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PerceptRL is presented, which strengthens perception at two complementary levels in multimodal large language models by using a multi-agent pipeline to extract verified errors from dense image captions and convert the corresponding visual facts into QA pairs with fixed answer labels and rule-based rewards.
Scene Text Retrieval (STR) aims to search images containing a given textual query within large-scale image collections. However, existing approaches are fundamentally constrained in two ways: 1) they are evaluated on narrow benchmarks that focus primarily on natural scenes; and 2) they rely on either error-prone multi-...
VaRS-Doc is proposed, a visual document retrieval framework that diversifies document representations by enabling the model to actively explore variant latent interpretations during document encoding, while preserving efficient late-interaction retrieval in which each query adaptively selects the best-fit representatio...
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