RNED：面向医学视觉语言模型的旋转数值编码与解码
RNED: Rotary Number Encoding and Decoding for Medical VLMs
RNED 将连续数值编码为旋转矩阵作用于专用数值词元嵌入，并设计基于分数匹配的解码器，可直接用于视觉语言模型中的坐标回归、尺寸估计等连续量预测任务，替代离散化词元方案。
原文依据 we propose Rotary Number Encoding and Decoding (RNED), a principled method for embedding continuous numerical values directly in the representation space of a VLM.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论视觉语言模型（VLM）正越来越多地应用于医学领域，但其临床实用性受限于定量推理方面的核心弱点。这一局限影响从病灶尺寸回归到边界框坐标预测等任务，根源在于大语言模型（LLM）所依赖的离散词元化方案。为此，我们提出旋转数值编码与解码（RNED），一种将连续数值直接嵌入 VLM 表示空间的原则性方法。类似于旋转位置编码，RNED 通过将数值特定的旋转矩阵作用于专用的数值词元嵌入来表示标量。这种保范变换在宽数值范围内保持序结构，并与预训练模型权重无缝集成。在解码方面，我们引入一种基于分数匹配的鲁棒方案，在存在随机噪声的情况下从隐藏状态中恢复连续值。我们在两个定量任务上评估 RNED：放射学测量估计和医学视觉定位。在内部和公开基准上，RNED 均持续优于现有的 VLM 基线。这些结果表明，RNED 为医学 VLM 中的数值推理提供了一种鲁棒且可泛化的解决方案，使模型既在定量上可靠又具备临床适用性。
英文原文摘要 1400 字符
Vision-Language Models (VLMs) are increasingly adopted for medical applications, but their clinical utility is limited by a core weakness in quantitative reasoning. This limitation affects tasks ranging from regression of lesion sizes to prediction of bounding-box coordinates and stems from the discrete tokenization schemes underlying Large Language Models (LLMs). To address this, we propose Rotary Number Encoding and Decoding (RNED), a principled method for embedding continuous numerical values directly in the representation space of a VLM. Analogous to rotary position encoding, RNED represents a scalar by applying a number-specific rotation matrix to a dedicated numeric token embedding. This norm-preserving transformation maintains ordinal structure over a wide numerical range and integrates seamlessly with pretrained model weights. For decoding, we introduce a robust score-matching-based scheme to recover continuous values from hidden states in the presence of stochastic noise. We evaluate RNED on two quantitative tasks: radiological measurement estimation and medical visual grounding. On both internal and public benchmarks, RNED consistently outperforms existing VLM baselines. Together, these results show that RNED offers a robust, generalizable solution for numerical reasoning in medical VLMs, enabling models that are both quantitatively reliable and clinically applicable.