UEmbed (Unified Embedding), a decoder-only multimodal embedding model that produces both sparse lexical and dense representations in one causal forward pass, offers a new paradigm: it unifies dense and sparse embeddings in one model, while further extending sparse retrieval to unify text and multimodal inputs.
Tingyu Song, Mingxin Li, Yanzhao Zhang et al.· 2 citations
CORE is proposed, which synthesizes candidate lists spanning five compositional matching levels and introduces a Rank-KL objective that trains the embedding model to reproduce the reranker's fine-grained ranking and compares contrastive learning, pairwise CoSENT, and listwise Rank-KL under the same data and tuning budg...
Tingyu Song, Mingxin Li, Yanzhao Zhang et al.· 0 citations
This work introduces VisDocAgentBench, a closed-corpus benchmark comparing static and agentic retrieval under a shared ranked-output contract, and motivates retrieval agents that combine modality-preserving discovery with evidence-directed verification.
Lexiang Hu, Yanzhao Zhang, Mingxin Li et al.· 0 citations
Document Embedding Preservation Tuning (DEPT) is introduced, which keeps tuned document embeddings close to cached initial embeddings while allowing retrieval gradients to pass through straight-through decoding into the generator.
Jingyuan Wang, Richong Zhang, Zhijie Nie et al.· 0 citations
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