Abstract
Multimodal retrieval-augmented generation over visually rich documents requires evidence at a finer granularity than entire pages. Existing visual retrievers typically index or return full pages, whereas training region-level retrievers often requires costly query-to-region annotations or noisy model-generated labels. We introduce GARRET, a region-retrieval framework trained from (page, question, answer) triplets through differentiable generative feedback. A shared context-preserving retriever produces page and region multi-vector representations. During training, their retrieval scores define a soft routing distribution over candidate region crops. An auxiliary vision-language model attends to each crop separately, aggregates crop-specific contexts using the routing weights, and predicts the question followed by the answer under a next-token prediction objective. Gradients through the routing distribution assign query-conditioned credit to the indexed region representations, without requiring query-to-region annotations. At inference, the auxiliary model is removed. GARRET also reuses the contextualized region representations, together with page summary tokens, to construct a compact page representation. We introduce ViDoRe-V3-Region, a corpus-wide benchmark containing 200,682 fixed candidate regions across eight domains. GARRET achieves a macro nDCG@10 of 0.4126, outperforming the strongest visual baseline by 2.94 points. On page retrieval, it remains comparable to ColQwen2.5-v0.2 while indexing 47.8% fewer vectors per page.
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URL
https://arxiv.org/abs/2610.27829
