MED-COREASONER: Reducing Language Disparities in Medical Reasoning via Language-Informed Co-Reasoning

While reasoning-enhanced large language models perform strongly on English medical tasks, a persistent multilingual gap remains, with substantially weaker reasoning in local languages, limiting equitable global medical deployment. To bridge this gap, we introduce MED-COREASONER 1 , a language-informed co-reasoning framework that elicits parallel English and local-language reasoning, abstracts them into structured concepts, and integrates local clinical knowledge into an English logical scaffold via concept-level alignment and retrieval. This design combines the structural robustness of English reasoning with the practicegrounded expertise encoded in local languages. To evaluate multilingual medical reasoning beyond multiple-choice settings, we construct MultiMed-X 2 , a benchmark covering seven languages with expert-annotated long-form question answering and natural language inference tasks, comprising 350 instances per language. Experiments across three benchmarks show that MED-COREASONER improves multilingual reasoning performance by an average of 5%, with particularly substantial gains in lowresource languages. Moreover, model distillation and expert evaluation analysis further confirm that MED-COREASONER produces clinically sound and culturally grounded reasoning traces.