Recent advancements in Large Language Models (LLMs) have demonstrated remarkable capabilities in various domains. However, effective decision-making relies heavily on strong reasoning abilities. Reasoning is the foundation for decision-making, providing the analytical and logical framework to make sound choices. Reasoning involves analyzing information, drawing inferences, and reaching conclusions based on logic or evidence. Decision-making builds on this foundation by applying the insights from reasoning to select the best course of action among alternatives. Together, these processes create a continuous cycle of thought and action aimed at achieving goals effectively. As AI technology evolves, there is a growing trend to train LLMs to excel in general reasoning. This study explores how the general reasoning capabilities of LLMs connect to their performance in domain-specific reasoning tasks.
@article{arxiv.2506.21580,
title = {From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models},
author = {Dana Alsagheer and Yang Lu and Abdulrahman Kamal and Omar Kamal and Mohammad Kamal and Nada Mansour and Cosmo Yang Wu and Rambiba Karanjai and Sen Li and Weidong Shi},
journal= {arXiv preprint arXiv:2506.21580},
year = {2025}
}