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        <title>Public Scientific-Figure Adaptation for AstroCLIMB with a 4B Vision–Language Model</title>
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        <namePart type="given">Irving</namePart>
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            <namePart type="given">Alberto</namePart>
            <namePart type="family">Accomazzi</namePart>
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        <name type="personal">
            <namePart type="given">Tirthankar</namePart>
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    <abstract>We adapt Qwen3.5-4B to the AstroCLIMB task, which asks whether two scientific figures or captions come from the same figure, the same paper, citation-linked papers, or unrelated papers. For caption–image pairs, we first adapt the model on 80,000 public pairs labeled automatically from figure, paper, and citation structure in the public corpus, then train on gold data. Across three gold-training seeds that share one public checkpoint, this initialization improves caption–image development macro-F1 by 0.067 on average over gold-only training under matched gold schedules, although with more total compute. Answer generation and the reinforcement-learning variants we tested do not beat the classifier. The combined system of three modality-specific classifiers reaches a pooled development macro-F1 of 0.756 and a Kaggle leaderboard score of 0.751. Repeated development selection and earlier project exposure to competition materials confine these findings to the evaluated setting, and we document both.</abstract>
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%0 Conference Proceedings
%T Public Scientific-Figure Adaptation for AstroCLIMB with a 4B Vision–Language Model
%A Ramírez, Irving Ernesto Quezada
%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 ramirez-2026-public
%X We adapt Qwen3.5-4B to the AstroCLIMB task, which asks whether two scientific figures or captions come from the same figure, the same paper, citation-linked papers, or unrelated papers. For caption–image pairs, we first adapt the model on 80,000 public pairs labeled automatically from figure, paper, and citation structure in the public corpus, then train on gold data. Across three gold-training seeds that share one public checkpoint, this initialization improves caption–image development macro-F1 by 0.067 on average over gold-only training under matched gold schedules, although with more total compute. Answer generation and the reinforcement-learning variants we tested do not beat the classifier. The combined system of three modality-specific classifiers reaches a pooled development macro-F1 of 0.756 and a Kaggle leaderboard score of 0.751. Repeated development selection and earlier project exposure to competition materials confine these findings to the evaluated setting, and we document both.
%U https://aclanthology.org/2026.wasp-1.17/
%P 154-165

Markdown (Informal)

[Public Scientific-Figure Adaptation for AstroCLIMB with a 4B Vision–Language Model](https://aclanthology.org/2026.wasp-1.17/) (Ramírez, WASP 2026)

• Public Scientific-Figure Adaptation for AstroCLIMB with a 4B Vision–Language Model (Ramírez, WASP 2026)

ACL

• Irving Ernesto Quezada Ramírez. 2026. Public Scientific-Figure Adaptation for AstroCLIMB with a 4B Vision–Language Model. In Proceedings of the 4th Workshop on Artificial Intelligence for Scientific Publications, pages 154–165, Hengqin, Zhuhai, China. Association for Computational Linguistics.