MRMR: A Realistic and Expert-Level Multidisciplinary Benchmark for Reasoning-Intensive Multimodal Retrieval
Abstract
MRMR is a benchmark for expert-level multidisciplinary multimodal retrieval that includes reasoning-intensive tasks, contradiction retrieval, and image-text interleaved sequences, highlighting the need for improved multimodal models.
We introduce MRMR, the first expert-level multidisciplinary multimodal retrieval benchmark requiring intensive reasoning. MRMR contains 1,502 queries spanning 23 domains, with positive documents carefully verified by human experts. Compared to prior benchmarks, MRMR introduces three key advancements. First, it challenges retrieval systems across diverse areas of expertise, enabling fine-grained model comparison across domains. Second, queries are reasoning-intensive, with images requiring deeper interpretation such as diagnosing microscopic slides. We further introduce Contradiction Retrieval, a novel task requiring models to identify conflicting concepts. Finally, queries and documents are constructed as image-text interleaved sequences. Unlike earlier benchmarks restricted to single images or unimodal documents, MRMR offers a realistic setting with multi-image queries and mixed-modality corpus documents. We conduct an extensive evaluation of 4 categories of multimodal retrieval systems and 14 frontier models on MRMR. The text embedding model Qwen3-Embedding with LLM-generated image captions achieves the highest performance, highlighting substantial room for improving multimodal retrieval models. Although latest multimodal models such as Ops-MM-Embedding perform competitively on expert-domain queries, they fall short on reasoning-intensive tasks. We believe that MRMR paves the way for advancing multimodal retrieval in more realistic and challenging scenarios.
Community
Release paper.
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- MR$^2$-Bench: Going Beyond Matching to Reasoning in Multimodal Retrieval (2025)
- UNIDOC-BENCH: A Unified Benchmark for Document-Centric Multimodal RAG (2025)
- Multimodal Iterative RAG for Knowledge-Intensive Visual Question Answering (2025)
- CMRAG: Co-modality-based visual document retrieval and question answering (2025)
- M3Retrieve: Benchmarking Multimodal Retrieval for Medicine (2025)
- Zero-shot Multimodal Document Retrieval via Cross-modal Question Generation (2025)
- MoLoRAG: Bootstrapping Document Understanding via Multi-modal Logic-aware Retrieval (2025)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment:
@librarian-bot
recommend
Models citing this paper 0
No model linking this paper
Datasets citing this paper 0
No dataset linking this paper
Spaces citing this paper 0
No Space linking this paper