Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches
For researchers and policymakers, this work identifies the uneven distribution of RLM resources across scientific disciplines, highlighting the need for targeted efforts to bridge the gap.
This survey analyzes the adoption of Reasoning Language Models (RLMs) across 28 scientific disciplines, revealing substantial disparities in maturity and a widening gap between hard sciences and other fields. It introduces a maturity-oriented assessment framework and highlights challenges and future directions for broader adoption.
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.