CARE: Causally-Aligned Reasoning Exploration for Medical Large Language Models proposes a new framework to improve medical reasoning in LLMs. It introduces two key conditions—Causal Sufficiency and Proximal Learnability—to curate high-quality training trajectories, using agreement-based self-verification and dynamic entropy bounds. Experiments on medical multimodal and text-only benchmarks show that CARE outperforms competitors, reducing incorrect reasoning and enhancing training stability.
By Yucheng Zhou, Peng Luo, Qianning Wang, Chengzhong Xu, Jianbing Shen
arXiv:2608.22622v1 Announce Type: cross
Abstract: Clinical decision-making relies on identifying relevant patient information to guide diagnosis and treatment, a challenge that is especially difficul...
By Miguel Contreras, Scott Siegel, Subhash Nerella, Jessica Sena, Jiaqing Zhang, Heng Sun, Hruday Tej Akkaladevi, Peiyu Lu, Jordan Rosen, Sumit Kapoor, Sasank Desaraju, Grace R. Thompson, Jacob Purcell, Michael Petrauskis, Philip KW. Hong, Meghan Brennan, Sarah Chrabaszcz, Tierra Smith, Ronnie Ren, Michel S. Kabbash, Ceyhun Haziroglu, Rushi Patel, Gabriel Gomez, Charlotte Chaiklin, Randy Leung, Kenneth N. John, Whitman Wiggins, Philip Kayser, Vincent Bird, Maria Bruzzone, Tyler J. Loftus, Azra Bihorac, Parisa Rashidi
arXiv:2609.24480v1 Announce Type: cross
Abstract: Deploying Large Language Models (LLMs) in healthcare requires robust performance across two complementary dimensions - diagnostic reasoning: the conv...
By Kalash Shah, Kunal Singh, Snehan J, Shreyas Singh
arXiv:2609.39566v1 Announce Type: new
Abstract: Foundation models can serve as clinical agents through tool-use harnesses. However, conventional medical benchmarks assess reasoning over preselected e...
By Minye Shao, Chaohui Yu, Yixuan Wu, Fan Wang, Ling Shao, Yang Long
arXiv:2606. 02802v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model structured longitudinal electronic health records (EHRs).
By Bo-Hong Wang, Baicheng Peng, Ruilin Wang, Jun Bai, Ziyang Song, Yue Li
arXiv:2609.22161v1 Announce Type: cross
Abstract: Medical large language models are commonly trained on mixtures of didactic data (e.g., textbooks) and clinical data (e.g., patient records), yet how...
By Yuzheng Fan, Haochun Wang, Sendong Zhao, Xiao Han, Ming Ma, Bing Qin