Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches

2026-05-31

AI Summary

This study addresses the pronounced lag in the application of reasoning language models beyond the natural sciences, which exacerbates disparities in research productivity across disciplines. It presents the first systematic cross-disciplinary assessment, leveraging the European Research Council’s 28-domain classification scheme alongside a comprehensive literature review and resource maturity analysis to evaluate the current state of development, evaluation, and deployment of reasoning language models. The findings reveal widespread resource scarcity and low maturity levels in non–hard science fields, identify common implementation paradigms and critical challenges, and propose a unified framework for assessing resource maturity. This work offers both theoretical grounding and practical guidance to foster more balanced, multidisciplinary advancement in the adoption of reasoning language models.

Abstract

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.

Problem

• Reasoning Language Models
• scientific disciplines
• research productivity gap
• model adoption disparity
• cross-disciplinary AI

Innovation

• Reasoning Language Models
• cross-disciplinary analysis
• maturity assessment framework
• scientific disciplines
• resource disparity