arXiv Computer Vision August 25, 2026 By Yucheng Chen, Yang Yu, Jiazhou Zhou, Yufei Shi, Yongying Lan, Yichi Zhang, Liyi Li, Si Yong Yeo

UR$^{2}$-MLLM: Uncertainty-aware Revisit Reasoning in Multimodal Large Language Models for Radiology Report Generation

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The paper introduces UR$^{2}$-MLLM, an uncertainty‑aware multimodal large language model that dynamically revisits uncertain image regions during radiology report generation. It incorporates an uncertainty perception module trained on a specialized dataset, builds a multimodal reasoning trajectory with a detect‑and‑copy mechanism to guide revisits, and refines this behavior using a visual grounding reward via reinforcement learning. Experiments on MIMIC‑CXR and IU‑Xray demonstrate state‑of‑the‑art performance, underscoring the importance of visual revisit reasoning for reliable, clinically aligned reports.

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