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arXiv · 2605.30917

Inference-Free Multimodal Learned Sparse Retrieval for Production-Scale Visual Document Search

Gyu-Hwung Cho·Youngjune Lee·Kiyoon Jeong·Siyoung Lee·Sanggyu Han·Hervé Dejean·Stéphane Clinchant·Seung-won Hwang
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Abstract

As large-scale visual-document corpora such as arXiv papers and enterprise PDFs continue to grow, visual-document retrieval has gained increasing attention; yet it still lacks a deployable system that lexically indexes visual documents to serve queries without neural encoding at scale. Existing methods either achieve strong retrieval quality with VLM-based dense or multi-vector models but require neural query encoding at serving time, or avoid query encoding with OCR- or caption-based BM25 at the cost of time-consuming text extraction or generation. To fill this missing serving regime, we present V-SPLADE, an inference-free sparse retriever for visual-document retrieval. However, such inference-free multimodal learned sparse retrieval systems remain underexplored and have not yet shown dense-level effectiveness under high sparsity. We attribute this limitation to a lexical grounding problem: visual sparse representations often fail to capture the lexical content embedded in document images. To address this problem, we introduce caption-gated token supervision, a training-only signal that uses VLM-generated captions as lexical cues to activate retrieval-relevant vocabulary dimensions. With this supervision, V-SPLADE improves average NDCG@5 across six visual-document retrieval benchmarks by +13.8pp over the same-scale dense baseline and by up to +6.3pp over OCR- or caption-based BM25 baselines. On an 18.7M-document corpus, it more than doubles R@5 over the same-scale dense baseline and further improves competing retrievers through score fusion by up to +2.4pp R@5. Code will be released soon at https://github.com/naver/v-splade.

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Gyu-Hwung Cho, Youngjune Lee, Kiyoon Jeong, Siyoung Lee, Sanggyu Han, Hervé Dejean, Stéphane Clinchant, Seung-won Hwang. 2026-05-29. Inference-Free Multimodal Learned Sparse Retrieval for Production-Scale Visual Document Search. https://arxiv.org/abs/2605.30917

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