We study whether grounded reasoning supervision from abundant 2D medical images can improve 3D medical VQA when both input types are aligned through a common reasoning interface. We introduce UniReason-Med, a single-checkpoint framework that processes either a 2D image or a slice-serialized 3D volume at inference time, generating interleaved textual reasoning and localized visual evidence through shared box syntax, region-token injection, and a common grounded reasoning policy. To train this interface, we construct UniMed-CoT, a 220K instruction-tuning dataset with interleaved textual reasoning and grounded visual evidence, including 170K 2D and 50K 3D samples. Through supervised fine-tuning followed by outcome-level reinforcement learning, UniReason-Med learns to generate grounded reasoning traces without IoU/Dice-based localization rewards during RL. Data-mixture and component ablations show that joint 2D+3D grounded supervision substantially improves 3D reasoning over 3D-only training, while grounding and region-token injection consistently benefit both 2D and 3D tasks. These results suggest that a shared grounded reasoning interface can transfer reasoning structure from 2D images to slice-serialized volumetric medical understanding. The code and data are publicly available at https://github.com/IQuestLab/unireason-med.

翻译：我们研究了当输入类型通过公共推理接口对齐时，来自丰富二维医学图像的基于定位的推理监督能否提升三维医学视觉问答的性能。我们提出UniReason-Med，这是一个单检查点框架，在推理时可处理二维图像或切片序列化的三维体数据，通过共享框语法、区域令牌注入和公共基础推理策略生成交错文本推理与局部视觉证据。为训练该接口，我们构建了UniMed-CoT数据集，包含22万条指令微调样本，其中涵盖17万条二维样本与5万条三维样本，均具备交错文本推理与基础视觉证据。在监督微调后结合结果级强化学习，UniReason-Med学会了生成基础推理轨迹，而无需在强化学习阶段使用基于IoU/Dice的定位奖励。数据混合与模块消融实验表明，相比仅使用三维数据训练，二维与三维联合的定位监督能显著改善三维推理任务的表现；同时，定位机制与区域令牌注入对二维与三维任务均有持续增益。这些结果表明，共享基础推理接口可实现推理结构从二维图像向切片序列化三维体医学理解的有效迁移。代码与数据已开源至https://github.com/IQuestLab/unireason-med。