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

arXiv: https://arxiv.org/abs/2601.09562 · HTML: https://mlsanity.com/paper/2601.09562/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2026.

## Built on by (most cited first, 11 of 11)

- [LeanSearch v2: Global Premise Retrieval for Lean 4 Theorem Proving](https://mlsanity.com/paper/2605.13137/) (2026)
- [Unlocking Multimodal Document Intelligence: From Current Triumphs to Future Frontiers of Visual Document Retrieval](https://mlsanity.com/paper/2602.19961/) (2026)
- [SMMBench: A Benchmark for Source-Distributed Multimodal Agent Memory](https://mlsanity.com/paper/2605.15710/) (2026)
- [Are LLM-Based Retrievers Worth Their Cost? An Empirical Study of Efficiency, Robustness, and Reasoning Overhead](https://mlsanity.com/paper/2604.03676/) (2026)
- [Argus-Retriever: Vision-LLM Late-Interaction Retrieval with Region-Aware Query-Conditioned MoE for Visual Document Retrieval](https://mlsanity.com/paper/2606.04300/) (2026)
- [Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems](https://mlsanity.com/paper/2605.04018/) (2026)
- [Douyin Multimodal Embedding Model Technical Report](https://mlsanity.com/paper/2608.02148/) (2026)
- [MARVEL: Multimodal Adaptive Reasoning-intensiVe Expand-rerank and retrievaL](https://mlsanity.com/paper/2604.07079/) (2026)
- [BRIDGE: Multimodal-to-Text Retrieval via Reinforcement-Learned Query Alignment](https://mlsanity.com/paper/2604.07201/) (2026)
- [HIVE: Query, Hypothesize, Verify An LLM Framework for Multimodal Reasoning-Intensive Retrieval](https://mlsanity.com/paper/2604.07220/) (2026)
- [BracketRank: Large Language Model Document Ranking via Reasoning-based Competitive Elimination](https://mlsanity.com/paper/2604.08834/) (2026)

## Abstract (arXiv)

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/.