Argus achieves the highest reported NDCG scores among open late-interaction models on ViDoRe V1 and combined V1+V2 by introducing query-dependent document representations via a region-aware MoE on Qwen3.5-VL, trained on 9% of public data with a 1024-dim head.
arXiv preprint arXiv:2505.11651 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
citing papers explorer
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Argus-Retriever: Vision-LLM Late-Interaction Retrieval with Region-Aware Query-Conditioned MoE for Visual Document Retrieval
Argus achieves the highest reported NDCG scores among open late-interaction models on ViDoRe V1 and combined V1+V2 by introducing query-dependent document representations via a region-aware MoE on Qwen3.5-VL, trained on 9% of public data with a 1024-dim head.
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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.