MedVR：通过代理强化学习实现无标注的医学视觉推理
MedVR: Annotation-Free Medical Visual Reasoning via Agentic Reinforcement Learning
AI总结：
MedVR通过代理强化学习实现无标注的医学视觉推理，利用熵引导的视觉重定位和基于共识的信用分配机制，在多个公开医学VQA基准上取得最佳性能，提升医疗AI的鲁棒性和透明度。
摘要：
医学视觉-语言模型（VLMs）在复杂临床任务中具有巨大潜力，但其推理能力常受限于仅依赖文本的范式，无法将推理结果与视觉证据相结合。为此，我们提出MedVR，一种新的强化学习框架，使医学VLMs能够实现无标注的视觉推理。其核心创新在于两个协同机制：熵引导的视觉重定位（EVR）利用模型不确定性引导探索，而基于共识的信用分配（CCA）从滚动协议中提炼伪监督。无需任何人工标注的中间步骤，MedVR在多个公开医学VQA基准上实现了最先进的性能，显著优于现有模型。通过直接学习与视觉证据进行推理，MedVR促进了医疗AI临床部署所需的鲁棒性和透明度。
英文摘要：
Medical Vision-Language Models (VLMs) hold immense promise for complex clinical tasks, but their reasoning capabilities are often constrained by text-only paradigms that fail to ground inferences in visual evidence. This limitation not only curtails performance on tasks requiring fine-grained visual analysis but also introduces risks of visual hallucination in safety-critical applications. Thus, we introduce MedVR, a novel reinforcement learning framework that enables annotation-free visual reasoning for medical VLMs. Its core innovation lies in two synergistic mechanisms: Entropy-guided Visual Regrounding (EVR) uses model uncertainty to direct exploration, while Consensus-based Credit Assignment (CCA) distills pseudo-supervision from rollout agreement. Without any human annotations for intermediate steps, MedVR achieves state-of-the-art performance on diverse public medical VQA benchmarks, significantly outperforming existing models. By learning to reason directly with visual evidence, MedVR promotes the robustness and transparency essential for accelerating the clinical deployment of medical AI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
