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        <title>Parameter-Efficient Adaptation of Qwen3-VL for Astronomy Citation Linking at AstroCLIMB 2026</title>
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    <abstract>We describe a submission to the AstroCLIMB shared task at WASP 2026. The task requires classifying a pair of astronomy figures and/or captions as the same figure, from the same paper, from citation-related papers, or from unrelated papers. Our system casts classification as constrained next-token prediction with Qwen3-VL and adapts the model using 4-bit quantization and low-rank adapters. On the 10,000-pair Kaggle test set, three Qwen3-VL-8B models with language-attention adapters obtain 0.73230–0.73307 macro-F1 individually. Averaging their four-class probabilities raises the score to 0.73604, and masking the impossible same-figure class for caption–caption and image–image pairs produces 0.73725. This paper documents empirical system engineering rather than proposing a new learning algorithm; broader visual adaptation, restricted-loss blending, and citation-focused auxiliary training do not improve the result.</abstract>
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%0 Conference Proceedings
%T Parameter-Efficient Adaptation of Qwen3-VL for Astronomy Citation Linking at AstroCLIMB 2026
%A Hoque, Syed Mohaiminul
%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 hoque-2026-parameter
%X We describe a submission to the AstroCLIMB shared task at WASP 2026. The task requires classifying a pair of astronomy figures and/or captions as the same figure, from the same paper, from citation-related papers, or from unrelated papers. Our system casts classification as constrained next-token prediction with Qwen3-VL and adapts the model using 4-bit quantization and low-rank adapters. On the 10,000-pair Kaggle test set, three Qwen3-VL-8B models with language-attention adapters obtain 0.73230–0.73307 macro-F1 individually. Averaging their four-class probabilities raises the score to 0.73604, and masking the impossible same-figure class for caption–caption and image–image pairs produces 0.73725. This paper documents empirical system engineering rather than proposing a new learning algorithm; broader visual adaptation, restricted-loss blending, and citation-focused auxiliary training do not improve the result.
%U https://aclanthology.org/2026.wasp-1.12/
%P 104-111

Markdown (Informal)

[Parameter-Efficient Adaptation of Qwen3-VL for Astronomy Citation Linking at AstroCLIMB 2026](https://aclanthology.org/2026.wasp-1.12/) (Hoque, WASP 2026)

• Parameter-Efficient Adaptation of Qwen3-VL for Astronomy Citation Linking at AstroCLIMB 2026 (Hoque, WASP 2026)

ACL

• Syed Mohaiminul Hoque. 2026. Parameter-Efficient Adaptation of Qwen3-VL for Astronomy Citation Linking at AstroCLIMB 2026. In Proceedings of the 4th Workshop on Artificial Intelligence for Scientific Publications, pages 104–111, Hengqin, Zhuhai, China. Association for Computational Linguistics.