MR$^2$-Bench: Going Beyond Matching to Reasoning in Multimodal Retrieval

• URL: http://arxiv.org/abs/2509.26378v1
• Date: Tue, 30 Sep 2025 15:09:14 GMT
• Title: MR$^2$-Bench: Going Beyond Matching to Reasoning in Multimodal Retrieval
• Abstract summary: Multimodal retrieval is becoming a crucial component of modern AI applications, yet its evaluation lags behind the demands of more realistic and challenging scenarios.<n>We introduce MR$2$-Bench, a reasoning-intensive benchmark for multimodal retrieval.
• Score: 86.35779264575154
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multimodal retrieval is becoming a crucial component of modern AI applications, yet its evaluation lags behind the demands of more realistic and challenging scenarios. Existing benchmarks primarily probe surface-level semantic correspondence (e.g., object-text matching) while failing to assess the deeper reasoning required to capture complex relationships between visual and textual information. To address this gap, we introduce MR$^2$-Bench, a reasoning-intensive benchmark for multimodal retrieval. MR$^2$-Bench presents the following critical values: 1) all tasks are reasoning-driven, going beyond shallow matching to effectively assess models' capacity for logical, spatial, and causal inference; 2) it features diverse multimodal data, such as natural images, diagrams, and visual puzzles, enabling comprehensive evaluation across content types; 3) it supports complex queries and documents containing multiple images and covers diverse retrieval scenarios, more accurately reflecting real-world applications. Our benchmark contains 1,309 curated queries, derived either from manual collection and annotation or from selective consolidation of public datasets. Despite achieving strong results on existing benchmarks, current state-of-the-art models still struggle on MR$^2$-Bench: for example, the leading Seed1.6-Embedding model attains a Recall@1 of 77.78 on MMEB, but only 9.91 on MR$^2$-Bench. This substantial performance gap highlights both the increased challenge posed by our benchmark and the pressing need for further advances in reasoning-intensive multimodal retrieval. The dataset and evaluation code will be made publicly available at https://github.com/VectorSpaceLab/MR2-Bench.

Related papers

• FaLCon: Facet-Anchored Retrieval with Late Consensus for Sim2Real Text-Based Person Anomaly Search [0.8609344172589112]
  Text-based person anomaly search requires retrieving real-world pedestrian images from detailed natural-language descriptions.<n>This Sim2Real setting is particularly challenging because visually similar candidates may differ only in subtle actions, object interactions, or appearance attributes.<n>We propose an anchor-constrained coarse-to-fine retrieval framework that combines global semantic matching with fine-grained verification.
  arXiv  Detail & Related papers  (2026-08-10T11:42:52Z)
• Do Composed Image Retrieval Benchmarks Require Multimodal Composition? [86.99911649534795]
  We show that strong performance on Composed Image Retrieval benchmarks is assumed to require multimodal composition.<n>Across four widely used CIR benchmarks and eleven Generalist Multimodal Embedding models, a large fraction of queries can be solved using a single modality.<n>High CIR performance can arise from unimodal signals rather than true multimodal composition.
  arXiv  Detail & Related papers  (2026-05-14T12:56:36Z)
• Benchmarking Deflection and Hallucination in Large Vision-Language Models [25.176271096443482]
  Existing benchmarks overlook conflicts between visual and textual evidence.<n>We introduce VLM-DeflectionBench, a benchmark of 2,775 samples spanning diverse multimodal retrieval settings.<n>Our results highlight the need to evaluate not only what models know, but how they behave when they do not.
  arXiv  Detail & Related papers  (2026-04-13T20:22:22Z)
• OmniRet: Efficient and High-Fidelity Omni Modality Retrieval [51.80205678389465]
  We present OmniRet, the first retrieval model capable of handling complex, composed queries spanning three key modalities: text, vision, and audio.<n>Our model demonstrates significant improvements on composed query, audio and video retrieval tasks, while achieving on-par performance with state-of-the-art models on others.
  arXiv  Detail & Related papers  (2026-03-02T17:19:55Z)
• Beyond Global Similarity: Towards Fine-Grained, Multi-Condition Multimodal Retrieval [27.493644447594367]
  MCMR (Multi-Conditional Multimodal Retrieval) is a large-scale benchmark designed to evaluate fine-grained, multi-condition cross-modal retrieval under natural-language queries.<n>It spans five product domains: upper and bottom clothing, jewelry, shoes, and furniture.<n>We benchmark a diverse suite of MLLM-based multimodal retrievers and vision-language rerankers to assess their condition-aware reasoning abilities.
  arXiv  Detail & Related papers  (2026-03-01T12:53:47Z)
• MARA: A Multimodal Adaptive Retrieval-Augmented Framework for Document Question Answering [51.19392014547221]
  Retrieval-based multimodal document QA aims to identify and integrate relevant information from visually rich documents with complex multimodal structures.<n>Current approaches rely on query-agnostic document representations that overlook salient content.<n>We propose the Multimodal Adaptive Retrieval-Augmented (MARA) framework, which introduces query-adaptive generation.
  arXiv  Detail & Related papers  (2026-02-01T12:27:40Z)
• Guided Query Refinement: Multimodal Hybrid Retrieval with Test-Time Optimization [10.476757608225475]
  Multimodal encoders have pushed the boundaries of visual document retrieval.<n>Recent models relying on this paradigm have massively scaled the sizes of their query and document representations.<n>We investigate whether a lightweight dense text retriever can enhance a stronger vision-centric model.
  arXiv  Detail & Related papers  (2025-10-06T17:12:53Z)
• UNIDOC-BENCH: A Unified Benchmark for Document-Centric Multimodal RAG [82.84014669683863]
  Multimodal retrieval-augmented generation (MM-RAG) is a key approach for applying large language models to real-world knowledge bases.<n>UniDoc-Bench is the first large-scale, realistic benchmark for MM-RAG built from 70k real-world PDF pages.<n>Our experiments show that multimodal text-image fusion RAG systems consistently outperform both unimodal and jointly multimodal embedding-based retrieval.
  arXiv  Detail & Related papers  (2025-10-04T04:30:13Z)
• MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents [78.3863007028688]
  MM-BrowseComp is a novel benchmark comprising 224 challenging, hand-crafted questions.<n>These questions often incorporate images in prompts, and crucial information encountered during the search and reasoning process may also be embedded within images or videos on webpages.<n>Our comprehensive evaluation of state-of-the-art models on MM-BrowseComp reveals that even top models like OpenAI o3 with tools achieve only 29.02% accuracy.
  arXiv  Detail & Related papers  (2025-08-14T13:46:47Z)
• OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Understanding [16.1559012200066]
  We introduce OST-Bench, a benchmark designed to evaluate Online Spatio-Temporal understanding from the perspective of an agent.<n>Built on an efficient data collection pipeline, OST-Bench consists of 1.4k scenes and 10k question-answer pairs collected from ScanNet, Matterport3D, and ARKitScenes.<n>We find that both complex-based spatial reasoning demands and long-term memory retrieval requirements significantly drop model performance along two separate axes.
  arXiv  Detail & Related papers  (2025-07-10T17:56:07Z)
• MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation [56.87891213797931]
  We present MTR-Bench for Large Language Models' Multi-Turn Reasoning evaluation.<n>Comprising 4 classes, 40 tasks, and 3600 instances, MTR-Bench covers diverse reasoning capabilities.<n>MTR-Bench features fully-automated framework spanning both dataset constructions and model evaluations.
  arXiv  Detail & Related papers  (2025-05-21T17:59:12Z)
• MultiConIR: Towards multi-condition Information Retrieval [38.864056667809095]
  MultiConIR is a benchmark designed to evaluate retrieval and reranking models under nuanced multi-condition query scenarios.<n>Most retrievers and rerankers exhibit severe performance degradation as query complexity increases.<n>This work delves into the factors contributing to reranker performance deterioration and examines how condition positioning within queries affects similarity assessment.
  arXiv  Detail & Related papers  (2025-03-11T05:02:03Z)
• REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark [16.55516587540082]
  We introduce REAL-MM-RAG, an automatically generated benchmark designed to address four key properties essential for real-world retrieval.<n>We propose a multi-difficulty-level scheme based on query rephrasing to evaluate models' semantic understanding beyond keyword matching.<n>Our benchmark reveals significant model weaknesses, particularly in handling table-heavy documents and robustness to query rephrasing.
  arXiv  Detail & Related papers  (2025-02-17T22:10:47Z)
• CART: A Generative Cross-Modal Retrieval Framework with Coarse-To-Fine Semantic Modeling [53.97609687516371]
  Cross-modal retrieval aims to search for instances, which are semantically related to the query through the interaction of different modal data.<n>Traditional solutions utilize a single-tower or dual-tower framework to explicitly compute the score between queries and candidates.<n>We propose a generative cross-modal retrieval framework (CART) based on coarse-to-fine semantic modeling.
  arXiv  Detail & Related papers  (2024-06-25T12:47:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.