Multimodal retrieval relies heavily on single-vector retrievers, which compress rich, sequential token sequences into one single global representation. While efficient, they discard fine-grained, local evidence critical for dense retrieval tasks. Multi-vector approaches were introduced as a solution, but they strictly require training and many ignore the necessity of a globally summarizing representation. To address this, we introduce SMART, a framework that unlocks the latent multi-vector capabilities of standard single-vector models. We first demonstrate that standard contrastive training on the pooled embedding implicitly shapes the retrieval geometry of preceding hidden states via gradient flow. By applying direct late-interaction over these frozen hidden states during inference, SMART acts as a plug-and-play upgrade that consistently improves performance across diverse modalities, improving even the state-of-the-art models further on MMEB-V2. We also reveal SMART's superior performance, as simple lightweight post-training not only saves time and compute, but also brings forth further improvement on Visual Document retrieval, allowing a single-vector model to outperform SoTA multi-vector counterparts. Ultimately, SMART offers both a highly efficient inference enhancement and a powerful finetuning technique for multimodal retrieval. We open source our code and weights at https://github.com/HanSolo9682/SMART.

翻译：多模态检索严重依赖单向量检索器，此类检索器将丰富的顺序令牌序列压缩为单个全局表征。虽然高效，但它们丢弃了密集检索任务中关键的细粒度局部证据。多向量方法作为解决方案被提出，但严格需要训练且许多方法忽略了全局总结表征的必要性。为解决这一问题，我们提出SMART框架，该框架解锁标准单向量模型潜在的多元能力。我们首先证明，基于池化嵌入的标准对比训练通过梯度流隐式塑造了前序隐藏状态的检索几何结构。通过在推理过程中对这些冻结的隐藏状态应用直接的后期交互，SMART作为一种即插即用的升级方案，在不同模态上持续提升性能，甚至在MMEB-V2基准上进一步改进了当前最优模型。我们还揭示了SMART的卓越性能：简单的轻量级后训练不仅节省时间和计算资源，还能在视觉文档检索中带来进一步改进，使得单向量模型超越当前最优的多向量对应模型。最终，SMART为多模态检索提供了一种高效推理增强方案和强大的微调技术。我们在https://github.com/HanSolo9682/SMART开源代码和权重。