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

ACL ARR 2026 January Submission4687 Authors

Keywords: Multilingual Medical Reasoning, Medical Evaluation Benchmark, Medical Reasoning, Multilingual Reasoning

Abstract: 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, 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 practice-grounded expertise encoded in local languages. To evaluate multilingual medical reasoning beyond multiple-choice settings, we construct MultiMed-X, 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 \sysName improves multilingual reasoning performance by an average of 5\%, with particularly substantial gains in low-resource languages. Moreover, model distillation and expert evaluation analysis further confirm that Med-CoReasoner produces clinically sound and culturally grounded reasoning traces.

Paper Type: Long

Research Area: Multilinguality and Language Diversity

Research Area Keywords: Language Modeling,Multilingualism and Cross-Lingual NLP

Contribution Types: Approaches to low-resource settings, Data resources

Languages Studied: English,Chinese,Japanese,Germany,French,Spanish,Italian,Swahili,Thai,Yoruba,Zulu

Submission Number: 4687