MMR-Bench: A Comprehensive Benchmark for Multimodal LLM Routing

• URL: http://arxiv.org/abs/2601.17814v1
• Date: Sun, 25 Jan 2026 12:44:14 GMT
• Title: MMR-Bench: A Comprehensive Benchmark for Multimodal LLM Routing
• Authors: Haoxuan Ma, Guannan Lai, Han-Jia Ye,
• Abstract summary: In practical deployments, workloads span lightweight OCR to complex multimodal reasoning.<n>routing is nontrivial due to modality fusion, wide variation in computational cost across models, and the absence of a standardized, budget-aware evaluation.<n>We present MMR-Bench, a unified benchmark that isolates the multimodal routing problem and enables comparison under fixed candidate sets and cost models.
• Score: 41.77627136743721
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal large language models (MLLMs) have advanced rapidly, yet heterogeneity in architecture, alignment strategies, and efficiency means that no single model is uniformly superior across tasks. In practical deployments, workloads span lightweight OCR to complex multimodal reasoning; using one MLLM for all queries either over-provisions compute on easy instances or sacrifices accuracy on hard ones. Query-level model selection (routing) addresses this tension, but extending routing from text-only LLMs to MLLMs is nontrivial due to modality fusion, wide variation in computational cost across models, and the absence of a standardized, budget-aware evaluation. We present MMR-Bench, a unified benchmark that isolates the multimodal routing problem and enables comparison under fixed candidate sets and cost models. MMR-Bench provides (i) a controlled environment with modality-aware inputs and variable compute budgets, (ii) a broad suite of vision-language tasks covering OCR, general VQA, and multimodal math reasoning, and (iii) strong single-model reference, oracle upper bounds, and representative routing policies. Using MMR-Bench, we show that incorporating multimodal signals improves routing quality. Empirically, these cues improve the cost-accuracy frontier and enable the routed system to exceed the strongest single model's accuracy at roughly 33% of its cost. Furthermore, policies trained on a subset of models and tasks generalize zero-shot to new datasets and text-only benchmarks without retuning, establishing MMR-Bench as a foundation for studying adaptive multimodal model selection and efficient MLLM deployment. The code will be available at: https://github.com/Hunter-Wrynn/MMR-Bench.

Related papers

• Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition [51.68340973140949]
  Multimodal Named Entity Recognition (GMNER) aims to extract text-based entities, assign them semantic categories, and ground them to corresponding visual regions.<n> MLLMs exhibit $textbfmodality bias$, including visual bias and textual bias, which stems from their tendency to take unimodal shortcuts.<n>We propose Modality-aware Consistency Reasoning ($bfMCR$), which enforces structured cross-modal reasoning.
  arXiv  Detail & Related papers  (2026-02-04T12:12:49Z)
• Tiny-R1V: Lightweight Multimodal Unified Reasoning Model via Model Merging [34.0419616643477]
  Tiny-R1V is a novel lightweight 3B model that achieves faster inference and higher accuracy via a two-stage optimization.<n>In the first stage, Tiny-R1V introduces Length-Informed Relative Policy Optimization (LIPO), a novel reinforcement learning method.<n>In the second stage, we propose Adaptive Model Merging (AMM), a training-free model merging method.
  arXiv  Detail & Related papers  (2025-10-10T04:14:57Z)
• Think Then Embed: Generative Context Improves Multimodal Embedding [51.76690812535934]
  We propose a Think-Then-Embed (TTE) framework for Universal Multimodal Embeddings (UME), composed of a reasoner and an embedder.<n>By leveraging a powerful MLLM reasoner, we achieve state-of-the-art performance on the MMEB-V2 benchmark, surpassing proprietary models trained on massive in-house datasets.
  arXiv  Detail & Related papers  (2025-10-06T16:53:56Z)
• RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory [57.449129198822476]
  RCR is a role-aware context routing framework for multi-agent large language model (LLM) systems.<n>It dynamically selects semantically relevant memory subsets for each agent based on its role and task stage.<n>A lightweight scoring policy guides memory selection, and agent outputs are integrated into a shared memory store.
  arXiv  Detail & Related papers  (2025-08-06T21:59:34Z)
• Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement Learning [27.70756702796812]
  We present textbf generalization-R1, a reinforcement learning framework that formulates multi-LLM routing and aggregation as a sequential decision process.<n>To facilitate learning, we employ a lightweight rule-based reward comprising format rewards, final outcome rewards, and a novel cost reward for optimizing the balance between performance and cost.
  arXiv  Detail & Related papers  (2025-06-10T17:56:45Z)
• Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques [14.892995952768352]
  Language Models (LMs) have excelled at tasks like text generation, summarization, and question answering.<n>Their inference remains computationally expensive and energy intensive in settings with limited hardware, power, or bandwidth.<n>Recent approaches have introduced multi LLM intelligent model selection strategies that dynamically allocate computational resources based on query complexity.
  arXiv  Detail & Related papers  (2025-06-06T23:13:08Z)
• OptMerge: Unifying Multimodal LLM Capabilities and Modalities via Model Merging [124.91183814854126]
  Model merging seeks to combine multiple expert models into a single model.<n>We introduce a benchmark for model merging research that clearly divides the tasks for MLLM training and evaluation.<n>We find that model merging offers a promising way for building improved MLLMs without requiring training data.
  arXiv  Detail & Related papers  (2025-05-26T12:23:14Z)
• Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts [56.7225771305861]
  This paper introduces Multi-Modal Retrieval-Augmented Generation (M$2$RAG), a benchmark designed to evaluate the effectiveness of Multi-modal Large Language Models.<n>The benchmark comprises four tasks: image captioning, multi-modal question answering, multi-modal fact verification, and image reranking.<n>To enhance the context utilization capabilities of MLLMs, we also introduce Multi-Modal Retrieval-Augmented Instruction Tuning (MM-RAIT)
  arXiv  Detail & Related papers  (2025-02-24T16:25:25Z)
• LLM Bandit: Cost-Efficient LLM Generation via Preference-Conditioned Dynamic Routing [3.090041654375235]
  We present a novel framework that formulates the LLM selection process as a multi-armed bandit problem.<n>Our approach incorporates a preference-conditioned dynamic routing mechanism, allowing users to specify their preferences at inference time.<n>Our method achieves significant improvements in both accuracy and cost-effectiveness across various LLM platforms.
  arXiv  Detail & Related papers  (2025-02-04T22:09:43Z)
• Towards Robust Multi-Modal Reasoning via Model Selection [7.6621866737827045]
  LLM serves as the "brain" of the agent, orchestrating multiple tools for collaborative multi-step task solving. We propose the $textitM3$ framework as a plug-in with negligible runtime overhead at test-time. Our experiments reveal that our framework enables dynamic model selection, considering both user inputs and subtask dependencies.
  arXiv  Detail & Related papers  (2023-10-12T16:06:18Z)

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.