首份跨28个学科的科研大模型应用分析，揭示领域间差距与潜力。
Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches
- 按欧盟科研委员会分类，系统梳理28个学科中推理大模型的应用现状。
- 发现各学科在资源可用性上存在显著差异，公开资源下差距更明显。
- 适合关注AI赋能多学科科研的研究者与政策制定者阅读。
尽管推理语言模型（RLMs）正迅速成为科学研究的强大工具，但其影响主要集中在“硬科学”领域。其他科学分支对RLM的采纳进展缓慢甚至缺失，导致研究生产力差距不断扩大。本文首次全面分析了28个科学领域的RLM应用情况，依据欧盟科研委员会（ERC）的分类体系，涵盖社会科学与人文学科、物理科学与工程、生命科学。我们考察了各领域中RLM的开发、评估与应用方式。此外，基于现有领域特定的开发与评估资源，提出一种以成熟度为导向的评估框架，揭示出显著的成熟度差异，尤其在仅考虑公开资源时更为突出。最后，我们总结了当前跨学科流行的实施范式、面临的挑战及推动RLM在科学领域普及的未来方向。
原文摘要 · Abstract (English)
While Reasoning Language Models (RLMs) are rapidly emerging as powerful tools for scientific research, their impact is primarily concentrated in "hard science" fields. The slow -- or lack of -- adoption of RLMs in other branches of science is causing a widening gap in research productivity. In this survey, we provide the first comprehensive analysis of RLM adoption across 28 scientific disciplines following the classification used by the European Research Council (ERC), spanning the Social Sciences and Humanities, Physical Sciences and Engineering, and Life Sciences. We examine how RLMs are developed, evaluated, and applied across disciplines. Furthermore, we introduce a maturity-oriented assessment framework based on available domain-specific development and evaluation resources, revealing substantial disparities in RLM maturity that become even more pronounced when only publicly available resources are considered. Finally, we highlight current implementation paradigms that are gaining popularity across disciplines, current challenges, and future directions in enabling RLM adoption across science.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。