arXiv Computation and Language August 28, 2026 By Yucheng Zhou, Peng Luo, Qianning Wang, Chengzhong Xu, Jianbing Shen

CARE: Causally-Aligned Reasoning Exploration for Medical Large Language Models

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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.

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