mlsanityData to 7 Oct 2026

Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches

arXiv 2606.01145 ↗First posted 31 May 2026cs.AIcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 139 of its 364 references that are in the map. First posted on arXiv 31 May 2026.

Builds on

139 of 364 references.

Closest ancestors · 5

Same direction, newest first

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  4. 2025Reasoning Over the Glyphs: Evaluation of LLM's Decipherment of Rare Scripts29 Jan
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Other references · 134

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  33. 2024TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Datacited by 19
  34. 2025FineMedLM-o1: Enhancing Medical Knowledge Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Trainingcited by 19
  35. 2025MatterChat: A Multi-Modal LLM for Material Sciencecited by 18
  36. 2025Sparks: Multi-Agent Artificial Intelligence Model Discovers Protein Design Principlescited by 16
  37. 2025Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoningcited by 16
  38. 2025Probing the Critical Point (CritPt) of AI Reasoning: a Frontier Physics Research Benchmarkcited by 16

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