CRAG-MM: Multi-modal Multi-turn Comprehensive RAG Benchmark
Authors: name: Jiaqi Wang, name: Xiao Yang, name: Kai Sun, name: Parth Suresh, name: Sanat Sharma, name: Adam Czyzewski, name: Derek Andersen, name: Surya Appini, name: Arkav Banerjee, name: Sajal Choudhary, name: Shervin Ghasemlou, name: Ziqiang Guan, name: Akil Iyer, name: Haidar Khan, name: Lingkun Kong, name: Roy Luo, name: Tiffany Ma, name: Zhen Qiao, name: David Tran, name: Wenfang Xu, name: Skyler Yeatman, name: Chen Zhou, name: Gunveer Gujral, name: Yinglong Xia, name: Shane Moon, name: Nicolas Scheffer, name: Nirav Shah, name: Eun Chang, name: Yue Liu, name: Florian Metze, name: Tammy Stark, name: Zhaleh Feizollahi, name: Andrea Jessee, name: Mangesh Pujari, name: Ahmed Aly, name: Babak Damavandi, name: Rakesh Wanga, name: Anuj Kumar, name: Rohit Patel, name: Wen-tau Yih, name: Xin Luna Dong
Organization: AI at Meta

Wearable devices such as smart glasses are transforming the way people
interact with their surroundings, enabling users to seek information regarding
entities in their view. Multi-Modal Retrieval-Augmented Generation (MM-RAG)
plays a key role in supporting such questions, yet there is still no
comprehensive benchmark for this task, especially regarding wearables
scenarios. To fill this gap, we present CRAG-MM -- a Comprehensive RAG
benchmark for Multi-modal Multi-turn conversations. CRAG-MM contains a diverse
set of 6.5K (image, question, answer) triplets and 2K visual-based multi-turn
conversations across 13 domains, including 6.2K egocentric images designed to
mimic captures from wearable devices. We carefully constructed the questions to
reflect real-world scenarios and challenges, including five types of
image-quality issues, six question types, varying entity popularity, differing
information dynamism, and different conversation turns. We design three tasks:
single-source augmentation, multi-source augmentation, and multi-turn
conversations -- each paired with an associated retrieval corpus and APIs for
both image-KG retrieval and webpage retrieval. Our evaluation shows that
straightforward RAG approaches achieve only 32% and 43% truthfulness on CRAG-MM
single- and multi-turn QA, respectively, whereas state-of-the-art industry
solutions have similar quality (32%/45%), underscoring ample room for
improvement. The benchmark has hosted KDD Cup 2025, attracting about 1K
participants and 5K submissions, with winning solutions improving baseline
performance by 28%, highlighting its early impact on advancing the field.