Your Embedding Model is SMARTer Than You Think
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 gra...
| Authors | Jianrui Zhang, Hyun Jung Lee, Sukanta Ganguly et al. |
|---|---|
| Primary category | cs.IR |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
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