on Artificial Intelligence for Scientific Publications, pages 99–103, Hengqin, Zhuhai, China. Association for Computational Linguistics.