Title: ReCLLaMA: A Reasoning-Centered LLM Agent for Medical Diagnosis
Primary area: interpretability and explainable AI
Keywords: Large Language Model, Explainable AI Agent, Knowledge Graph Reasoning, Deduction/Abduction Inference, Medical Diagnosis

Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities in natural language understanding, yet their application to clinical diagnosis remains constrained by hallucinations, limited interpretability, and the absence of formal reasoning mechanisms. To address these limitations, we propose ReCLLaMA, a Reasoning-Centered LLM Agent for Medical Diagnosis, which integrates statistical language models with symbolic inference over structured medical knowledge. ReCLLaMA aligns free-text symptom descriptions with standardized ontologies using pretrained biomedical encoders and performs logical reasoning over heterogeneous knowledge graphs constructed from EHR and pharmacological data. To reconcile representational mismatches across sources, we introduce a statistical entity alignment module based on random forest classification. This enables the construction of a unified knowledge space in which ReCLLaMA applies both deductive and abductive reasoning to derive interpretable diagnostic pathways. Our framework advances the theoretical integration of subsymbolic and symbolic AI in clinical contexts, offering a principled approach to traceable, knowledge-grounded decision-making. Empirical results on real-world datasets validate its superiority over black-box LLMs and rule-based systems in both accuracy and explainability.