摘要：视觉—语言模型必须将视觉实体与文本属性建立关联。随着输入语言发生变化，这些关联或概念绑定能否保持稳定，尚未得到研究。我们提出了 M$^2$BIND，这是一个在多种语言之间改变上下文和查询语言的基准测试。我们通过任务性能指标进行外在评估，并通过因果干预进行内在评估，以衡量绑定能力。我们发现，绑定并不具有语言不变性：跨语系和跨文字系统的设定会引发显著的绑定崩溃，模型内部的绑定计算会转移到更靠后的层，并失去因果强度。关系更为接近的语言相对更能保持关联。更广泛地说，我们的发现表明，在全球多语言环境中部署的 VLM，不能被假定能够维持单语评估中观察到的相同关联质量。

Abstract:Vision-language models must associate visual entities with textual attributes. Whether these associations or concept bindings remain stable when the language of the input changes is unexplored. We introduce M$^2$BIND, a benchmark varying the language of the context and query across multiple languages. We evaluate binding both extrinsically through task performance metrics and intrinsically through causal interventions. We find that binding is not language-invariant: cross-family and cross-script settings trigger significant binding collapse, with the model's internal binding computation shifting to later layers and losing causal strength. Closely related languages preserve associations comparatively better. In a broader sense, our findings indicate how VLMs deployed globally in multilingual settings cannot be assumed to maintain the same association quality observed in monolingual evaluation.