MedLA: A Logic-Driven Multi-Agent Framework for Complex Medical Reasoning with Large Language Models

Answering complex medical questions requires not only adequate domain knowledge and patient information, but also multi-perspective and in-depth logical tree structure reasoning. Existing multi-intelligentsia approaches usually use fixed roles or simple hints for collaboration, which makes it difficult to detect and resolve fine-grained logical conflicts, leading to incomplete or imprecise conclusions. To this end, we propose MedLA: a logic-driven multi-intelligentsia framework based on a large language model. Each intelligence explicitly represents its reasoning process as a logical tree - a sequence of trinomial reasoning consisting of “major premise, minor premise → conclusion” - and guides the discussion through a multi-round graph. The logical tree is then compared, calibrated, and corrected through a multi-round graph-guided discussion. We also demonstrate that MedLA is better at solving difficult medical problems and has the potential to demonstrate higher performance with larger base models. On both the MedDDx Benchmarks and standard medical quiz benchmarks, MedLA leads substantially in accuracy and robustness compared to both the static role system and the single-intelligent body baseline, setting a new optimal score. The code of HDTree is available at anonymous link https://anonymous.4open.science/status/MedLA_review-1410.