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UEmbed: Unified Sparse and Dense Multimodal Embeddings

Research Multimodal & Generative

Ranking

Overall 77
Content 80
Popularity 69

Observed public metrics from 1 member.

Merged summary

TL;DR - UEmbed is a decoder-only multimodal embedding model that emits both learned-sparse lexical and dense representations in a single causal forward pass, removing LSR's dependence on bidirectional encoders and auxiliary cross-modal modules. It matters because it collapses two retrieval paradigms (sparse + dense) and two modalities (text + image) into one unified model.

  • Appends N learnable special tokens and partitions the vocabulary into N disjoint subsets; each token's causal hidden state predicts sparse weights over its subset, and the subsets concatenate into the full sparse vector.
  • Released at 2B, 4B, and 9B scales, trained only on public data.
  • UEmbed-9B scores 71.8 (dense) and 71.0 (sparse) on MMEB-v2, outperforming public-data multimodal embedding models such as RzenEmbed; stays competitive with strong dense and sparse baselines on BEIR.
  • Authors evaluate utility along effectiveness, efficiency, and agentic applications, positioning it for RAG and search pipelines.

Sources (1)

UEmbed: Unified Sparse and Dense Multimodal Embeddings

arXiv cs.CV Tingyu Song, Mingxin Li, Yanzhao Zhang, Dingkun Long, Pengjun Xie, Zhijie Nie, Yilun Zhao, Shu Wu 2026-08-03 arXiv:2608.02583
Public signals Hugging Face upvotes 52
Providers: Hugging Face · Upvotes 52 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-03 14:33:25.750073 UTC

TL;DR - UEmbed is a decoder-only multimodal embedding model that emits both learned-sparse lexical and dense representations in a single causal forward pass, removing LSR's dependence on bidirectional encoders and auxiliary cross-modal modules. It matters because it collapses two retrieval paradigms (sparse + dense) and two modalities (text + image) into one unified model.

  • Appends N learnable special tokens and partitions the vocabulary into N disjoint subsets; each token's causal hidden state predicts sparse weights over its subset, and the subsets concatenate into the full sparse vector.
  • Released at 2B, 4B, and 9B scales, trained only on public data.
  • UEmbed-9B scores 71.8 (dense) and 71.0 (sparse) on MMEB-v2, outperforming public-data multimodal embedding models such as RzenEmbed; stays competitive with strong dense and sparse baselines on BEIR.
  • Authors evaluate utility along effectiveness, efficiency, and agentic applications, positioning it for RAG and search pipelines.
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