WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs

Jack Hong¹, Shilin Yan^1†, Jiayin Cai¹, Xiaolong Jiang¹, Yao Hu¹, Weidi Xie^2‡

^†Project Leader ^‡Corresponding Author

¹Xiaohongshu Inc. ²Shanghai Jiao Tong University

[🏠 Project Page] [📖 arXiv Paper] [🤗 Dataset] [🏆 Leaderboard]

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🔥 News

• 2025.02.07 🌟 We release WorldSense, the first benchmark for real-world omnimodal understanding of MLLMs.

👀 WorldSense Overview

we introduce WorldSense, the first benchmark to assess the multi-modal video understanding, that simultaneously encompasses visual, audio, and text inputs. In contrast to existing benchmarks, our WorldSense has several features:

• Collaboration of omni-modality. We design the evaluation tasks to feature a strong coupling of audio and video, requiring models to effectively utilize the synergistic perception of omni-modality;
• Diversity of videos and tasks. WorldSense encompasses a diverse collection of 1,662 audio-visual synchronised videos, systematically categorized into 8 primary domains and 67 fine-grained subcategories to cover the broad scenarios, and 3,172 multi-choice QA pairs across 26 distinct tasks to enable the comprehensive evaluation;
• High-quality annotations. All the QA pairs are manually labeled by 80 expert annotators with multiple rounds of correction to ensure quality.

Based on our WorldSense, we extensively evaluate various state-of-the-art models. The experimental results indicate that existing models face significant challenges in understanding real-world scenarios (48% best accuracy). We hope our WorldSense can provide a platform for evaluating the ability in constructing and understanding coherent contexts from omni-modality.

📐 Dataset Examples

🔍 Dataset

Please download our WorldSense from here.

🔮 Evaluation Pipeline

📍 Evaluation: Thanks for the reproduction of our evaluation through VLMEvalkit. Please refer to VLMEvalkit for details.

📍 Leaderboard:

If you want to add your model to our leaderboard, please contact jaaackhong@gmail.com.

📈 Experimental Results

• Evaluation results of sota MLLMs.

• Fine-grained results on task category.

• Fine-grained results on audio type.

• In-depth analysis for real-world omnimodal understanding.

Impact of vision information.

Impact of audio information.

Impact of audio information for Video MLLMs.

Impact of video frames.

📖 Citation

If you find WorldSense helpful for your research, please consider citing our work. Thanks!

@article{hong2025worldsenseevaluatingrealworldomnimodal,
    title={WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs},
    author={Jack Hong and Shilin Yan and Jiayin Cai and Xiaolong Jiang and Yao Hu and Weidi Xie},
    year={2025},
    eprint={2502.04326},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    url={https://arxiv.org/abs/2502.04326}, 
}