מפורקים למספרי רפואיים: פיתוח מודלי שפה גדולים רב-תפקודיים עם ספרות רפואית

תקציר מקורי באנגליתarXiv:2511.22232v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are increasingly capable in medical imaging, yet most focus on single-image settings. Clinical interpretation often requires integrating evidence across multiple images, such as different modalities, views, or time points. However, large-scale medical multi-image data and training strategies for such reasoning remain limited. We construct PMC-MI, a large-scale resource comprising 234,956 instruction instances derived from biomedical compound figures, with 10,555 multi-subimage instances structured for reinforcement learning and assessed by medical reviewers. We also introduce PMC-MI-Bench, a manually reviewed benchmark separated at the source-article level. We further propose a three-stage tr