Rethinking Domain Specialization for Open-Ended Scientific Reasoning in Astronomy Language Models

Abstract:Domain-specialized language models are widely used for scientific question answering, but stronger general-purpose systems raise a sharper question: when does domain-specific fine-tuning remain valuable for open-ended scientific reasoning? We study this in astronomy with a curated QA benchmark from publicly available 2017--2026 Olympiad-style materials. The free-response subset contains 300 questions, including 204 text-only and 96 image-linked examples. We compare open-weight and API-served general-purpose, multimodal, and astronomy-specialized models using judge-based correctness and complementary reference metrics. Strong general-purpose models establish the highest correctness baseline in this testbed, while analyses of metric agreement, judge sensitivity, benchmark composition, and modality reveal variation not captured by a single leaderboard. These results motivate treating domain specialization as a task- and deployment-dependent property and highlight the role of domain-specific evaluation in determining which models, capabilities, and evaluation criteria are appropriate for scientific workflows.

Instrumentation and Methods for Astrophysics (astro-ph.IM); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)

arXiv:2609.17644 [astro-ph.IM]

(or arXiv:2609.17644v1 [astro-ph.IM] for this version)

https://doi.org/10.48550/arXiv.2609.17644 Focus to learn more arXiv-issued DOI via DataCite

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