ColMate: Contrastive Late Interaction and Masked Text for Multimodal Document Retrieval

• URL: http://arxiv.org/abs/2511.00903v1
• Date: Sun, 02 Nov 2025 11:51:20 GMT
• Title: ColMate: Contrastive Late Interaction and Masked Text for Multimodal Document Retrieval
• Authors: Ahmed Masry, Megh Thakkar, Patrice Bechard, Sathwik Tejaswi Madhusudhan, Rabiul Awal, Shambhavi Mishra, Akshay Kalkunte Suresh, Srivatsava Daruru, Enamul Hoque, Spandana Gella, Torsten Scholak, Sai Rajeswar,
• Abstract summary: ColMate is a document retrieval model that bridges the gap between multimodal representation learning and document retrieval.<n>ColMate obtains 3.61% improvements over existing retrieval models on the ViDoRe V2 benchmark.
• Score: 21.39502089420643
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-augmented generation has proven practical when models require specialized knowledge or access to the latest data. However, existing methods for multimodal document retrieval often replicate techniques developed for text-only retrieval, whether in how they encode documents, define training objectives, or compute similarity scores. To address these limitations, we present ColMate, a document retrieval model that bridges the gap between multimodal representation learning and document retrieval. ColMate utilizes a novel OCR-based pretraining objective, a self-supervised masked contrastive learning objective, and a late interaction scoring mechanism more relevant to multimodal document structures and visual characteristics. ColMate obtains 3.61% improvements over existing retrieval models on the ViDoRe V2 benchmark, demonstrating stronger generalization to out-of-domain benchmarks.

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