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
4 Pith papers cite this work. Polarity classification is still indexing.
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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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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.