Visual document retrieval requires expressive representations to match queries with evidence distributed across text, tables, and page layouts. Multi-vector representations capture fine-grained information, but storing and comparing many vectors introduces substantial retrieval costs. In this paper, we introduce RandSl...
De-Wen Guo, Shi Yu, Ling-Xiao Zhang et al.· 0 citations
ConceptFormer models query-relevant evidence as continuous, query-conditioned latent concepts that explicitly bridge localized visual evidence and semantic relevance, without requiring either textual intermediate representations or direct reliance on raw visual annotations.
Chunyi Peng, Zhi-Peng Xu, Yu-Kun Yan et al.· 0 citations
KARE-RAG (Knowledge-Aware Refinement and Enhancement for RAG), a training-time scaffolded alignment framework that uses structured knowledge representations as temporary scaffolds to expose evidence organization, support localized factual refinement, and construct fine-grained preference pairs is presented.
Yongjian Li, Haocheng Chu, Yu-Kun Yan et al.· arXiv.org· 0 citations
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