MM-BRIGHT: A Multi-Task Multimodal Benchmark for Reasoning-Intensive Retrieval

• URL: http://arxiv.org/abs/2601.09562v2
• Date: Thu, 15 Jan 2026 03:05:41 GMT
• Title: MM-BRIGHT: A Multi-Task Multimodal Benchmark for Reasoning-Intensive Retrieval
• Authors: Abdelrahman Abdallah, Mohamed Darwish Mounis, Mahmoud Abdalla, Mahmoud SalahEldin Kasem, Mostafa Farouk Senussi, Mohamed Mahmoud, Mohammed Ali, Adam Jatowt, Hyun-Soo Kang,
• Abstract summary: MM-BRIGHT is the first multimodal benchmark for reasoning-intensive retrieval.<n>Our dataset consists of 2,803 real-world queries spanning 29 diverse technical domains.
• Score: 18.53521844184766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing retrieval benchmarks primarily consist of text-based queries where keyword or semantic matching is usually sufficient. Many real-world queries contain multimodal elements, particularly, images such as diagrams, charts, and screenshots that require intensive reasoning to identify relevant documents. To address this gap, we introduce MM-BRIGHT, the first multimodal benchmark for reasoning-intensive retrieval. Our dataset consists of 2,803 real-world queries spanning 29 diverse technical domains, with four tasks of increasing complexity: text-to-text, multimodal-to-text, multimodal-to-image, and multimodal-to-multimodal retrieval. Extensive evaluation reveals that state-of-the-art models struggle across all tasks: BM25 achieves only 8.5 nDCG@10 on text-only retrieval, while the best multimodal model Nomic-Vision reaches just 27.6 nDCG@10 on multimodal-to-text retrieval actually underperforming the best text-only model (DiVeR: 32.2). These results highlight substantial headroom and position MM-BRIGHT as a testbed for next-generation retrieval models that better integrate visual reasoning. Our code and data are available at https://github.com/mm-bright/MM-BRIGHT. See also our official website: https://mm-bright.github.io/.

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