ColChunk adaptively chunks visual document patches into contextual multi-vectors via clustering, cutting storage by over 90% while raising average nDCG@5 by 9 points.
ModernVBERT: To- wards smaller visual document retrievers.arXiv preprint arXiv:2510.01149
6 Pith papers cite this work. Polarity classification is still indexing.
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A contextual multimodal document retrieval benchmark (CMDR-Bench) and embedding model (CMDR-Embed) that jointly encodes multiple document pages and splits them into page-level representations, trained with a context-aware contrastive objective, outperforming non-contextual baselines by 13–16 nDCG@5.
MM-Matryoshka is a 2D Matryoshka training framework enabling budget-elastic ColPali-style multi-vector visual document retrieval along dimension and layer without separate models per budget.
ReAlign improves visual document retrieval by training retrievers to match query-induced rankings with rankings derived from VLM-generated, region-focused descriptions of relevant page content.
LEMUR accelerates multi-vector retrieval by learning a neural network approximation to MaxSim and reducing it to single-vector search in latent space.
A user-diversity condition is necessary and sufficient for personalized alignment to achieve O(1) online regret and log(1/epsilon) offline sample complexity.
citing papers explorer
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Visual Late Chunking: An Empirical Study of Contextual Chunking for Efficient Visual Document Retrieval
ColChunk adaptively chunks visual document patches into contextual multi-vectors via clustering, cutting storage by over 90% while raising average nDCG@5 by 9 points.
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CMDR: Contextual Multimodal Document Retrieval
A contextual multimodal document retrieval benchmark (CMDR-Bench) and embedding model (CMDR-Embed) that jointly encodes multiple document pages and splits them into page-level representations, trained with a context-aware contrastive objective, outperforming non-contextual baselines by 13–16 nDCG@5.
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MM-Matryoshka: Towards Budget-Elastic Visual Document Retrieval via a 2D Multimodal Matryoshka Training Framework
MM-Matryoshka is a 2D Matryoshka training framework enabling budget-elastic ColPali-style multi-vector visual document retrieval along dimension and layer without separate models per budget.
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ReAlign: Optimizing the Visual Document Retriever with Reasoning-Guided Fine-Grained Alignment
ReAlign improves visual document retrieval by training retrievers to match query-induced rankings with rankings derived from VLM-generated, region-focused descriptions of relevant page content.
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LEMUR: Learned Multi-Vector Retrieval
LEMUR accelerates multi-vector retrieval by learning a neural network approximation to MaxSim and reducing it to single-vector search in latent space.
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Personalized Alignment Revisited: The Necessity and Sufficiency of User Diversity
A user-diversity condition is necessary and sufficient for personalized alignment to achieve O(1) online regret and log(1/epsilon) offline sample complexity.