# R-Bench: Are your Large Multimodal Model Robust to Real-world Corruptions?

arXiv: https://arxiv.org/abs/2410.05474 · HTML: https://mlsanity.com/paper/2410.05474/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 45 of its 59 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 30 of 45)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities](https://mlsanity.com/paper/2308.02490/) (2023)
- [OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge](https://mlsanity.com/paper/1906.00067/) (2019)
- [MiniCPM-V: A GPT-4V Level MLLM on Your Phone](https://mlsanity.com/paper/2408.01800/) (2024)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [ShareGPT4V: Improving Large Multi-Modal Models with Better Captions](https://mlsanity.com/paper/2311.12793/) (2023)
- [SPICE: Semantic Propositional Image Caption Evaluation](https://mlsanity.com/paper/1607.08822/) (2016)
- [DeepSeek-VL: Towards Real-World Vision-Language Understanding](https://mlsanity.com/paper/2403.05525/) (2024)
- [RobustBench: a standardized adversarial robustness benchmark](https://mlsanity.com/paper/2010.09670/) (2020)
- [LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models](https://mlsanity.com/paper/2407.07895/) (2024)
- [mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration](https://mlsanity.com/paper/2311.04257/) (2023)
- [Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels](https://mlsanity.com/paper/2312.17090/) (2023)
- [Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Models](https://mlsanity.com/paper/2311.06607/) (2023)
- [InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model](https://mlsanity.com/paper/2401.16420/) (2024)
- [mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models](https://mlsanity.com/paper/2408.04840/) (2024)
- [On Evaluating Adversarial Robustness of Large Vision-Language Models](https://mlsanity.com/paper/2305.16934/) (2023)
- [Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective](https://mlsanity.com/paper/2303.14968/) (2023)

## Built on by (most cited first, 30 of 30)

- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://mlsanity.com/paper/2504.10479/) (2025)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models](https://mlsanity.com/paper/2605.06388/) (2026)
- [Redundancy Principles for MLLMs Benchmarks](https://mlsanity.com/paper/2501.13953/) (2025)
- [Information Density Principle for MLLM Benchmarks](https://mlsanity.com/paper/2503.10079/) (2025)
- [Res-Bench: Benchmarking the Robustness of Multimodal Large Language Models to Dynamic Resolution Input](https://mlsanity.com/paper/2510.16926/) (2025)
- [What Makes Video World Model Latents Action-Relevant: Prediction over Reconstruction](https://mlsanity.com/paper/2606.07687/) (2026)
- [StructVRM: Aligning Multimodal Reasoning with Structured and Verifiable Reward Models](https://mlsanity.com/paper/2508.05383/) (2025)
- [OmniBridge: Unified Multimodal Understanding, Generation, and Retrieval via Latent Space Alignment](https://mlsanity.com/paper/2509.19018/) (2025)
- [Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models](https://mlsanity.com/paper/2510.21978/) (2025)
- [Using GUI Agent for Electronic Design Automation](https://mlsanity.com/paper/2512.11611/) (2025)
- [AndesVL Technical Report: An Efficient Mobile-side Multimodal Large Language Model](https://mlsanity.com/paper/2510.11496/) (2025)
- [Diagnosing Corruption-Induced Reliability Failures in Vision-Language Models](https://mlsanity.com/paper/2511.19032/) (2025)
- [RoboStressBench: Benchmarking VLM Robustness to Physical Visual Stress in Embodied Scenes](https://mlsanity.com/paper/2606.00828/) (2026)
- [Embodied Image Quality Assessment for Robotic Intelligence](https://mlsanity.com/paper/2412.18774/) (2024)
- [Robust Driving QA through Metadata-Grounded Context and Task-Specific Prompts](https://mlsanity.com/paper/2510.19001/) (2025)
- [jina-vlm: Small Multilingual Vision Language Model](https://mlsanity.com/paper/2512.04032/) (2025)
- [CLEAR: Unlocking Generative Potential for Degraded Image Understanding in Unified Multimodal Models](https://mlsanity.com/paper/2604.04780/) (2026)
- [Reversing the Flow: Generation-to-Understanding Synergy in Large Multimodal Models](https://mlsanity.com/paper/2605.15792/) (2026)
- [Robust-U1: Can MLLMs Self-Recover Corrupted Visual Content for Robust Understanding?](https://mlsanity.com/paper/2606.08063/) (2026)
- [Confidence-Aware Tool Orchestration for Robust Video Understanding](https://mlsanity.com/paper/2606.26904/) (2026)
- [DEVAL: A Framework for Evaluating and Improving the Derivation Capability of Large Language Models](https://mlsanity.com/paper/2511.14813/) (2025)
- [MedQ-Deg: A Multidimensional Benchmark for Evaluating MLLMs Across Medical Image Quality Degradations](https://mlsanity.com/paper/2603.07769/) (2026)
- [Robust Test-time Video-Text Retrieval: Benchmarking and Adapting for Query Shifts](https://mlsanity.com/paper/2604.20851/) (2026)
- [Latent Denoising Improves Visual Alignment in Large Multimodal Models](https://mlsanity.com/paper/2604.21343/) (2026)
- [Learning from Fine-Grained Visual Discrepancies: Mitigating Multimodal Hallucinations via In-Context Visual Contrastive Optimization](https://mlsanity.com/paper/2605.31312/) (2026)
- [BRUCE: Benchmarking Robustness Under Corruption Escalation for Scientific Vision-Language Reasoning](https://mlsanity.com/paper/2608.07742/) (2026)
- [Reading Right, Answering Wrong: How Visual Configuration Changes Affect Evidence Use in VLMs](https://mlsanity.com/paper/2609.25770/) (2026)
- [On the Limits of Representation-Based Test-Time Adaptation for Multimodal LLMs](https://mlsanity.com/paper/2610.19892/) (2026)

## Abstract (arXiv)

The outstanding performance of Large Multimodal Models (LMMs) has made them widely applied in vision-related tasks. However, various corruptions in the real world mean that images will not be as ideal as in simulations, presenting significant challenges for the practical application of LMMs. To address this issue, we introduce R-Bench, a benchmark focused on the **Real-world Robustness of LMMs**. Specifically, we: (a) model the complete link from user capture to LMMs reception, comprising 33 corruption dimensions, including 7 steps according to the corruption sequence, and 7 groups based on low-level attributes; (b) collect reference/distorted image dataset before/after corruption, including 2,970 question-answer pairs with human labeling; (c) propose comprehensive evaluation for absolute/relative robustness and benchmark 20 mainstream LMMs. Results show that while LMMs can correctly handle the original reference images, their performance is not stable when faced with distorted images, and there is a significant gap in robustness compared to the human visual system. We hope that R-Bench will inspire improving the robustness of LMMs, **extending them from experimental simulations to the real-world application**. Check https://q-future.github.io/R-Bench for details.