Abstract
Radiology Report Generation (RRG) is a critical step toward automating healthcare workflows, facilitating accurate patient assessments, and reducing the workload of medical professionals. Despite recent progress in Large Medical Vision-Language Models (Med-VLMs), generating radiology reports that are both visually grounded and clinically accurate remains a significant challenge. Existing approaches often rely on large labeled corpora for pre-training, costly task-specific preference data, or retrieval-based knowledge. However, these strategies do not adequately mitigate hallucinations arising from poor cross-modal alignment between visual and linguistic representations. To address these limitations, we propose VALOR: Visual Alignment of Medical Vision-Language Models for GrOunded Radiology Report Generation, which tackles visual hallucinations through two complementary reasoning stages: (1) Clinically Informed Textual Reasoning guides the model with verifiable natural language and clin